Dynamic Causal Models and Autopoietic Systems
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OLIVIER DAVID
2007-01-01
Full Text Available Dynamic Causal Modelling (DCM and the theory of autopoietic systems are two important conceptual frameworks. In this review, we suggest that they can be combined to answer important questions about self-organising systems like the brain. DCM has been developed recently by the neuroimaging community to explain, using biophysical models, the non-invasive brain imaging data are caused by neural processes. It allows one to ask mechanistic questions about the implementation of cerebral processes. In DCM the parameters of biophysical models are estimated from measured data and the evidence for each model is evaluated. This enables one to test different functional hypotheses (i.e., models for a given data set. Autopoiesis and related formal theories of biological systems as autonomous machines represent a body of concepts with many successful applications. However, autopoiesis has remained largely theoretical and has not penetrated the empiricism of cognitive neuroscience. In this review, we try to show the connections that exist between DCM and autopoiesis. In particular, we propose a simple modification to standard formulations of DCM that includes autonomous processes. The idea is to exploit the machinery of the system identification of DCMs in neuroimaging to test the face validity of the autopoietic theory applied to neural subsystems. We illustrate the theoretical concepts and their implications for interpreting electroencephalographic signals acquired during amygdala stimulation in an epileptic patient. The results suggest that DCM represents a relevant biophysical approach to brain functional organisation, with a potential that is yet to be fully evaluated
Intelligent diagnosis of jaundice with dynamic uncertain causality graph model*
Hao, Shao-rui; Geng, Shi-chao; Fan, Lin-xiao; Chen, Jia-jia; Zhang, Qin; Li, Lan-juan
2017-01-01
Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A “chaining” inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure. PMID:28471111
Intelligent diagnosis of jaundice with dynamic uncertain causality graph model.
Hao, Shao-Rui; Geng, Shi-Chao; Fan, Lin-Xiao; Chen, Jia-Jia; Zhang, Qin; Li, Lan-Juan
2017-05-01
Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A "chaining" inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure.
Dynamic Causal Modeling of the Cortical Responses to Wrist Perturbations
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Yuan Yang
2017-09-01
Full Text Available Mechanical perturbations applied to the wrist joint typically evoke a stereotypical sequence of cortical and muscle responses. The early cortical responses (<100 ms are thought be involved in the “rapid” transcortical reaction to the perturbation while the late cortical responses (>100 ms are related to the “slow” transcortical reaction. Although previous studies indicated that both responses involve the primary motor cortex, it remains unclear if both responses are engaged by the same effective connectivity in the cortical network. To answer this question, we investigated the effective connectivity cortical network after a “ramp-and-hold” mechanical perturbation, in both the early (<100 ms and late (>100 ms periods, using dynamic causal modeling. Ramp-and-hold perturbations were applied to the wrist joint while the subject maintained an isometric wrist flexion. Cortical activity was recorded using a 128-channel electroencephalogram (EEG. We investigated how the perturbation modulated the effective connectivity for the early and late periods. Bayesian model comparisons suggested that different effective connectivity networks are engaged in these two periods. For the early period, we found that only a few cortico-cortical connections were modulated, while more complicated connectivity was identified in the cortical network during the late period with multiple modulated cortico-cortical connections. The limited early cortical network likely allows for a rapid muscle response without involving high-level cognitive processes, while the complexity of the late network may facilitate coordinated responses.
Development of face recognition: Dynamic causal modelling of MEG data.
He, Wei; Johnson, Blake W
2018-04-01
Electrophysiological studies of adults indicate that brain activity is enhanced during viewing of repeated faces, at a latency of about 250 ms after the onset of the face (M250/N250). The present study aimed to determine if this effect was also present in preschool-aged children, whose brain activity was measured in a custom-sized pediatric MEG system. The results showed that, unlike adults, face repetition did not show any significant modulation of M250 amplitude in children; however children's M250 latencies were significantly faster for repeated than non-repeated faces. Dynamic causal modelling (DCM) of the M250 in both age groups tested the effects of face repetition within the core face network including the occipital face area (OFA), the fusiform face area (FFA), and the superior temporal sulcus (STS). DCM revealed that repetition of identical faces altered both forward and backward connections in children and adults; however the modulations involved inputs to both FFA and OFA in adults but only to OFA in children. These findings suggest that the amplitude-insensitivity of the immature M250 may be due to a weaker connection between the FFA and lower visual areas. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.
Development of face recognition: Dynamic causal modelling of MEG data
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Wei He
2018-04-01
Full Text Available Electrophysiological studies of adults indicate that brain activity is enhanced during viewing of repeated faces, at a latency of about 250 ms after the onset of the face (M250/N250. The present study aimed to determine if this effect was also present in preschool-aged children, whose brain activity was measured in a custom-sized pediatric MEG system. The results showed that, unlike adults, face repetition did not show any significant modulation of M250 amplitude in children; however children’s M250 latencies were significantly faster for repeated than non-repeated faces. Dynamic causal modelling (DCM of the M250 in both age groups tested the effects of face repetition within the core face network including the occipital face area (OFA, the fusiform face area (FFA, and the superior temporal sulcus (STS. DCM revealed that repetition of identical faces altered both forward and backward connections in children and adults; however the modulations involved inputs to both FFA and OFA in adults but only to OFA in children. These findings suggest that the amplitude-insensitivity of the immature M250 may be due to a weaker connection between the FFA and lower visual areas. Keywords: MEG, Face recognition, Repetition, DCM, M250, M170
Exploring manual asymmetries during grasping: a dynamic causal modeling approach.
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Chiara eBegliomini
2015-02-01
Full Text Available Recording of neural activity during grasping actions in macaques showed that grasp-related sensorimotor transformations are accomplished in a circuit constituted by the anterior part of the intraparietal sulcus (AIP, the ventral (F5 and the dorsal (F2 region of the premotor area. In humans, neuroimaging studies have revealed the existence of a similar circuit, involving the putative homolog of macaque areas AIP, F5 and F2. These studies have mainly considered grasping movements performed with the right dominant hand and only a few studies have measured brain activity associated with a movement performed with the left non-dominant hand. As a consequence of this gap, how the brain controls for grasping movement performed with the dominant and the non-dominant hand still represents an open question. A functional resonance imaging experiment (fMRI has been conducted, and effective connectivity (Dynamic Causal Modelling, DCM was used to assess how connectivity among grasping-related areas is modulated by hand (i.e., left and right during the execution of grasping movements towards a small object requiring precision grasping. Results underlined boosted inter-hemispheric couplings between dorsal premotor cortices during the execution of movements performed with the left rather than the right dominant hand. More specifically, they suggest that the dorsal premotor cortices may play a fundamental role in monitoring the configuration of fingers when grasping movements are performed by either the right and the left hand. This role becomes particularly evident when the hand less-skilled (i.e., the left hand to perform such action is utilized. The results are discussed in light of recent theories put forward to explain how parieto-frontal connectivity is modulated by the execution of prehensile movements.
Dynamics and causality constraints
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Sousa, Manoelito M. de
2001-04-01
The physical meaning and the geometrical interpretation of causality implementation in classical field theories are discussed. Causality in field theory are kinematical constraints dynamically implemented via solutions of the field equation, but in a limit of zero-distance from the field sources part of these constraints carries a dynamical content that explains old problems of classical electrodynamics away with deep implications to the nature of physicals interactions. (author)
Molenaar, P.C.M.
1987-01-01
Outlines a frequency domain analysis of the dynamic factor model and proposes a solution to the problem of constructing a causal filter of lagged factor loadings. The method is illustrated with applications to simulated and real multivariate time series. The latter applications involve topographic
Causally nonseparable processes admitting a causal model
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Feix, Adrien; Araújo, Mateus; Brukner, Caslav
2016-01-01
A recent framework of quantum theory with no global causal order predicts the existence of ‘causally nonseparable’ processes. Some of these processes produce correlations incompatible with any causal order (they violate so-called ‘causal inequalities’ analogous to Bell inequalities ) while others do not (they admit a ‘causal model’ analogous to a local model ). Here we show for the first time that bipartite causally nonseparable processes with a causal model exist, and give evidence that they have no clear physical interpretation. We also provide an algorithm to generate processes of this kind and show that they have nonzero measure in the set of all processes. We demonstrate the existence of processes which stop violating causal inequalities but are still causally nonseparable when mixed with a certain amount of ‘white noise’. This is reminiscent of the behavior of Werner states in the context of entanglement and nonlocality. Finally, we provide numerical evidence for the existence of causally nonseparable processes which have a causal model even when extended with an entangled state shared among the parties. (paper)
Neural pathways in processing of sexual arousal: a dynamic causal modeling study.
Seok, J-W; Park, M-S; Sohn, J-H
2016-09-01
Three decades of research have investigated brain processing of visual sexual stimuli with neuroimaging methods. These researchers have found that sexual arousal stimuli elicit activity in a broad neural network of cortical and subcortical brain areas that are known to be associated with cognitive, emotional, motivational and physiological components. However, it is not completely understood how these neural systems integrate and modulated incoming information. Therefore, we identify cerebral areas whose activations were correlated with sexual arousal using event-related functional magnetic resonance imaging and used the dynamic causal modeling method for searching the effective connectivity about the sexual arousal processing network. Thirteen heterosexual males were scanned while they passively viewed alternating short trials of erotic and neutral pictures on a monitor. We created a subset of seven models based on our results and previous studies and selected a dominant connectivity model. Consequently, we suggest a dynamic causal model of the brain processes mediating the cognitive, emotional, motivational and physiological factors of human male sexual arousal. These findings are significant implications for the neuropsychology of male sexuality.
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Kaustubh Supekar
2012-02-01
Full Text Available Cognitive skills undergo protracted developmental changes resulting in proficiencies that are a hallmark of human cognition. One skill that develops over time is the ability to problem solve, which in turn relies on cognitive control and attention abilities. Here we use a novel multimodal neurocognitive network-based approach combining task-related fMRI, resting-state fMRI and diffusion tensor imaging (DTI to investigate the maturation of control processes underlying problem solving skills in 7-9 year-old children. Our analysis focused on two key neurocognitive networks implicated in a wide range of cognitive tasks including control: the insula-cingulate salience network, anchored in anterior insula (AI, ventrolateral prefrontal cortex and anterior cingulate cortex, and the fronto-parietal central executive network, anchored in dorsolateral prefrontal cortex and posterior parietal cortex (PPC. We found that, by age 9, the AI node of the salience network is a major causal hub initiating control signals during problem solving. Critically, despite stronger AI activation, the strength of causal regulatory influences from AI to the PPC node of the central executive network was significantly weaker and contributed to lower levels of behavioral performance in children compared to adults. These results were validated using two different analytic methods for estimating causal interactions in fMRI data. In parallel, DTI-based tractography revealed weaker AI-PPC structural connectivity in children. Our findings point to a crucial role of AI connectivity, and its causal cross-network influences, in the maturation of dynamic top-down control signals underlying cognitive development. Overall, our study demonstrates how a unified neurocognitive network model when combined with multimodal imaging enhances our ability to generalize beyond individual task-activated foci and provides a common framework for elucidating key features of brain and cognitive
Putting a cap on causality violations in causal dynamical triangulations
International Nuclear Information System (INIS)
Ambjoern, Jan; Loll, Renate; Westra, Willem; Zohren, Stefan
2007-01-01
The formalism of causal dynamical triangulations (CDT) provides us with a non-perturbatively defined model of quantum gravity, where the sum over histories includes only causal space-time histories. Path integrals of CDT and their continuum limits have been studied in two, three and four dimensions. Here we investigate a generalization of the two-dimensional CDT model, where the causality constraint is partially lifted by introducing branching points with a weight g s , and demonstrate that the system can be solved analytically in the genus-zero sector. The solution is analytic in a neighborhood around weight g s = 0 and cannot be analytically continued to g s = ∞, where the branching is entirely geometric and where one would formally recover standard Euclidean two-dimensional quantum gravity defined via dynamical triangulations or Liouville theory
Dynamics of Quantum Causal Structures
Castro-Ruiz, Esteban; Giacomini, Flaminia; Brukner, Časlav
2018-01-01
It was recently suggested that causal structures are both dynamical, because of general relativity, and indefinite, because of quantum theory. The process matrix formalism furnishes a framework for quantum mechanics on indefinite causal structures, where the order between operations of local laboratories is not definite (e.g., one cannot say whether operation in laboratory A occurs before or after operation in laboratory B ). Here, we develop a framework for "dynamics of causal structures," i.e., for transformations of process matrices into process matrices. We show that, under continuous and reversible transformations, the causal order between operations is always preserved. However, the causal order between a subset of operations can be changed under continuous yet nonreversible transformations. An explicit example is that of the quantum switch, where a party in the past affects the causal order of operations of future parties, leading to a transition from a channel from A to B , via superposition of causal orders, to a channel from B to A . We generalize our framework to construct a hierarchy of quantum maps based on transformations of process matrices and transformations thereof.
Dynamics of Quantum Causal Structures
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Esteban Castro-Ruiz
2018-03-01
Full Text Available It was recently suggested that causal structures are both dynamical, because of general relativity, and indefinite, because of quantum theory. The process matrix formalism furnishes a framework for quantum mechanics on indefinite causal structures, where the order between operations of local laboratories is not definite (e.g., one cannot say whether operation in laboratory A occurs before or after operation in laboratory B. Here, we develop a framework for “dynamics of causal structures,” i.e., for transformations of process matrices into process matrices. We show that, under continuous and reversible transformations, the causal order between operations is always preserved. However, the causal order between a subset of operations can be changed under continuous yet nonreversible transformations. An explicit example is that of the quantum switch, where a party in the past affects the causal order of operations of future parties, leading to a transition from a channel from A to B, via superposition of causal orders, to a channel from B to A. We generalize our framework to construct a hierarchy of quantum maps based on transformations of process matrices and transformations thereof.
Identifying abnormal connectivity in patients using Dynamic Causal Modelling of fMRI responses.
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Mohamed L Seghier
2010-08-01
Full Text Available Functional imaging studies of brain damaged patients offer a unique opportunity to understand how sensori-motor and cognitive tasks can be carried out when parts of the neural system that support normal performance are no longer available. In addition to knowing which regions a patient activates, we also need to know how these regions interact with one another, and how these inter-regional interactions deviate from normal. Dynamic Causal Modelling (DCM offers the opportunity to assess task-dependent interactions within a set of regions. Here we review its use in patients when the question of interest concerns the characterisation of abnormal connectivity for a given pathology. We describe the currently available implementations of DCM for fMRI responses, varying from the deterministic bilinear models with one-state equation to the stochastic nonlinear models with two-state equations. We also highlight the importance of the new Bayesian model selection and averaging tools that allow different plausible models to be compared at the single subject and group level. These procedures allow inferences to be made at different levels of model selection, from features (model families to connectivity parameters. Following a critical review of previous DCM studies that investigated abnormal connectivity we propose a systematic procedure that will ensure more flexibility and efficiency when using DCM in patients. Finally, some practical and methodological issues crucial for interpreting or generalising DCM findings in patients are discussed.
Dynamic causal modeling of touch-evoked potentials in the rubber hand illusion.
Zeller, Daniel; Friston, Karl J; Classen, Joseph
2016-09-01
The neural substrate of bodily ownership can be disclosed by the rubber hand illusion (RHI); namely, the illusory self-attribution of an artificial hand that is induced by synchronous tactile stimulation of the subject's hand that is hidden from view. Previous studies have pointed to the premotor cortex (PMC) as a pivotal area in such illusions. To investigate the effective connectivity between - and within - sensory and premotor areas involved in bodily perceptions, we used dynamic causal modeling of touch-evoked responses in 13 healthy subjects. Each subject's right hand was stroked while viewing their own hand ("REAL"), or an artificial hand presented in an anatomically plausible ("CONGRUENT") or implausible ("INCONGRUENT") position. Bayesian model comparison revealed strong evidence for a differential involvement of the PMC in the generation of touch-evoked responses under the three conditions, confirming a crucial role of PMC in bodily self-attribution. In brief, the extrinsic (forward) connection from left occipital cortex to left PMC was stronger for CONGRUENT and INCONGRUENT as compared to REAL, reflecting the augmentation of bottom-up visual input when multisensory integration is challenged. Crucially, intrinsic connectivity in the primary somatosensory cortex (S1) was attenuated in the CONGRUENT condition, during the illusory percept. These findings support predictive coding models of the functional architecture of multisensory integration (and attenuation) in bodily perceptual experience. Copyright © 2016 Elsevier Inc. All rights reserved.
Connectivity-based neurofeedback: Dynamic causal modeling for real-time fMRI☆
Koush, Yury; Rosa, Maria Joao; Robineau, Fabien; Heinen, Klaartje; W. Rieger, Sebastian; Weiskopf, Nikolaus; Vuilleumier, Patrik; Van De Ville, Dimitri; Scharnowski, Frank
2013-01-01
Neurofeedback based on real-time fMRI is an emerging technique that can be used to train voluntary control of brain activity. Such brain training has been shown to lead to behavioral effects that are specific to the functional role of the targeted brain area. However, real-time fMRI-based neurofeedback so far was limited to mainly training localized brain activity within a region of interest. Here, we overcome this limitation by presenting near real-time dynamic causal modeling in order to provide feedback information based on connectivity between brain areas rather than activity within a single brain area. Using a visual–spatial attention paradigm, we show that participants can voluntarily control a feedback signal that is based on the Bayesian model comparison between two predefined model alternatives, i.e. the connectivity between left visual cortex and left parietal cortex vs. the connectivity between right visual cortex and right parietal cortex. Our new approach thus allows for training voluntary control over specific functional brain networks. Because most mental functions and most neurological disorders are associated with network activity rather than with activity in a single brain region, this novel approach is an important methodological innovation in order to more directly target functionally relevant brain networks. PMID:23668967
Comparison of two integration methods for dynamic causal modeling of electrophysiological data.
Lemaréchal, Jean-Didier; George, Nathalie; David, Olivier
2018-06-01
Dynamic causal modeling (DCM) is a methodological approach to study effective connectivity among brain regions. Based on a set of observations and a biophysical model of brain interactions, DCM uses a Bayesian framework to estimate the posterior distribution of the free parameters of the model (e.g. modulation of connectivity) and infer architectural properties of the most plausible model (i.e. model selection). When modeling electrophysiological event-related responses, the estimation of the model relies on the integration of the system of delay differential equations (DDEs) that describe the dynamics of the system. In this technical note, we compared two numerical schemes for the integration of DDEs. The first, and standard, scheme approximates the DDEs (more precisely, the state of the system, with respect to conduction delays among brain regions) using ordinary differential equations (ODEs) and solves it with a fixed step size. The second scheme uses a dedicated DDEs solver with adaptive step sizes to control error, making it theoretically more accurate. To highlight the effects of the approximation used by the first integration scheme in regard to parameter estimation and Bayesian model selection, we performed simulations of local field potentials using first, a simple model comprising 2 regions and second, a more complex model comprising 6 regions. In these simulations, the second integration scheme served as the standard to which the first one was compared. Then, the performances of the two integration schemes were directly compared by fitting a public mismatch negativity EEG dataset with different models. The simulations revealed that the use of the standard DCM integration scheme was acceptable for Bayesian model selection but underestimated the connectivity parameters and did not allow an accurate estimation of conduction delays. Fitting to empirical data showed that the models systematically obtained an increased accuracy when using the second
Network interactions underlying mirror feedback in stroke: A dynamic causal modeling study
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Soha Saleh
2017-01-01
Full Text Available Mirror visual feedback (MVF is potentially a powerful tool to facilitate recovery of disordered movement and stimulate activation of under-active brain areas due to stroke. The neural mechanisms underlying MVF have therefore been a focus of recent inquiry. Although it is known that sensorimotor areas can be activated via mirror feedback, the network interactions driving this effect remain unknown. The aim of the current study was to fill this gap by using dynamic causal modeling to test the interactions between regions in the frontal and parietal lobes that may be important for modulating the activation of the ipsilesional motor cortex during mirror visual feedback of unaffected hand movement in stroke patients. Our intent was to distinguish between two theoretical neural mechanisms that might mediate ipsilateral activation in response to mirror-feedback: transfer of information between bilateral motor cortices versus recruitment of regions comprising an action observation network which in turn modulate the motor cortex. In an event-related fMRI design, fourteen chronic stroke subjects performed goal-directed finger flexion movements with their unaffected hand while observing real-time visual feedback of the corresponding (veridical or opposite (mirror hand in virtual reality. Among 30 plausible network models that were tested, the winning model revealed significant mirror feedback-based modulation of the ipsilesional motor cortex arising from the contralesional parietal cortex, in a region along the rostral extent of the intraparietal sulcus. No winning model was identified for the veridical feedback condition. We discuss our findings in the context of supporting the latter hypothesis, that mirror feedback-based activation of motor cortex may be attributed to engagement of a contralateral (contralesional action observation network. These findings may have important implications for identifying putative cortical areas, which may be targeted with
Albouy, Philippe; Mattout, Jérémie; Sanchez, Gaëtan; Tillmann, Barbara; Caclin, Anne
2015-01-01
Congenital amusia is a neuro-developmental disorder that primarily manifests as a difficulty in the perception and memory of pitch-based materials, including music. Recent findings have shown that the amusic brain exhibits altered functioning of a fronto-temporal network during pitch perception and short-term memory. Within this network, during the encoding of melodies, a decreased right backward frontal-to-temporal connectivity was reported in amusia, along with an abnormal connectivity within and between auditory cortices. The present study investigated whether connectivity patterns between these regions were affected during the short-term memory retrieval of melodies. Amusics and controls had to indicate whether sequences of six tones that were presented in pairs were the same or different. When melodies were different only one tone changed in the second melody. Brain responses to the changed tone in "Different" trials and to its equivalent (original) tone in "Same" trials were compared between groups using Dynamic Causal Modeling (DCM). DCM results confirmed that congenital amusia is characterized by an altered effective connectivity within and between the two auditory cortices during sound processing. Furthermore, right temporal-to-frontal message passing was altered in comparison to controls, with notably an increase in "Same" trials. An additional analysis in control participants emphasized that the detection of an unexpected event in the typically functioning brain is supported by right fronto-temporal connections. The results can be interpreted in a predictive coding framework as reflecting an abnormal prediction error sent by temporal auditory regions towards frontal areas in the amusic brain.
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Philippe eAlbouy
2015-02-01
Full Text Available Congenital amusia is a neuro-developmental disorder that primarily manifests as a difficulty in the perception and memory of pitch-based materials, including music. Recent findings have shown that the amusic brain exhibits altered functioning of a fronto-temporal network during pitch perception and memory. Within this network, during the encoding of melodies, a decreased right backward frontal-to-temporal connectivity was reported in amusia, along with an abnormal connectivity within and between auditory cortices. The present study investigated whether connectivity patterns between these regions were affected during the retrieval of melodies. Amusics and controls had to indicate whether sequences of six tones that were presented in pairs were the same or different. When melodies were different only one tone changed in the second melody. Brain responses to the changed tone in Different trials and to its equivalent (original tone in Same trials were compared between groups using Dynamic Causal Modeling (DCM. DCM results confirmed that congenital amusia is characterized by an altered effective connectivity within and between the two auditory cortices during sound processing. Furthermore, right temporal-to-frontal message passing was altered in comparison to controls, with an increase in Same trials and a decrease in Different trials. An additional analysis in control participants emphasized that the detection of an unexpected event in the typically functioning brain is supported by right fronto-temporal connections. The results can be interpreted in a predictive coding framework as reflecting an abnormal prediction error sent by temporal auditory regions towards frontal areas in the amusic brain.
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Liangsuo Ma
2015-01-01
Full Text Available Cocaine dependence is associated with increased impulsivity in humans. Both cocaine dependence and impulsive behavior are under the regulatory control of cortico-striatal networks. One behavioral laboratory measure of impulsivity is response inhibition (ability to withhold a prepotent response in which altered patterns of regional brain activation during executive tasks in service of normal performance are frequently found in cocaine dependent (CD subjects studied with functional magnetic resonance imaging (fMRI. However, little is known about aberrations in specific directional neuronal connectivity in CD subjects. The present study employed fMRI-based dynamic causal modeling (DCM to study the effective (directional neuronal connectivity associated with response inhibition in CD subjects, elicited under performance of a Go/NoGo task with two levels of NoGo difficulty (Easy and Hard. The performance on the Go/NoGo task was not significantly different between CD subjects and controls. The DCM analysis revealed that prefrontal–striatal connectivity was modulated (influenced during the NoGo conditions for both groups. The effective connectivity from left (L anterior cingulate cortex (ACC to L caudate was similarly modulated during the Easy NoGo condition for both groups. During the Hard NoGo condition in controls, the effective connectivity from right (R dorsolateral prefrontal cortex (DLPFC to L caudate became more positive, and the effective connectivity from R ventrolateral prefrontal cortex (VLPFC to L caudate became more negative. In CD subjects, the effective connectivity from L ACC to L caudate became more negative during the Hard NoGo conditions. These results indicate that during Hard NoGo trials in CD subjects, the ACC rather than DLPFC or VLPFC influenced caudate during response inhibition.
Li, Liang; Li, Baojuan; Bai, Yuanhan; Liu, Wenlei; Wang, Huaning; Leung, Hoi-Chung; Tian, Ping; Zhang, Linchuan; Guo, Fan; Cui, Long-Biao; Yin, Hong; Lu, Hongbing; Tan, Qingrong
2017-07-01
Understanding the neural basis underlying major depressive disorder (MDD) is essential for the diagnosis and treatment of this mental disorder. Aberrant activation and functional connectivity of the default mode network (DMN) have been consistently found in patients with MDD. It is not known whether effective connectivity within the DMN is altered in MDD. The primary object of this study is to investigate the effective connectivity within the DMN during resting state in MDD patients before and after eight weeks of antidepressant treatment. We defined four regions of the DMN (medial frontal cortex, posterior cingulate cortex, left parietal cortex, and right parietal cortex) for each participant using a group independent component analysis. The coupling parameters reflecting the causal interactions among the DMN regions were estimated using spectral dynamic causal modeling (DCM). Twenty-seven MDD patients and 27 healthy controls were included in the statistical analysis. Our results showed declined influences from the left parietal cortex to other DMN regions in the pre-treatment patients as compared with healthy controls. After eight weeks of treatment, the influence from the right parietal cortex to the posterior cingulate cortex significantly decreased. These findings suggest that the reduced excitatory causal influence of the left parietal cortex is the key alteration of the DMN in patients with MDD, and the disrupted causal influences that parietal cortex exerts on the posterior cingulate cortex is responsive to antidepressant treatment.
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A. Jackson Stenner
2013-08-01
Full Text Available Rasch’s unidimensional models for measurement show how to connect object measures (e.g., reader abilities, measurement mechanisms (e.g., machine-generated cloze reading items, and observational outcomes (e.g., counts correct on reading instruments. Substantive theory shows what interventions or manipulations to the measurement mechanism can be traded off against a change to the object measure to hold the observed outcome constant. A Rasch model integrated with a substantive theory dictates the form and substance of permissible interventions. Rasch analysis, absent construct theory and an associated specification equation, is a black box in which understanding may be more illusory than not. Finally, the quantitative hypothesis can be tested by comparing theory-based trade-off relations with observed trade-off relations. Only quantitative variables (as measured support such trade-offs. Note that to test the quantitative hypothesis requires more than manipulation of the algebraic equivalencies in the Rasch model or descriptively fitting data to the model. A causal Rasch model involves experimental intervention/manipulation on either reader ability or text complexity or a conjoint intervention on both simultaneously to yield a successful prediction of the resultant observed outcome (count correct. We conjecture that when this type of manipulation is introduced for individual reader text encounters and model predictions are consistent with observations, the quantitative hypothesis is sustained.
Stenner, A Jackson; Fisher, William P; Stone, Mark H; Burdick, Donald S
2013-01-01
Rasch's unidimensional models for measurement show how to connect object measures (e.g., reader abilities), measurement mechanisms (e.g., machine-generated cloze reading items), and observational outcomes (e.g., counts correct on reading instruments). Substantive theory shows what interventions or manipulations to the measurement mechanism can be traded off against a change to the object measure to hold the observed outcome constant. A Rasch model integrated with a substantive theory dictates the form and substance of permissible interventions. Rasch analysis, absent construct theory and an associated specification equation, is a black box in which understanding may be more illusory than not. Finally, the quantitative hypothesis can be tested by comparing theory-based trade-off relations with observed trade-off relations. Only quantitative variables (as measured) support such trade-offs. Note that to test the quantitative hypothesis requires more than manipulation of the algebraic equivalencies in the Rasch model or descriptively fitting data to the model. A causal Rasch model involves experimental intervention/manipulation on either reader ability or text complexity or a conjoint intervention on both simultaneously to yield a successful prediction of the resultant observed outcome (count correct). We conjecture that when this type of manipulation is introduced for individual reader text encounters and model predictions are consistent with observations, the quantitative hypothesis is sustained.
Stenner, A. Jackson; Fisher, William P.; Stone, Mark H.; Burdick, Donald S.
2013-01-01
Rasch's unidimensional models for measurement show how to connect object measures (e.g., reader abilities), measurement mechanisms (e.g., machine-generated cloze reading items), and observational outcomes (e.g., counts correct on reading instruments). Substantive theory shows what interventions or manipulations to the measurement mechanism can be traded off against a change to the object measure to hold the observed outcome constant. A Rasch model integrated with a substantive theory dictates the form and substance of permissible interventions. Rasch analysis, absent construct theory and an associated specification equation, is a black box in which understanding may be more illusory than not. Finally, the quantitative hypothesis can be tested by comparing theory-based trade-off relations with observed trade-off relations. Only quantitative variables (as measured) support such trade-offs. Note that to test the quantitative hypothesis requires more than manipulation of the algebraic equivalencies in the Rasch model or descriptively fitting data to the model. A causal Rasch model involves experimental intervention/manipulation on either reader ability or text complexity or a conjoint intervention on both simultaneously to yield a successful prediction of the resultant observed outcome (count correct). We conjecture that when this type of manipulation is introduced for individual reader text encounters and model predictions are consistent with observations, the quantitative hypothesis is sustained. PMID:23986726
Di, Xin; Biswal, Bharat B
2014-02-01
The default mode network is part of the brain structure that shows higher neural activity and energy consumption when one is at rest. The key regions in the default mode network are highly interconnected as conveyed by both the white matter fiber tracing and the synchrony of resting-state functional magnetic resonance imaging signals. However, the causal information flow within the default mode network is still poorly understood. The current study used the dynamic causal modeling on a resting-state fMRI data set to identify the network structure underlying the default mode network. The endogenous brain fluctuations were explicitly modeled by Fourier series at the low frequency band of 0.01-0.08Hz, and those Fourier series were set as driving inputs of the DCM models. Model comparison procedures favored a model wherein the MPFC sends information to the PCC and the bilateral inferior parietal lobule sends information to both the PCC and MPFC. Further analyses provide evidence that the endogenous connectivity might be higher in the right hemisphere than in the left hemisphere. These data provided insight into the functions of each node in the DMN, and also validate the usage of DCM on resting-state fMRI data. © 2013.
Causal Loop-based Modeling on System Dynamics for Risk Communication
Energy Technology Data Exchange (ETDEWEB)
Lee, Chang Ju [Korea Institute of Nuclear Safety, Daejeon (Korea, Republic of); Kang, Kyung Min [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)
2009-10-15
It is true that a national policy should be based on public confidence, analyzing their recognition and attitude on life safety, since they have very special risk perception characteristics. For achieving effective public consensus regarding a national policy such as nuclear power, we have to utilize a risk communication (hereafter, calls RiCom) process. However, domestic research models on RiCom process do not provide a practical guideline, because most of them are still superficial and stick on an administrative aspect. Also, most of current models have no experience in terms of verification and validation for effective applications to diverse stake holders. This study focuses on public's dynamic mechanism through the modeling on system dynamics, basically utilizing casual loop diagram (CLD) and stock flow diagram (SFD), which regards as a critical technique for decision making in many industrial RiCom models.
Causal Loop-based Modeling on System Dynamics for Risk Communication
International Nuclear Information System (INIS)
Lee, Chang Ju; Kang, Kyung Min
2009-01-01
It is true that a national policy should be based on public confidence, analyzing their recognition and attitude on life safety, since they have very special risk perception characteristics. For achieving effective public consensus regarding a national policy such as nuclear power, we have to utilize a risk communication (hereafter, calls RiCom) process. However, domestic research models on RiCom process do not provide a practical guideline, because most of them are still superficial and stick on an administrative aspect. Also, most of current models have no experience in terms of verification and validation for effective applications to diverse stake holders. This study focuses on public's dynamic mechanism through the modeling on system dynamics, basically utilizing casual loop diagram (CLD) and stock flow diagram (SFD), which regards as a critical technique for decision making in many industrial RiCom models
Sharaev, Maksim G; Zavyalova, Viktoria V; Ushakov, Vadim L; Kartashov, Sergey I; Velichkovsky, Boris M
2016-01-01
The Default Mode Network (DMN) is a brain system that mediates internal modes of cognitive activity, showing higher neural activation when one is at rest. Nowadays, there is a lot of interest in assessing functional interactions between its key regions, but in the majority of studies only association of Blood-oxygen-level dependent (BOLD) activation patterns is measured, so it is impossible to identify causal influences. There are some studies of causal interactions (i.e., effective connectivity), however often with inconsistent results. The aim of the current work is to find a stable pattern of connectivity between four DMN key regions: the medial prefrontal cortex (mPFC), the posterior cingulate cortex (PCC), left and right intraparietal cortex (LIPC and RIPC). For this purpose functional magnetic resonance imaging (fMRI) data from 30 healthy subjects (1000 time points from each one) was acquired and spectral dynamic causal modeling (DCM) on a resting-state fMRI data was performed. The endogenous brain fluctuations were explicitly modeled by Discrete Cosine Set at the low frequency band of 0.0078-0.1 Hz. The best model at the group level is the one where connections from both bilateral IPC to mPFC and PCC are significant and symmetrical in strength (p bidirectional, significant in the group and weaker than connections originating from bilateral IPC. In general, all connections from LIPC/RIPC to other DMN regions are much stronger. One can assume that these regions have a driving role within the DMN. Our results replicate some data from earlier works on effective connectivity within the DMN as well as provide new insights on internal DMN relationships and brain's functioning at resting state.
Directory of Open Access Journals (Sweden)
Maksim eSharaev
2016-02-01
Full Text Available The Default Mode Network (DMN is a brain system that mediates internal modes of cognitive activity, showing higher neural activation when one is at rest. Nowadays, there is a lot of interest in assessing functional interactions between its key regions, but in the majority of studies only association of BOLD (Blood-oxygen-level dependent activation patterns is measured, so it is impossible to identify causal influences. There are some studies of causal interactions (i.e. effective connectivity, however often with inconsistent results. The aim of the current work is to find a stable pattern of connectivity between four DMN key regions: the medial prefrontal cortex mPFC, the posterior cingulate cortex PCC, left and right intraparietal cortex LIPC and RIPC. For this purpose fMRI (functional magnetic resonance imaging data from 30 healthy subjects (1000 time points from each one was acquired and spectral dynamic causal modeling (DCM on a resting-state fMRI data was performed. The endogenous brain fluctuations were explicitly modeled by Discrete Cosine Set at the low frequency band of 0.0078–0.1 Hz. The best model at the group level is the one where connections from both bilateral IPC to mPFC and PCC are significant and symmetrical in strength (p<0.05. Connections between mPFC and PCC are bidirectional, significant in the group and weaker than connections originating from bilateral IPC. In general, all connections from LIPC/RIPC to other DMN regions are much stronger. One can assume that these regions have a driving role within the DMN. Our results replicate some data from earlier works on effective connectivity within the DMN as well as provide new insights on internal DMN relationships and brain’s functioning at resting state.
Directory of Open Access Journals (Sweden)
Vadim Leonidovich Ushakov
2016-10-01
Full Text Available The purpose of this paper was to study causal relationships between left and right hippocampal regions (LHIP and RHIP, respectively within the default mode network (DMN as represented by its key structures: the medial prefrontal cortex (MPFC, posterior cingulate cortex (PCC and the inferior parietal cortex of left (LIPC and right (RIPC hemispheres. Furthermore, we were interested in testing the stability of the connectivity patterns when adding or deleting regions of interest. The functional magnetic resonance imaging (fMRI data from a group of 30 healthy right-handed subjects in the resting state were collected and a connectivity analysis was performed. To model the effective connectivity, we used the spectral Dynamic Causal Modeling (DCM. Three DCM analyses were completed. Two of them modeled interaction between five nodes that included four DMN key structures in addition to either LHIP or RHIP. The last DCM analysis modeled interactions between four nodes whereby one of the main DMN structures, PCC, was excluded from the analysis. The results of all DCM analyses indicated a high level of stability in the computational method: those parts of the winning models that included the key DMN structures demonstrated causal relations known from recent research. However, we discovered new results as well. First of all, we found a pronounced asymmetry in LHIP and RHIP connections. LHIP demonstrated a high involvement of DMN activity with preponderant information outflow to all other DMN regions. Causal interactions of LHIP were bidirectional only in the case of LIPC. On the contrary, RHIP was primarily affected by inputs from LIPC, RIPC and LHIP without influencing these or other DMN key structures. For the first time, an inhibitory link was found from MPFC to LIPC, which may indicate the subjects’ effort to maintain a resting state. Functional connectivity data echoed these results, though they also showed links not reflected in the patterns of
Health and Wealth of Elderly Couples : Causality Tests Using Dynamic Panel Data Models
Michaud, P.C.; van Soest, A.H.O.
2004-01-01
A positive relationship between socio-economic status (SES) and health, the so-called \\health-wealth gradient", is repeatedly found in most industrialized countries with similar levels of health care technology and economic welfare. This study analyzes causality from health to wealth (health
Causal reasoning with mental models
Khemlani, Sangeet S.; Barbey, Aron K.; Johnson-Laird, Philip N.
2014-01-01
This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations. It reviews evidence both to corroborate the theory and to account for phenomena sometimes taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating that mental models for causal inference are implemented within lateral prefrontal cortex. PMID:25389398
Causal reasoning with mental models.
Khemlani, Sangeet S; Barbey, Aron K; Johnson-Laird, Philip N
2014-01-01
This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations. It reviews evidence both to corroborate the theory and to account for phenomena sometimes taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating that mental models for causal inference are implemented within lateral prefrontal cortex.
Causal reasoning with mental models
Directory of Open Access Journals (Sweden)
Sangeet eKhemlani
2014-10-01
Full Text Available This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations. It reviews evidence both to corroborate the theory and to account for phenomena sometimes taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating that mental models for causal inference are implemented within lateral prefrontal cortex.
Scenario development, qualitative causal analysis and system dynamics
Directory of Open Access Journals (Sweden)
Michael H. Ruge
2009-02-01
Full Text Available The aim of this article is to demonstrate that technology assessments can be supported by methods such as scenario modeling and qualitative causal analysis. At Siemens, these techniques are used to develop preliminary purely qualitative models. These or parts of these comprehensive models may be extended to system dynamics models. While it is currently not possible to automatically generate a system dynamics models (or vice versa, obtain a qualitative simulation model from a system dynamics model, the two thechniques scenario development and qualitative causal analysis provide valuable indications on how to proceed towards a system dynamics model. For the qualitative analysis phase, the Siemens – proprietary prototype Computer – Aided Technology Assessment Software (CATS supportes complete cycle and submodel analysis. Keywords: Health care, telecommucations, qualitative model, sensitivity analysis, system dynamics.
Causal Reasoning with Mental Models
2014-08-08
The initial rubric is equivalent to an exclusive disjunction between the two causal assertions. It 488 yields the following two mental models: 489...are 575 important, whereas the functions of artifacts are important (Ahn, 1998). A genetic code is 576 accordingly more critical to being a goat than
Modeling of causality with metamaterials
International Nuclear Information System (INIS)
Smolyaninov, Igor I
2013-01-01
Hyperbolic metamaterials may be used to model a 2 + 1-dimensional Minkowski space–time in which the role of time is played by one of the spatial coordinates. When a metamaterial is built and illuminated with a coherent extraordinary laser beam, the stationary pattern of light propagation inside the metamaterial may be treated as a collection of particle world lines, which represents a complete ‘history’ of this 2 + 1-dimensional space–time. While this model may be used to build interesting space–time analogs, such as metamaterial ‘black holes’ and a metamaterial ‘big bang’, it lacks causality: since light inside the metamaterial may propagate back and forth along the ‘timelike’ spatial coordinate, events in the ‘future’ may affect events in the ‘past’. Here we demonstrate that a more sophisticated metamaterial model may fix this deficiency via breaking the mirror and temporal (PT) symmetries of the original model and producing one-way propagation along the ‘timelike’ spatial coordinate. The resulting 2 + 1-dimensional Minkowski space–time appears to be causal. This scenario may be considered as a metamaterial model of the Wheeler–Feynman absorber theory of causality. (paper)
From causal dynamical triangulations to astronomical observations
Mielczarek, Jakub
2017-09-01
This letter discusses phenomenological aspects of dimensional reduction predicted by the Causal Dynamical Triangulations (CDT) approach to quantum gravity. The deformed form of the dispersion relation for the fields defined on the CDT space-time is reconstructed. Using the Fermi satellite observations of the GRB 090510 source we find that the energy scale of the dimensional reduction is E* > 0.7 \\sqrt{4-d\\text{UV}} \\cdot 1010 \\text{GeV} at (95% CL), where d\\text{UV} is the value of the spectral dimension in the UV limit. By applying the deformed dispersion relation to the cosmological perturbations it is shown that, for a scenario when the primordial perturbations are formed in the UV region, the scalar power spectrum PS \\propto kn_S-1 , where n_S-1≈ \\frac{3 r (d\\text{UV}-2)}{(d\\text{UV}-1)r-48} . Here, r is the tensor-to-scalar ratio. We find that within the considered model, the predicted from CDT deviation from the scale invariance (n_S=1) is in contradiction with the up to date Planck and BICEP2.
Diagnostic reasoning using qualitative causal models
International Nuclear Information System (INIS)
Sudduth, A.L.
1992-01-01
The application of expert systems to reasoning problems involving real-time data from plant measurements has been a topic of much research, but few practical systems have been deployed. One obstacle to wider use of expert systems in applications involving real-time data is the lack of adequate knowledge representation methodologies for dynamic processes. Knowledge bases composed mainly of rules have disadvantages when applied to dynamic processes and real-time data. This paper describes a methodology for the development of qualitative causal models that can be used as knowledge bases for reasoning about process dynamic behavior. These models provide a systematic method for knowledge base construction, considerably reducing the engineering effort required. They also offer much better opportunities for verification and validation of the knowledge base, thus increasing the possibility of the application of expert systems to reasoning about mission critical systems. Starting with the Signed Directed Graph (SDG) method that has been successfully applied to describe the behavior of diverse dynamic processes, the paper shows how certain non-physical behaviors that result from abstraction may be eliminated by applying causal constraint to the models. The resulting Extended Signed Directed Graph (ESDG) may then be compiled to produce a model for use in process fault diagnosis. This model based reasoning methodology is used in the MOBIAS system being developed by Duke Power Company under EPRI sponsorship. 15 refs., 4 figs
Quantum gravity from simplices: analytical investigations of causal dynamical triangulations
Benedetti, D.
2007-01-01
A potentially powerful approach to quantum gravity has been developed over the last few years under the name of Causal Dynamical Triangulations. Although these models can be solved exactly in a variety of ways in the case of pure gravity in (1+1) dimensions,it is difficult to extend any of the
Salimi, Parisa; Hamedi, Mohsen; Jamshidi, Nima; Vismeh, Milad
2017-04-01
Diabetes and its associated complications are realized as one of the most challenging medical conditions threatening more than 29 million people only in the USA. The forecasts suggest a suffering of more than half a billion worldwide by 2030. Amid all diabetic complications, diabetic foot ulcer (DFU) has attracted much scientific investigations to lead to a better management of this disease. In this paper, a system thinking methodology is adopted to investigate the dynamic nature of the ulceration. The causal loop diagram as a tool is utilized to illustrate the well-researched relations and interrelations between causes of the DFU. The result of clustering causality evaluation suggests a vicious loop that relates external trauma to callus. Consequently a hypothesis is presented which localizes development of foot ulceration considering distribution of normal and shear stress. It specifies that normal and tangential forces, as the main representatives of external trauma, play the most important role in foot ulceration. The evaluation of this hypothesis suggests the significance of the information related to both normal and shear stress for managing DFU. The results also discusses how these two react on different locations on foot such as metatarsal head, heel and hallux. The findings of this study can facilitate tackling the complexity of DFU problem and looking for constructive mitigation measures. Moreover they lead to developing a more promising methodology for managing DFU including better prognosis, designing prosthesis and insoles for DFU and patient caring recommendations. Copyright © 2017 Elsevier Ltd. All rights reserved.
Exploring Torus Universes in Causal Dynamical Triangulations
DEFF Research Database (Denmark)
Budd, Timothy George; Loll, R.
2013-01-01
Motivated by the search for new observables in nonperturbative quantum gravity, we consider Causal Dynamical Triangulations (CDT) in 2+1 dimensions with the spatial topology of a torus. This system is of particular interest, because one can study not only the global scale factor, but also global...... shape variables in the presence of arbitrary quantum fluctuations of the geometry. Our initial investigation focusses on the dynamics of the scale factor and uncovers a qualitatively new behaviour, which leads us to investigate a novel type of boundary conditions for the path integral. Comparing large....... Apart from setting the stage for the analysis of shape dynamics on the torus, the new set-up highlights the role of nontrivial boundaries and topology....
Linear causal modeling with structural equations
Mulaik, Stanley A
2009-01-01
Emphasizing causation as a functional relationship between variables that describe objects, Linear Causal Modeling with Structural Equations integrates a general philosophical theory of causation with structural equation modeling (SEM) that concerns the special case of linear causal relations. In addition to describing how the functional relation concept may be generalized to treat probabilistic causation, the book reviews historical treatments of causation and explores recent developments in experimental psychology on studies of the perception of causation. It looks at how to perceive causal
A Causal Model of Faculty Research Productivity.
Bean, John P.
A causal model of faculty research productivity was developed through a survey of the literature. Models of organizational behavior, organizational effectiveness, and motivation were synthesized into a causal model of productivity. Two general types of variables were assumed to affect individual research productivity: institutional variables and…
Pearl, Judea
2000-03-01
Written by one of the pre-eminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, philosophy, cognitive science, and the health and social sciences. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artifical intelligence, business, epidemiology, social science and economics. Students in these areas will find natural models, simple identification procedures, and precise mathematical definitions of causal concepts that traditional texts have tended to evade or make unduly complicated. This book will be of interest to professionals and students in a wide variety of fields. Anyone who wishes to elucidate meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, or form theories of causal understanding and causal speech will find this book stimulating and invaluable.
Bose, Eliezer; Hravnak, Marilyn; Sereika, Susan M
Patients undergoing continuous vital sign monitoring (heart rate [HR], respiratory rate [RR], pulse oximetry [SpO2]) in real time display interrelated vital sign changes during situations of physiological stress. Patterns in this physiological cross-talk could portend impending cardiorespiratory instability (CRI). Vector autoregressive (VAR) modeling with Granger causality tests is one of the most flexible ways to elucidate underlying causal mechanisms in time series data. The purpose of this article is to illustrate the development of patient-specific VAR models using vital sign time series data in a sample of acutely ill, monitored, step-down unit patients and determine their Granger causal dynamics prior to onset of an incident CRI. CRI was defined as vital signs beyond stipulated normality thresholds (HR = 40-140/minute, RR = 8-36/minute, SpO2 time segment prior to onset of first CRI was chosen for time series modeling in 20 patients using a six-step procedure: (a) the uniform time series for each vital sign was assessed for stationarity, (b) appropriate lag was determined using a lag-length selection criteria, (c) the VAR model was constructed, (d) residual autocorrelation was assessed with the Lagrange Multiplier test, (e) stability of the VAR system was checked, and (f) Granger causality was evaluated in the final stable model. The primary cause of incident CRI was low SpO2 (60% of cases), followed by out-of-range RR (30%) and HR (10%). Granger causality testing revealed that change in RR caused change in HR (21%; i.e., RR changed before HR changed) more often than change in HR causing change in RR (15%). Similarly, changes in RR caused changes in SpO2 (15%) more often than changes in SpO2 caused changes in RR (9%). For HR and SpO2, changes in HR causing changes in SpO2 and changes in SpO2 causing changes in HR occurred with equal frequency (18%). Within this sample of acutely ill patients who experienced a CRI event, VAR modeling indicated that RR changes
Bose, Eliezer; Hravnak, Marilyn; Sereika, Susan M.
2016-01-01
Background Patients undergoing continuous vital sign monitoring (heart rate [HR], respiratory rate [RR], pulse oximetry [SpO2]) in real time display inter-related vital sign changes during situations of physiologic stress. Patterns in this physiological cross-talk could portend impending cardiorespiratory instability (CRI). Vector autoregressive (VAR) modeling with Granger causality tests is one of the most flexible ways to elucidate underlying causal mechanisms in time series data. Purpose The purpose of this article is to illustrate development of patient-specific VAR models using vital sign time series (VSTS) data in a sample of acutely ill, monitored, step-down unit (SDU) patients, and determine their Granger causal dynamics prior to onset of an incident CRI. Approach CRI was defined as vital signs beyond stipulated normality thresholds (HR = 40–140/minute, RR = 8–36/minute, SpO2 < 85%) and persisting for 3 minutes within a 5-minute moving window (60% of the duration of the window). A 6-hour time segment prior to onset of first CRI was chosen for time series modeling in 20 patients using a six-step procedure: (a) the uniform time series for each vital sign was assessed for stationarity; (b) appropriate lag was determined using a lag-length selection criteria; (c) the VAR model was constructed; (d) residual autocorrelation was assessed with the Lagrange Multiplier test; (e) stability of the VAR system was checked; and (f) Granger causality was evaluated in the final stable model. Results The primary cause of incident CRI was low SpO2 (60% of cases), followed by out-of-range RR (30%) and HR (10%). Granger causality testing revealed that change in RR caused change in HR (21%) (i.e., RR changed before HR changed) more often than change in HR causing change in RR (15%). Similarly, changes in RR caused changes in SpO2 (15%) more often than changes in SpO2 caused changes in RR (9%). For HR and SpO2, changes in HR causing changes in SpO2 and changes in SpO2 causing
Causal Modelling in Evaluation Research.
Winteler, Adolf
1983-01-01
A study applied path analysis methods, using new techniques of causal analysis, to the problem of predicting the achievement, dropout rate, and satisfaction of university students. Besides providing explanations, the technique indicates possible remedial measures. (MSE)
Causal relations among events and states in dynamic geographical phenomena
Huang, Zhaoqiang; Feng, Xuezhi; Xuan, Wenling; Chen, Xiuwan
2007-06-01
There is only a static state of the real world to be recorded in conventional geographical information systems. However, there is not only static information but also dynamic information in geographical phenomena. So that how to record the dynamic information and reveal the relations among dynamic information is an important issue in a spatio-temporal information system. From an ontological perspective, we can initially divide the spatio-temporal entities in the world into continuants and occurrents. Continuant entities endure through some extended (although possibly very short) interval of time (e.g., houses, roads, cities, and real-estate). Occurrent entities happen and are then gone (e.g., a house repair job, road construction project, urban expansion, real-estate transition). From an information system perspective, continuants and occurrents that have a unique identity in the system are referred to as objects and events, respectively. And the change is represented implicitly by static snapshots in current spatial temporal information systems. In the previous models, the objects can be considered as the fundamental components of the system, and the change is modeled by considering time-varying attributes of these objects. In the spatio-temporal database, the temporal information that is either interval or instant is involved and the underlying data structures and indexes for temporal are considerable investigated. However, there is the absence of explicit ways of considering events, which affect the attributes of objects or the state. So the research issue of this paper focuses on how to model events in conceptual models of dynamic geographical phenomena and how to represent the causal relations among events and the objects or states. Firstly, the paper reviews the conceptual modeling in a temporal GIS by researchers. Secondly, this paper discusses the spatio-temporal entities: objects and events. Thirdly, this paper investigates the causal relations amongst
Directory of Open Access Journals (Sweden)
Inken Rothkirch
Full Text Available Writer's cramp (WC is a focal task-specific dystonia characterized by sustained or intermittent muscle contractions while writing, particularly with the dominant hand. Since structural lesions rarely cause WC, it has been assumed that the disease might be caused by a functional maladaptation within the sensory-motor system. Therefore, our objective was to examine the differences between patients suffering from WC and a healthy control (HC group with regard to the effective connectivity that describes causal influences one brain region exerts over another within the motor network. The effective connectivity within a network including contralateral motor cortex (M1, supplementary motor area (SMA, globus pallidus (GP, putamen (PU and ipsilateral cerebellum (CB was investigated using dynamic causal modeling (DCM for fMRI. Eight connectivity models of functional motor systems were compared. Fifteen WC patients and 18 age-matched HC performed a sequential, five-element finger-tapping task with the non-dominant and non-affected left hand within a 3 T MRI-scanner as quickly and accurately as possible. The task was conducted in a fixed block design repeated 15 times and included 30 s of tapping followed by 30 s of rest. DCM identified the same model in WC and HC as superior for reflecting basal ganglia and cerebellar motor circuits of healthy subjects. The M1-PU, as well as M1-CB connectivity, was more strongly influenced by tapping in WC, but the intracortical M1-SMA connection was more facilitating in controls. Inhibiting influences originating from GP to M1 were stronger in controls compared to WC patients whereby facilitating influences the PU exerts over CB and CB exerts over M1 were not as strong. Although the same model structure explains the given data best, DCM confirms previous research demonstrating a malfunction in effective connectivity intracortically (M1-SMA and in the cortico-basal ganglia circuitry in WC. In addition, DCM analysis
International Nuclear Information System (INIS)
Omri, Anis; Kahouli, Bassem
2014-01-01
This paper examines the interrelationships between energy consumption, foreign direct investment and economic growth using dynamic panel data models in simultaneous-equations for a global panel consisting of 65 countries. The time component of our dataset is 1990–2011 inclusive. To make the panel data analysis more homogenous, we also investigate this interrelationship for a number of sub-panels which are constructed based on the income level of countries. In this way, we end up with three income panels; namely, high income, middle income, and low income panels. In the empirical part, we draw on the growth theory and augment the classical growth model, which consists of capital stock, labor force and inflation, with foreign direct investment and energy. Generally, we show mixed results about the interrelationship between energy consumption, FDI and economic growth. - Highlights: • We examine the energy–FDI–growth nexus for a global panel of 65 countries. • Dynamic simultaneous-equation panel data models are used to address this issue. • We also investigate this nexus for three sub-panels which are constructed based on the income level of countries. • We show mixed results about the interrelationship between the three variables
Causality analysis in business performance measurement system using system dynamics methodology
Yusof, Zainuridah; Yusoff, Wan Fadzilah Wan; Maarof, Faridah
2014-07-01
One of the main components of the Balanced Scorecard (BSC) that differentiates it from any other performance measurement system (PMS) is the Strategy Map with its unidirectional causality feature. Despite its apparent popularity, criticisms on the causality have been rigorously discussed by earlier researchers. In seeking empirical evidence of causality, propositions based on the service profit chain theory were developed and tested using the econometrics analysis, Granger causality test on the 45 data points. However, the insufficiency of well-established causality models was found as only 40% of the causal linkages were supported by the data. Expert knowledge was suggested to be used in the situations of insufficiency of historical data. The Delphi method was selected and conducted in obtaining the consensus of the causality existence among the 15 selected expert persons by utilizing 3 rounds of questionnaires. Study revealed that only 20% of the propositions were not supported. The existences of bidirectional causality which demonstrate significant dynamic environmental complexity through interaction among measures were obtained from both methods. With that, a computer modeling and simulation using System Dynamics (SD) methodology was develop as an experimental platform to identify how policies impacting the business performance in such environments. The reproduction, sensitivity and extreme condition tests were conducted onto developed SD model to ensure their capability in mimic the reality, robustness and validity for causality analysis platform. This study applied a theoretical service management model within the BSC domain to a practical situation using SD methodology where very limited work has been done.
International Nuclear Information System (INIS)
Jahangir Alam, Mohammad; Ara Begum, Ismat; Buysse, Jeroen; Van Huylenbroeck, Guido
2012-01-01
The paper investigates the possible existence of dynamic causality between energy consumption, electricity consumption, carbon emissions and economic growth in Bangladesh. First, we have tested cointegration relationships using the Johansen bi-variate cointegration model. This is complemented with an analysis of an auto-regressive distributed lag model to examine the results' robustness. Then, the Granger short-run, the long-run and strong causality are tested with a vector error correction modelling framework. The results indicate that uni-directional causality exists from energy consumption to economic growth both in the short and the long-run while a bi-directional long-run causality exists between electricity consumption and economic growth but no causal relationship exists in short-run. The strong causality results indicate bi-directional causality for both the cases. A uni-directional causality runs from energy consumption to CO 2 emission for the short-run but feedback causality exists in the long-run. CO 2 Granger causes economic growth both in the short and in the long-run. An important policy implication is that energy (electricity as well) can be considered as an important factor for the economic growth in Bangladesh. Moreover, as higher energy consumption also means higher pollution in the long-run, policy makers should stimulate alternative energy sources for meeting up the increasing energy demand. - Highlights: ► Dynamic causality among energy and electricity consumption, CO 2 and economic growth. ► Uni-directional causality exists from energy consumption to economic growth. ► Bi-directional causality exists between electricity consumption and economic growth. ► Feedback causality exists between CO 2 emission to energy consumption. ► CO 2 Granger causes economic growth both in the short and in the long-run.
Exploring Causal Models of Educational Achievement.
Parkerson, Jo Ann; And Others
1984-01-01
This article evaluates five causal model of educational productivity applied to learning science in a sample of 882 fifth through eighth graders. Each model explores the relationship between achievement and a combination of eight constructs: home environment, peer group, media, ability, social environment, time on task, motivation, and…
A quantum probability model of causal reasoning
Directory of Open Access Journals (Sweden)
Jennifer S Trueblood
2012-05-01
Full Text Available People can often outperform statistical methods and machine learning algorithms in situations that involve making inferences about the relationship between causes and effects. While people are remarkably good at causal reasoning in many situations, there are several instances where they deviate from expected responses. This paper examines three situations where judgments related to causal inference problems produce unexpected results and describes a quantum inference model based on the axiomatic principles of quantum probability theory that can explain these effects. Two of the three phenomena arise from the comparison of predictive judgments (i.e., the conditional probability of an effect given a cause with diagnostic judgments (i.e., the conditional probability of a cause given an effect. The third phenomenon is a new finding examining order effects in predictive causal judgments. The quantum inference model uses the notion of incompatibility among different causes to account for all three phenomena. Psychologically, the model assumes that individuals adopt different points of view when thinking about different causes. The model provides good fits to the data and offers a coherent account for all three causal reasoning effects thus proving to be a viable new candidate for modeling human judgment.
Causal Measurement Models: Can Criticism Stimulate Clarification?
Markus, Keith A.
2016-01-01
In their 2016 work, Aguirre-Urreta et al. provided a contribution to the literature on causal measurement models that enhances clarity and stimulates further thinking. Aguirre-Urreta et al. presented a form of statistical identity involving mapping onto the portion of the parameter space involving the nomological net, relationships between the…
A Causal Model of Faculty Turnover Intentions.
Smart, John C.
1990-01-01
A causal model assesses the relative influence of individual attributes, institutional characteristics, contextual-work environment variables, and multiple measures of job satisfaction on faculty intentions to leave their current institutions. Factors considered include tenure status, age, institutional status, governance style, organizational…
Causality in Psychiatry: A Hybrid Symptom Network Construct Model
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Gerald eYoung
2015-11-01
Full Text Available Causality or etiology in psychiatry is marked by standard biomedical, reductionistic models (symptoms reflect the construct involved that inform approaches to nosology, or classification, such as in the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; American Psychiatric Association, 2013. However, network approaches to symptom interaction (i.e., symptoms are formative of the construct; e.g., McNally, Robinaugh, Wu, Wang, Deserno, & Borsboom, 2014, for PTSD (posttraumatic stress disorder are being developed that speak to bottom-up processes in mental disorder, in contrast to the typical top-down psychological construct approach. The present article presents a hybrid top-down, bottom-up model of the relationship between symptoms and mental disorder, viewing symptom expression and their causal complex as a reciprocally dynamic system with multiple levels, from lower-order symptoms in interaction to higher-order constructs affecting them. The hybrid model hinges on good understanding of systems theory in which it is embedded, so that the article reviews in depth nonlinear dynamical systems theory (NLDST. The article applies the concept of emergent circular causality (Young, 2011 to symptom development, as well. Conclusions consider that symptoms vary over several dimensions, including: subjectivity; objectivity; conscious motivation effort; and unconscious influences, and the degree to which individual (e.g., meaning and universal (e.g., causal processes are involved. The opposition between science and skepticism is a complex one that the article addresses in final comments.
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Marcela Perrone-Bertolotti
2017-06-01
Full Text Available It has been suggested that dorsal and ventral pathways support distinct aspects of language processing. Yet, the full extent of their involvement and their inter-regional connectivity in visual word recognition is still unknown. Studies suggest that they might reflect the dual-route model of reading, with the dorsal pathway more involved in grapho-phonological conversion during phonological tasks, and the ventral pathway performing lexico-semantic access during semantic tasks. Furthermore, this subdivision is also suggested at the level of the inferior frontal cortex, involving ventral and dorsal parts for lexico-semantic and phonological processing, respectively. In the present study, we assessed inter-regional brain connectivity and task-induced modulations of brain activity during a phoneme detection and semantic categorization tasks, using fMRI in healthy subject. We used a dynamic causal modeling approach to assess inter-regional connectivity and task demand modulation within the dorsal and ventral pathways, including the following network components: the ventral occipito-temporal cortex (vOTC; dorsal and ventral, the superior temporal gyrus (STG; dorsal, the dorsal inferior frontal gyrus (dIFG; dorsal, and the ventral IFG (vIFG; ventral. We report three distinct inter-regional interactions supporting orthographic information transfer from vOTC to other language regions (vOTC -> STG, vOTC -> vIFG and vOTC -> dIFG regardless of task demands. Moreover, we found that (a during semantic processing (direct ventral pathway the vOTC -> vIFG connection strength specifically increased and (b a lack of modulation of the vOTC -> dIFG connection strength by the task that could suggest a more general involvement of the dorsal pathway during visual word recognition. Results are discussed in terms of anatomo-functional connectivity of visual word recognition network.
Indications of de Sitter spacetime from classical sequential growth dynamics of causal sets
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Ahmed, Maqbool; Rideout, David
2010-01-01
A large class of the dynamical laws for causal sets described by a classical process of sequential growth yields a cyclic universe, whose cycles of expansion and contraction are punctuated by single 'origin elements' of the causal set. We present evidence that the effective dynamics of the immediate future of one of these origin elements, within the context of the sequential growth dynamics, yields an initial period of de Sitter-like exponential expansion, and argue that the resulting picture has many attractive features as a model of the early universe, with the potential to solve some of the standard model puzzles without any fine-tuning.
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Long-Biao eCui
2015-11-01
Full Text Available Understanding the neural basis of schizophrenia (SZ is important for shedding light on the neurobiological mechanisms underlying this mental disorder. Structural and functional alterations in the anterior cingulate cortex (ACC, dorsolateral prefrontal cortex (DLPFC, hippocampus, and medial prefrontal cortex (MPFC have been implicated in the neurobiology of SZ. However, the effective connectivity among them in SZ remains unclear. The current study investigated how neuronal pathways involving these regions were affected in first-episode SZ using functional magnetic resonance imaging (fMRI. Forty-nine patients with a first-episode of psychosis and diagnosis of SZ—according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision—were studied. Fifty healthy controls (HCs were included for comparison. All subjects underwent resting state fMRI. We used spectral dynamic causal modeling (DCM to estimate directed connections among the bilateral ACC, DLPFC, hippocampus, and MPFC. We characterized the differences using Bayesian parameter averaging (BPA in addition to classical inference (t-test. In addition to common effective connectivity in these two groups, HCs displayed widespread significant connections predominantly involved in ACC not detected in SZ patients, but SZ showed few connections. Based on BPA results, SZ patients exhibited anterior cingulate cortico-prefrontal-hippocampal hyperconnectivity, as well as ACC-related and hippocampal-dorsolateral prefrontal-medial prefrontal hypoconnectivity. In summary, sDCM revealed the pattern of effective connectivity involving ACC in patients with first-episode SZ. This study provides a potential link between SZ and dysfunction of ACC, creating an ideal situation to associate mechanisms behind SZ with aberrant connectivity among these cognition and emotion-related regions.
Causal models in epidemiology: past inheritance and genetic future
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Kriebel David
2006-07-01
Full Text Available Abstract The eruption of genetic research presents a tremendous opportunity to epidemiologists to improve our ability to identify causes of ill health. Epidemiologists have enthusiastically embraced the new tools of genomics and proteomics to investigate gene-environment interactions. We argue that neither the full import nor limitations of such studies can be appreciated without clarifying underlying theoretical models of interaction, etiologic fraction, and the fundamental concept of causality. We therefore explore different models of causality in the epidemiology of disease arising out of genes, environments, and the interplay between environments and genes. We begin from Rothman's "pie" model of necessary and sufficient causes, and then discuss newer approaches, which provide additional insights into multifactorial causal processes. These include directed acyclic graphs and structural equation models. Caution is urged in the application of two essential and closely related concepts found in many studies: interaction (effect modification and the etiologic or attributable fraction. We review these concepts and present four important limitations. 1. Interaction is a fundamental characteristic of any causal process involving a series of probabilistic steps, and not a second-order phenomenon identified after first accounting for "main effects". 2. Standard methods of assessing interaction do not adequately consider the life course, and the temporal dynamics through which an individual's sufficient cause is completed. Different individuals may be at different stages of development along the path to disease, but this is not usually measurable. Thus, for example, acquired susceptibility in children can be an important source of variation. 3. A distinction must be made between individual-based and population-level models. Most epidemiologic discussions of causality fail to make this distinction. 4. At the population level, there is additional
Charakopoulos, A. K.; Katsouli, G. A.; Karakasidis, T. E.
2018-04-01
Understanding the underlying processes and extracting detailed characteristics of spatiotemporal dynamics of ocean and atmosphere as well as their interaction is of significant interest and has not been well thoroughly established. The purpose of this study was to examine the performance of two main additional methodologies for the identification of spatiotemporal underlying dynamic characteristics and patterns among atmospheric and oceanic variables from Seawatch buoys from Aegean and Ionian Sea, provided by the Hellenic Center for Marine Research (HCMR). The first approach involves the estimation of cross correlation analysis in an attempt to investigate time-lagged relationships, and further in order to identify the direction of interactions between the variables we performed the Granger causality method. According to the second approach the time series are converted into complex networks and then the main topological network properties such as degree distribution, average path length, diameter, modularity and clustering coefficient are evaluated. Our results show that the proposed analysis of complex network analysis of time series can lead to the extraction of hidden spatiotemporal characteristics. Also our findings indicate high level of positive and negative correlations and causalities among variables, both from the same buoy and also between buoys from different stations, which cannot be determined from the use of simple statistical measures.
Application of dynamic uncertain causality graph in spacecraft fault diagnosis: Logic cycle
Yao, Quanying; Zhang, Qin; Liu, Peng; Yang, Ping; Zhu, Ma; Wang, Xiaochen
2017-04-01
Intelligent diagnosis system are applied to fault diagnosis in spacecraft. Dynamic Uncertain Causality Graph (DUCG) is a new probability graphic model with many advantages. In the knowledge expression of spacecraft fault diagnosis, feedback among variables is frequently encountered, which may cause directed cyclic graphs (DCGs). Probabilistic graphical models (PGMs) such as bayesian network (BN) have been widely applied in uncertain causality representation and probabilistic reasoning, but BN does not allow DCGs. In this paper, DUGG is applied to fault diagnosis in spacecraft: introducing the inference algorithm for the DUCG to deal with feedback. Now, DUCG has been tested in 16 typical faults with 100% diagnosis accuracy.
Detecting dynamic causal inference in nonlinear two-phase fracture flow
Faybishenko, Boris
2017-08-01
Identifying dynamic causal inference involved in flow and transport processes in complex fractured-porous media is generally a challenging task, because nonlinear and chaotic variables may be positively coupled or correlated for some periods of time, but can then become spontaneously decoupled or non-correlated. In his 2002 paper (Faybishenko, 2002), the author performed a nonlinear dynamical and chaotic analysis of time-series data obtained from the fracture flow experiment conducted by Persoff and Pruess (1995), and, based on the visual examination of time series data, hypothesized that the observed pressure oscillations at both inlet and outlet edges of the fracture result from a superposition of both forward and return waves of pressure propagation through the fracture. In the current paper, the author explores an application of a combination of methods for detecting nonlinear chaotic dynamics behavior along with the multivariate Granger Causality (G-causality) time series test. Based on the G-causality test, the author infers that his hypothesis is correct, and presents a causation loop diagram of the spatial-temporal distribution of gas, liquid, and capillary pressures measured at the inlet and outlet of the fracture. The causal modeling approach can be used for the analysis of other hydrological processes, for example, infiltration and pumping tests in heterogeneous subsurface media, and climatic processes, for example, to find correlations between various meteorological parameters, such as temperature, solar radiation, barometric pressure, etc.
Theories of conduct disorder: a causal modelling analysis
Krol, N.P.C.M.; Morton, J.; Bruyn, E.E.J. De
2004-01-01
Background: If a clinician has to make decisions on diagnosis and treatment, he or she is confronted with a variety of causal theories. In order to compare these theories a neutral terminology and notational system is needed. The Causal Modelling framework involving three levels of description –
The Causal Foundations of Structural Equation Modeling
2012-02-16
and Baumrind (1993).” This, together with the steady influx of statisticians into the field, has left SEM re- searchers in a quandary about the...considerations. Journal of Personality and Social Psychology 51 1173–1182. Baumrind , D. (1993). Specious causal attributions in social sciences: The
Energy Technology Data Exchange (ETDEWEB)
Chang, Y.H.J. [Center for Risk and Reliability, University of Maryland, College Park, MD 20742 (United States) and Paul Scherrer Institute, 5232 Villigen PSI (Switzerland)]. E-mail: yhc@umd.edu; Mosleh, A. [Center for Risk and Reliability, University of Maryland, College Park, MD 20742 (United States)
2007-08-15
This is the fourth in a series of five papers describing the Information, Decision, and Action in Crew context (IDAC) operator response model for human reliability analysis. An example application of this modeling technique is also discussed in this series. The model has been developed to probabilistically predicts the responses of a nuclear power plant control room operating crew in accident conditions. The operator response spectrum includes cognitive, emotional, and physical activities during the course of an accident. This paper assesses the effects of the performance-influencing factors (PIFs) affecting the operators' problem-solving responses including information pre-processing (I), diagnosis and decision making (D), and action execution (A). Literature support and justifications are provided for the assessment on the influences of PIFs.
International Nuclear Information System (INIS)
Chang, Y.H.J.; Mosleh, A.
2007-01-01
This is the fourth in a series of five papers describing the Information, Decision, and Action in Crew context (IDAC) operator response model for human reliability analysis. An example application of this modeling technique is also discussed in this series. The model has been developed to probabilistically predicts the responses of a nuclear power plant control room operating crew in accident conditions. The operator response spectrum includes cognitive, emotional, and physical activities during the course of an accident. This paper assesses the effects of the performance-influencing factors (PIFs) affecting the operators' problem-solving responses including information pre-processing (I), diagnosis and decision making (D), and action execution (A). Literature support and justifications are provided for the assessment on the influences of PIFs
Causal dissipation for the relativistic dynamics of ideal gases.
Freistühler, Heinrich; Temple, Blake
2017-05-01
We derive a general class of relativistic dissipation tensors by requiring that, combined with the relativistic Euler equations, they form a second-order system of partial differential equations which is symmetric hyperbolic in a second-order sense when written in the natural Godunov variables that make the Euler equations symmetric hyperbolic in the first-order sense. We show that this class contains a unique element representing a causal formulation of relativistic dissipative fluid dynamics which (i) is equivalent to the classical descriptions by Eckart and Landau to first order in the coefficients of viscosity and heat conduction and (ii) has its signal speeds bounded sharply by the speed of light. Based on these properties, we propose this system as a natural candidate for the relativistic counterpart of the classical Navier-Stokes equations.
Dynamical Symmetries and Causality in Non-Equilibrium Phase Transitions
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Malte Henkel
2015-11-01
Full Text Available Dynamical symmetries are of considerable importance in elucidating the complex behaviour of strongly interacting systems with many degrees of freedom. Paradigmatic examples are cooperative phenomena as they arise in phase transitions, where conformal invariance has led to enormous progress in equilibrium phase transitions, especially in two dimensions. Non-equilibrium phase transitions can arise in much larger portions of the parameter space than equilibrium phase transitions. The state of the art of recent attempts to generalise conformal invariance to a new generic symmetry, taking into account the different scaling behaviour of space and time, will be reviewed. Particular attention will be given to the causality properties as they follow for co-variant n-point functions. These are important for the physical identification of n-point functions as responses or correlators.
An example of numerical simulation in causal set dynamics
International Nuclear Information System (INIS)
Krugly, Alexey L; Tserkovnikov, Ivan A
2013-01-01
The model of a discrete pregeometry on a microscopic scale is an x-graph. This is a directed acyclic graph. An outdegree and an indegree of each vertex are not more than 2. The sets of vertices and edges of x-graph are particular cases of causal sets. The sequential growth of a graph is an addition of new vertices one by one. A simple stochastic algorithm of sequential growth of x-graph are considered. It is based on a random walk at the x-graph. The particles in this model must be self-organized repetitive structures. We introduce the method of search of such repetitive structures. It is based on a discrete Fourier transformation. An example of numerical simulation is introduced.
Causal Bayes Model of Mathematical Competence in Kindergarten
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Božidar Tepeš
2016-06-01
Full Text Available In this paper authors define mathematical competences in the kindergarten. The basic objective was to measure the mathematical competences or mathematical knowledge, skills and abilities in mathematical education. Mathematical competences were grouped in the following areas: Arithmetic and Geometry. Statistical set consisted of 59 children, 65 to 85 months of age, from the Kindergarten Milan Sachs from Zagreb. The authors describe 13 variables for measuring mathematical competences. Five measuring variables were described for the geometry, and eight measuring variables for the arithmetic. Measuring variables are tasks which children solved with the evaluated results. By measuring mathematical competences the authors make causal Bayes model using free software Tetrad 5.2.1-3. Software makes many causal Bayes models and authors as experts chose the model of the mathematical competences in the kindergarten. Causal Bayes model describes five levels for mathematical competences. At the end of the modeling authors use Bayes estimator. In the results, authors describe by causal Bayes model of mathematical competences, causal effect mathematical competences or how intervention on some competences cause other competences. Authors measure mathematical competences with their expectation as random variables. When expectation of competences was greater, competences improved. Mathematical competences can be improved with intervention on causal competences. Levels of mathematical competences and the result of intervention on mathematical competences can help mathematical teachers.
A Temporal-Causal Modelling Approach to Integrated Contagion and Network Change in Social Networks
Blankendaal, Romy; Parinussa, Sarah; Treur, Jan
2016-01-01
This paper introduces an integrated temporal-causal model for dynamics in social networks addressing the contagion principle by which states are affected mutually, and both the homophily principle and the more-becomes-more principle by which connections are adapted over time. The integrated model
Molenaar, Peter C M
2017-01-01
Equivalences of two classes of dynamic models for weakly stationary multivariate time series are discussed: dynamic factor models and autoregressive models. It is shown that exploratory dynamic factor models can be rotated, yielding an infinite set of equivalent solutions for any observed series. It also is shown that dynamic factor models with lagged factor loadings are not equivalent to the currently popular state-space models, and that restriction of attention to the latter type of models may yield invalid results. The known equivalent vector autoregressive model types, standard and structural, are given a new interpretation in which they are conceived of as the extremes of an innovating type of hybrid vector autoregressive models. It is shown that consideration of hybrid models solves many problems, in particular with Granger causality testing.
(2+1)-dimensional quantum gravity as the continuum limit of causal dynamical triangulations
International Nuclear Information System (INIS)
Benedetti, D.; Loll, R.; Zamponi, F.
2007-01-01
We perform a nonperturbative sum over geometries in a (2+1)-dimensional quantum gravity model given in terms of causal dynamical triangulations. Inspired by the concept of triangulations of product type introduced previously, we impose an additional notion of order on the discrete, causal geometries. This simplifies the combinatorial problem of counting geometries just enough to enable us to calculate the transfer matrix between boundary states labeled by the area of the spatial universe, as well as the corresponding quantum Hamiltonian of the continuum theory. This is the first time in dimension larger than 2 that a Hamiltonian has been derived from such a model by mainly analytical means, and it opens the way for a better understanding of scaling and renormalization issues
How causal analysis can reveal autonomy in models of biological systems
Marshall, William; Kim, Hyunju; Walker, Sara I.; Tononi, Giulio; Albantakis, Larissa
2017-11-01
Standard techniques for studying biological systems largely focus on their dynamical or, more recently, their informational properties, usually taking either a reductionist or holistic perspective. Yet, studying only individual system elements or the dynamics of the system as a whole disregards the organizational structure of the system-whether there are subsets of elements with joint causes or effects, and whether the system is strongly integrated or composed of several loosely interacting components. Integrated information theory offers a theoretical framework to (1) investigate the compositional cause-effect structure of a system and to (2) identify causal borders of highly integrated elements comprising local maxima of intrinsic cause-effect power. Here we apply this comprehensive causal analysis to a Boolean network model of the fission yeast (Schizosaccharomyces pombe) cell cycle. We demonstrate that this biological model features a non-trivial causal architecture, whose discovery may provide insights about the real cell cycle that could not be gained from holistic or reductionist approaches. We also show how some specific properties of this underlying causal architecture relate to the biological notion of autonomy. Ultimately, we suggest that analysing the causal organization of a system, including key features like intrinsic control and stable causal borders, should prove relevant for distinguishing life from non-life, and thus could also illuminate the origin of life problem. This article is part of the themed issue 'Reconceptualizing the origins of life'.
The causal structure of dynamical charged black holes
International Nuclear Information System (INIS)
Hong, Sungwook E; Hwang, Dong-il; Stewart, Ewan D; Yeom, Dong-han
2010-01-01
We study the causal structure of dynamical charged black holes, with a sufficient number of massless fields, using numerical simulations. Neglecting Hawking radiation, the inner horizon is a null Cauchy horizon and a curvature singularity due to mass inflation. When we include Hawking radiation, the inner horizon becomes space-like and is separated from the Cauchy horizon, which is parallel to the out-going null direction. Since a charged black hole must eventually transit to a neutral black hole, we studied the neutralization of the black hole and observed that the inner horizon evolves into a space-like singularity, generating a Cauchy horizon which is parallel to the in-going null direction. Since the mass function is finite around the inner horizon, the inner horizon is regular and penetrable in a general relativistic sense. However, since the curvature functions become trans-Planckian, we cannot say more about the region beyond the inner horizon, and it is natural to say that there is a 'physical' space-like singularity. However, if we assume an exponentially large number of massless scalar fields, our results can be extended beyond the inner horizon. In this case, strong cosmic censorship and black hole complementarity can be violated.
The causal structure of dynamical charged black holes
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Hong, Sungwook E; Hwang, Dong-il; Stewart, Ewan D; Yeom, Dong-han, E-mail: eostm@muon.kaist.ac.k, E-mail: enotsae@gmail.co, E-mail: innocent@muon.kaist.ac.k [Department of Physics, KAIST, Daejeon 305-701 (Korea, Republic of)
2010-02-21
We study the causal structure of dynamical charged black holes, with a sufficient number of massless fields, using numerical simulations. Neglecting Hawking radiation, the inner horizon is a null Cauchy horizon and a curvature singularity due to mass inflation. When we include Hawking radiation, the inner horizon becomes space-like and is separated from the Cauchy horizon, which is parallel to the out-going null direction. Since a charged black hole must eventually transit to a neutral black hole, we studied the neutralization of the black hole and observed that the inner horizon evolves into a space-like singularity, generating a Cauchy horizon which is parallel to the in-going null direction. Since the mass function is finite around the inner horizon, the inner horizon is regular and penetrable in a general relativistic sense. However, since the curvature functions become trans-Planckian, we cannot say more about the region beyond the inner horizon, and it is natural to say that there is a 'physical' space-like singularity. However, if we assume an exponentially large number of massless scalar fields, our results can be extended beyond the inner horizon. In this case, strong cosmic censorship and black hole complementarity can be violated.
Causal Analysis for Performance Modeling of Computer Programs
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Jan Lemeire
2007-01-01
Full Text Available Causal modeling and the accompanying learning algorithms provide useful extensions for in-depth statistical investigation and automation of performance modeling. We enlarged the scope of existing causal structure learning algorithms by using the form-free information-theoretic concept of mutual information and by introducing the complexity criterion for selecting direct relations among equivalent relations. The underlying probability distribution of experimental data is estimated by kernel density estimation. We then reported on the benefits of a dependency analysis and the decompositional capacities of causal models. Useful qualitative models, providing insight into the role of every performance factor, were inferred from experimental data. This paper reports on the results for a LU decomposition algorithm and on the study of the parameter sensitivity of the Kakadu implementation of the JPEG-2000 standard. Next, the analysis was used to search for generic performance characteristics of the applications.
Causal Indicator Models: Unresolved Issues of Construction and Evaluation
West, Stephen G.; Grimm, Kevin J.
2014-01-01
These authors agree with Bainter and Bollen that causal effects represents a useful measurement structure in some applications. The structure of the science of the measurement problem should determine the model; the measurement model should not determine the science. They also applaud Bainter and Bollen's important reminder that the full…
Causality and Information Dynamics in Networked Systems with Many Agents
2017-05-11
algorithms. 6 Future Perspectives Causal graph reconstruction from noisy data is a problem of central importance. In our research we have shown how the idea ...thrust of the research was to develop methods for GCG sparsification using ideas from Tikhonov regularization and ADMM based proximal algorithms...not vary with time. The notion of Granger-causality is captured in the following definition . Definition 2.1 If ξ̂[xi(t) |Ht] < ξ̂[xi(t) |H−jt ] , (2.2
Computation of Probabilities in Causal Models of History of Science
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Osvaldo Pessoa Jr.
2006-12-01
Full Text Available : The aim of this paper is to investigate the ascription of probabilities in a causal model of an episode in the history of science. The aim of such a quantitative approach is to allow the implementation of the causal model in a computer, to run simulations. As an example, we look at the beginning of the science of magnetism, “explaining” — in a probabilistic way, in terms of a single causal model — why the field advanced in China but not in Europe (the difference is due to different prior probabilities of certain cultural manifestations. Given the number of years between the occurrences of two causally connected advances X and Y, one proposes a criterion for stipulating the value pY=X of the conditional probability of an advance Y occurring, given X. Next, one must assume a specific form for the cumulative probability function pY=X(t, which we take to be the time integral of an exponential distribution function, as is done in physics of radioactive decay. Rules for calculating the cumulative functions for more than two events are mentioned, involving composition, disjunction and conjunction of causes. We also consider the problems involved in supposing that the appearance of events in time follows an exponential distribution, which are a consequence of the fact that a composition of causes does not follow an exponential distribution, but a “hypoexponential” one. We suggest that a gamma distribution function might more adequately represent the appearance of advances.
Measured, modeled, and causal conceptions of fitness
Abrams, Marshall
2012-01-01
This paper proposes partial answers to the following questions: in what senses can fitness differences plausibly be considered causes of evolution?What relationships are there between fitness concepts used in empirical research, modeling, and abstract theoretical proposals? How does the relevance of different fitness concepts depend on research questions and methodological constraints? The paper develops a novel taxonomy of fitness concepts, beginning with type fitness (a property of a genotype or phenotype), token fitness (a property of a particular individual), and purely mathematical fitness. Type fitness includes statistical type fitness, which can be measured from population data, and parametric type fitness, which is an underlying property estimated by statistical type fitnesses. Token fitness includes measurable token fitness, which can be measured on an individual, and tendential token fitness, which is assumed to be an underlying property of the individual in its environmental circumstances. Some of the paper's conclusions can be outlined as follows: claims that fitness differences do not cause evolution are reasonable when fitness is treated as statistical type fitness, measurable token fitness, or purely mathematical fitness. Some of the ways in which statistical methods are used in population genetics suggest that what natural selection involves are differences in parametric type fitnesses. Further, it's reasonable to think that differences in parametric type fitness can cause evolution. Tendential token fitnesses, however, are not themselves sufficient for natural selection. Though parametric type fitnesses are typically not directly measurable, they can be modeled with purely mathematical fitnesses and estimated by statistical type fitnesses, which in turn are defined in terms of measurable token fitnesses. The paper clarifies the ways in which fitnesses depend on pragmatic choices made by researchers. PMID:23112804
Cho, Soohyun; Metcalfe, Arron W S; Young, Christina B; Ryali, Srikanth; Geary, David C; Menon, Vinod
2012-09-01
Children's gains in problem-solving skills during the elementary school years are characterized by shifts in the mix of problem-solving approaches, with inefficient procedural strategies being gradually replaced with direct retrieval of domain-relevant facts. We used a well-established procedure for strategy assessment during arithmetic problem solving to investigate the neural basis of this critical transition. We indexed behavioral strategy use by focusing on the retrieval frequency and examined changes in brain activity and connectivity associated with retrieval fluency during arithmetic problem solving in second- and third-grade (7- to 9-year-old) children. Children with higher retrieval fluency showed elevated signal in the right hippocampus, parahippocampal gyrus (PHG), lingual gyrus (LG), fusiform gyrus (FG), left ventrolateral PFC (VLPFC), bilateral dorsolateral PFC (DLPFC), and posterior angular gyrus. Critically, these effects were not confounded by individual differences in problem-solving speed or accuracy. Psychophysiological interaction analysis revealed significant effective connectivity of the right hippocampus with bilateral VLPFC and DLPFC during arithmetic problem solving. Dynamic causal modeling analysis revealed strong bidirectional interactions between the hippocampus and the left VLPFC and DLPFC. Furthermore, causal influences from the left VLPFC to the hippocampus served as the main top-down component, whereas causal influences from the hippocampus to the left DLPFC served as the main bottom-up component of this retrieval network. Our study highlights the contribution of hippocampal-prefrontal circuits to the early development of retrieval fluency in arithmetic problem solving and provides a novel framework for studying dynamic developmental processes that accompany children's development of problem-solving skills.
Renormalization group approach to causal bulk viscous cosmological models
International Nuclear Information System (INIS)
Belinchon, J A; Harko, T; Mak, M K
2002-01-01
The renormalization group method is applied to the study of homogeneous and flat Friedmann-Robertson-Walker type universes, filled with a causal bulk viscous cosmological fluid. The starting point of the study is the consideration of the scaling properties of the gravitational field equations, the causal evolution equation of the bulk viscous pressure and the equations of state. The requirement of scale invariance imposes strong constraints on the temporal evolution of the bulk viscosity coefficient, temperature and relaxation time, thus leading to the possibility of obtaining the bulk viscosity coefficient-energy density dependence. For a cosmological model with bulk viscosity coefficient proportional to the Hubble parameter, we perform the analysis of the renormalization group flow around the scale-invariant fixed point, thereby obtaining the long-time behaviour of the scale factor
Optimization of a dynamic uncertain causality graph for fault diagnosis in nuclear power plant
Institute of Scientific and Technical Information of China (English)
Yue Zhao; Francesco Di Maio; Enrico Zio; Qin Zhang; Chun-Ling Dong; Jin-Ying Zhang
2017-01-01
Fault diagnostics is important for safe operation of nuclear power plants (NPPs).In recent years,data-driven approaches have been proposed and implemented to tackle the problem,e.g.,neural networks,fuzzy and neurofuzzy approaches,support vector machine,K-nearest neighbor classifiers and inference methodologies.Among these methods,dynamic uncertain causality graph (DUCG)has been proved effective in many practical cases.However,the causal graph construction behind the DUCG is complicate and,in many cases,results redundant on the symptoms needed to correctly classify the fault.In this paper,we propose a method to simplify causal graph construction in an automatic way.The method consists in transforming the expert knowledge-based DCUG into a fuzzy decision tree (FDT) by extracting from the DUCG a fuzzy rule base that resumes the used symptoms at the basis of the FDT.Genetic algorithm (GA) is,then,used for the optimization of the FDT,by performing a wrapper search around the FDT:the set of symptoms selected during the iterative search are taken as the best set of symptoms for the diagnosis of the faults that can occur in the system.The effectiveness of the approach is shown with respect to a DUCG model initially built to diagnose 23 faults originally using 262 symptoms of Unit-1 in the Ningde NPP of the China Guangdong Nuclear Power Corporation.The results show that the FDT,with GA-optimized symptoms and diagnosis strategy,can drive the construction of DUCG and lower the computational burden without loss of accuracy in diagnosis.
Optimization of a dynamic uncertain causality graph for fault diagnosis in nuclear power plant
Institute of Scientific and Technical Information of China (English)
Yue Zhao; Francesco Di Maio; Enrico Zio; Qin Zhang; Chun-Ling Dong; Jin-Ying Zhang
2017-01-01
Fault diagnostics is important for safe operation of nuclear power plants (NPPs).In recent years,data-driven approaches have been proposed and implemented to tackle the problem,e.g.,neural networks,fuzzy and neurofuzzy approaches,support vector machine,K-nearest neighbor classifiers and inference methodologies.Among these methods,dynamic uncertain causality graph (DUCG) has been proved effective in many practical cases.However,the causal graph construction behind the DUCG is complicate and,in many cases,results redundant on the symptoms needed to correctly classify the fault.In this paper,we propose a method to simplify causal graph construction in an automatic way.The method consists in transforming the expert knowledge-based DCUG into a fuzzy decision tree (FDT) by extracting from the DUCG a fuzzy rule base that resumes the used symptoms at the basis of the FDT.Genetic algorithm (GA) is,then,used for the optimization of the FDT,by performing a wrapper search around the FDT:the set of symptoms selected during the iterative search are taken as the best set of symptoms for the diagnosis of the faults that can occur in the system.The effectiveness of the approach is shown with respect to a DUCG model initially built to diagnose 23 faults originally using 262 symptoms of Unit-1 in the Ningde NPP of the China Guangdong Nuclear Power Corporation.The results show that the FDT,with GA-optimized symptoms and diagnosis strategy,can drive the construction of DUCG and lower the computational burden without loss of accuracy in diagnosis.
Directory of Open Access Journals (Sweden)
Sasipa Pojanavatee
2014-12-01
Full Text Available The existing literature finds conflicting results on the magnitude of price linkages between equity mutual funds and the stock market. The study contends that in an optimal lagged model, the expectations of future prices using knowledge of past price behaviour in a particular equity mutual fund category will improve forecasts of prices of other equity mutual fund categories and the stock market index. The evidence shows that the long-run pricing of equity mutual funds is cointegrated with the stock market index. In the short run, the results indicate that some equity mutual fund categories possess both long-run and short-run exogeneity with the stock market. Therefore, the short-run dynamic indicates short-run Granger causal links running between different equity mutual fund categories.
A hierarchical causal modeling for large industrial plants supervision
International Nuclear Information System (INIS)
Dziopa, P.; Leyval, L.
1994-01-01
A supervision system has to analyse the process current state and the way it will evolve after a modification of the inputs or disturbance. It is proposed to base this analysis on a hierarchy of models, witch differ by the number of involved variables and the abstraction level used to describe their temporal evolution. In a first step, special attention is paid to causal models building, from the most abstract one. Once the hierarchy of models has been build, the most detailed model parameters are estimated. Several models of different abstraction levels can be used for on line prediction. These methods have been applied to a nuclear reprocessing plant. The abstraction level could be chosen on line by the operator. Moreover when an abnormal process behaviour is detected a more detailed model is automatically triggered in order to focus the operator attention on the suspected subsystem. (authors). 11 refs., 11 figs
Optimal causal inference: estimating stored information and approximating causal architecture.
Still, Susanne; Crutchfield, James P; Ellison, Christopher J
2010-09-01
We introduce an approach to inferring the causal architecture of stochastic dynamical systems that extends rate-distortion theory to use causal shielding--a natural principle of learning. We study two distinct cases of causal inference: optimal causal filtering and optimal causal estimation. Filtering corresponds to the ideal case in which the probability distribution of measurement sequences is known, giving a principled method to approximate a system's causal structure at a desired level of representation. We show that in the limit in which a model-complexity constraint is relaxed, filtering finds the exact causal architecture of a stochastic dynamical system, known as the causal-state partition. From this, one can estimate the amount of historical information the process stores. More generally, causal filtering finds a graded model-complexity hierarchy of approximations to the causal architecture. Abrupt changes in the hierarchy, as a function of approximation, capture distinct scales of structural organization. For nonideal cases with finite data, we show how the correct number of the underlying causal states can be found by optimal causal estimation. A previously derived model-complexity control term allows us to correct for the effect of statistical fluctuations in probability estimates and thereby avoid overfitting.
Extracting neuronal functional network dynamics via adaptive Granger causality analysis.
Sheikhattar, Alireza; Miran, Sina; Liu, Ji; Fritz, Jonathan B; Shamma, Shihab A; Kanold, Patrick O; Babadi, Behtash
2018-04-24
Quantifying the functional relations between the nodes in a network based on local observations is a key challenge in studying complex systems. Most existing time series analysis techniques for this purpose provide static estimates of the network properties, pertain to stationary Gaussian data, or do not take into account the ubiquitous sparsity in the underlying functional networks. When applied to spike recordings from neuronal ensembles undergoing rapid task-dependent dynamics, they thus hinder a precise statistical characterization of the dynamic neuronal functional networks underlying adaptive behavior. We develop a dynamic estimation and inference paradigm for extracting functional neuronal network dynamics in the sense of Granger, by integrating techniques from adaptive filtering, compressed sensing, point process theory, and high-dimensional statistics. We demonstrate the utility of our proposed paradigm through theoretical analysis, algorithm development, and application to synthetic and real data. Application of our techniques to two-photon Ca 2+ imaging experiments from the mouse auditory cortex reveals unique features of the functional neuronal network structures underlying spontaneous activity at unprecedented spatiotemporal resolution. Our analysis of simultaneous recordings from the ferret auditory and prefrontal cortical areas suggests evidence for the role of rapid top-down and bottom-up functional dynamics across these areas involved in robust attentive behavior.
A Causal Model of Consumer-Based Brand Equity
Directory of Open Access Journals (Sweden)
Szőcs Attila
2015-12-01
Full Text Available Branding literature suggests that consumer-based brand equity (CBBE is a multidimensional construct. Starting from this approach and developing a conceptual multidimensional model, this study finds that CBBE can be best modelled with a two-dimensional structure and claims that it achieves this result by choosing the theoretically based causal specification. On the contrary, with reflective specification, one will be able to fit almost any valid construct because of the halo effect and common method bias. In the final model, Trust (in quality and Advantage are causing the second-order Brand Equity. The two-dimensional brand equity is an intuitive model easy to interpret and easy to measure, which thus may be a much more attractive means for the management as well.
Causal Inference and Model Selection in Complex Settings
Zhao, Shandong
Propensity score methods have become a part of the standard toolkit for applied researchers who wish to ascertain causal effects from observational data. While they were originally developed for binary treatments, several researchers have proposed generalizations of the propensity score methodology for non-binary treatment regimes. In this article, we firstly review three main methods that generalize propensity scores in this direction, namely, inverse propensity weighting (IPW), the propensity function (P-FUNCTION), and the generalized propensity score (GPS), along with recent extensions of the GPS that aim to improve its robustness. We compare the assumptions, theoretical properties, and empirical performance of these methods. We propose three new methods that provide robust causal estimation based on the P-FUNCTION and GPS. While our proposed P-FUNCTION-based estimator preforms well, we generally advise caution in that all available methods can be biased by model misspecification and extrapolation. In a related line of research, we consider adjustment for posttreatment covariates in causal inference. Even in a randomized experiment, observations might have different compliance performance under treatment and control assignment. This posttreatment covariate cannot be adjusted using standard statistical methods. We review the principal stratification framework which allows for modeling this effect as part of its Bayesian hierarchical models. We generalize the current model to add the possibility of adjusting for pretreatment covariates. We also propose a new estimator of the average treatment effect over the entire population. In a third line of research, we discuss the spectral line detection problem in high energy astrophysics. We carefully review how this problem can be statistically formulated as a precise hypothesis test with point null hypothesis, why a usual likelihood ratio test does not apply for problem of this nature, and a doable fix to correctly
Exploring causal networks of bovine milk fatty acids in a multivariate mixed model context
DEFF Research Database (Denmark)
Bouwman, Aniek C; Valente, Bruno D; Janss, Luc L G
2014-01-01
Knowledge regarding causal relationships among traits is important to understand complex biological systems. Structural equation models (SEM) can be used to quantify the causal relations between traits, which allow prediction of outcomes to interventions applied to such a network. Such models...... are fitted conditionally on a causal structure among traits, represented by a directed acyclic graph and an Inductive Causation (IC) algorithm can be used to search for causal structures. The aim of this study was to explore the space of causal structures involving bovine milk fatty acids and to select...
A developmental approach to learning causal models for cyber security
Mugan, Jonathan
2013-05-01
To keep pace with our adversaries, we must expand the scope of machine learning and reasoning to address the breadth of possible attacks. One approach is to employ an algorithm to learn a set of causal models that describes the entire cyber network and each host end node. Such a learning algorithm would run continuously on the system and monitor activity in real time. With a set of causal models, the algorithm could anticipate novel attacks, take actions to thwart them, and predict the second-order effects flood of information, and the algorithm would have to determine which streams of that flood were relevant in which situations. This paper will present the results of efforts toward the application of a developmental learning algorithm to the problem of cyber security. The algorithm is modeled on the principles of human developmental learning and is designed to allow an agent to learn about the computer system in which it resides through active exploration. Children are flexible learners who acquire knowledge by actively exploring their environment and making predictions about what they will find,1, 2 and our algorithm is inspired by the work of the developmental psychologist Jean Piaget.3 Piaget described how children construct knowledge in stages and learn new concepts on top of those they already know. Developmental learning allows our algorithm to focus on subsets of the environment that are most helpful for learning given its current knowledge. In experiments, the algorithm was able to learn the conditions for file exfiltration and use that knowledge to protect sensitive files.
Explaining quantum correlations through evolution of causal models
Harper, Robin; Chapman, Robert J.; Ferrie, Christopher; Granade, Christopher; Kueng, Richard; Naoumenko, Daniel; Flammia, Steven T.; Peruzzo, Alberto
2017-04-01
We propose a framework for the systematic and quantitative generalization of Bell's theorem using causal networks. We first consider the multiobjective optimization problem of matching observed data while minimizing the causal effect of nonlocal variables and prove an inequality for the optimal region that both strengthens and generalizes Bell's theorem. To solve the optimization problem (rather than simply bound it), we develop a genetic algorithm treating as individuals causal networks. By applying our algorithm to a photonic Bell experiment, we demonstrate the trade-off between the quantitative relaxation of one or more local causality assumptions and the ability of data to match quantum correlations.
Iturria-Medina, Yasser; Carbonell, Félix M; Sotero, Roberto C; Chouinard-Decorte, Francois; Evans, Alan C
2017-05-15
Generative models focused on multifactorial causal mechanisms in brain disorders are scarce and generally based on limited data. Despite the biological importance of the multiple interacting processes, their effects remain poorly characterized from an integrative analytic perspective. Here, we propose a spatiotemporal multifactorial causal model (MCM) of brain (dis)organization and therapeutic intervention that accounts for local causal interactions, effects propagation via physical brain networks, cognitive alterations, and identification of optimum therapeutic interventions. In this article, we focus on describing the model and applying it at the population-based level for studying late onset Alzheimer's disease (LOAD). By interrelating six different neuroimaging modalities and cognitive measurements, this model accurately predicts spatiotemporal alterations in brain amyloid-β (Aβ) burden, glucose metabolism, vascular flow, resting state functional activity, structural properties, and cognitive integrity. The results suggest that a vascular dysregulation may be the most-likely initial pathologic event leading to LOAD. Nevertheless, they also suggest that LOAD it is not caused by a unique dominant biological factor (e.g. vascular or Aβ) but by the complex interplay among multiple relevant direct interactions. Furthermore, using theoretical control analysis of the identified population-based multifactorial causal network, we show the crucial advantage of using combinatorial over single-target treatments, explain why one-target Aβ based therapies might fail to improve clinical outcomes, and propose an efficiency ranking of possible LOAD interventions. Although still requiring further validation at the individual level, this work presents the first analytic framework for dynamic multifactorial brain (dis)organization that may explain both the pathologic evolution of progressive neurological disorders and operationalize the influence of multiple interventional
DEFF Research Database (Denmark)
Borregaard, Michael K.; Matthews, Thomas J.; Whittaker, Robert James
2016-01-01
Aim: Island biogeography focuses on understanding the processes that underlie a set of well-described patterns on islands, but it lacks a unified theoretical framework for integrating these processes. The recently proposed general dynamic model (GDM) of oceanic island biogeography offers a step...... towards this goal. Here, we present an analysis of causality within the GDM and investigate its potential for the further development of island biogeographical theory. Further, we extend the GDM to include subduction-based island arcs and continental fragment islands. Location: A conceptual analysis...... of evolutionary processes in simulations derived from the mechanistic assumptions of the GDM corresponded broadly to those initially suggested, with the exception of trends in extinction rates. Expanding the model to incorporate different scenarios of island ontogeny and isolation revealed a sensitivity...
Wolfinger, Donna M.
The purpose of this research was to determine whether the young child's understanding of physical causality is affected by school science instruction. Sixty-four subjects, four and one-half through seven years of age, received 300 min of instruction designed to affect the subject's conception of causality as reflected in animism and dynamism. Instruction took place for 30 min per day on ten successive school days. Pretesting was done to allow a stratified random sample to be based on vocabulary level and developmental stage as well as on age and gender. Post-testing consisted of testing of developmental level and level within the causal relations of animism and dynamism. Significant differences (1.05 level) were found between the experimental and control groups for animism. Within the experimental group, males differed significantly (1.001 level) from females. The elimination of animism appeared to have occurred. For dynamism, significant differences (0.05 level) were found only between concrete operational subjects in the experimental and control groups, indicating a concrete level of operations was necessary if dynamism was to be affected. However, a review of interview protocols indicated that subjects classified as nonanimistic had learned to apply a definition rather than to think in a nonanimistic manner.
Causal Effect Inference with Deep Latent-Variable Models
Louizos, C; Shalit, U.; Mooij, J.; Sontag, D.; Zemel, R.; Welling, M.
2017-01-01
Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers. The most important aspect of inferring causal effects from observational data is the handling of
Cause and Event: Supporting Causal Claims through Logistic Models
O'Connell, Ann A.; Gray, DeLeon L.
2011-01-01
Efforts to identify and support credible causal claims have received intense interest in the research community, particularly over the past few decades. In this paper, we focus on the use of statistical procedures designed to support causal claims for a treatment or intervention when the response variable of interest is dichotomous. We identify…
Situation models and memory: the effects of temporal and causal information on recall sequence.
Brownstein, Aaron L; Read, Stephen J
2007-10-01
Participants watched an episode of the television show Cheers on video and then reported free recall. Recall sequence followed the sequence of events in the story; if one concept was observed immediately after another, it was recalled immediately after it. We also made a causal network of the show's story and found that recall sequence followed causal links; effects were recalled immediately after their causes. Recall sequence was more likely to follow causal links than temporal sequence, and most likely to follow causal links that were temporally sequential. Results were similar at 10-minute and 1-week delayed recall. This is the most direct and detailed evidence reported on sequential effects in recall. The causal network also predicted probability of recall; concepts with more links and concepts on the main causal chain were most likely to be recalled. This extends the causal network model to more complex materials than previous research.
Climate Modeling and Causal Identification for Sea Ice Predictability
Energy Technology Data Exchange (ETDEWEB)
Hunke, Elizabeth Clare [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Urrego Blanco, Jorge Rolando [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Urban, Nathan Mark [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2018-02-12
This project aims to better understand causes of ongoing changes in the Arctic climate system, particularly as decreasing sea ice trends have been observed in recent decades and are expected to continue in the future. As part of the Sea Ice Prediction Network, a multi-agency effort to improve sea ice prediction products on seasonal-to-interannual time scales, our team is studying sensitivity of sea ice to a collection of physical process and feedback mechanism in the coupled climate system. During 2017 we completed a set of climate model simulations using the fully coupled ACME-HiLAT model. The simulations consisted of experiments in which cloud, sea ice, and air-ocean turbulent exchange parameters previously identified as important for driving output uncertainty in climate models were perturbed to account for parameter uncertainty in simulated climate variables. We conducted a sensitivity study to these parameters, which built upon a previous study we made for standalone simulations (Urrego-Blanco et al., 2016, 2017). Using the results from the ensemble of coupled simulations, we are examining robust relationships between climate variables that emerge across the experiments. We are also using causal discovery techniques to identify interaction pathways among climate variables which can help identify physical mechanisms and provide guidance in predictability studies. This work further builds on and leverages the large ensemble of standalone sea ice simulations produced in our previous w14_seaice project.
Matsumoto, Atsushi; Kakigi, Ryusuke
2014-01-01
Recent neuroimaging experiments have revealed that subliminal priming of a target stimulus leads to the reduction of neural activity in specific regions concerned with processing the target. Such findings lead to questions about the degree to which the subliminal priming effect is based only on decreased activity in specific local brain regions, as opposed to the influence of neural mechanisms that regulate communication between brain regions. To address this question, this study recorded EEG during performance of a subliminal semantic priming task. We adopted an information-based approach that used independent component analysis and multivariate autoregressive modeling. Results indicated that subliminal semantic priming caused significant modulation of alpha band activity in the left inferior frontal cortex and modulation of gamma band activity in the left inferior temporal regions. The multivariate autoregressive approach confirmed significant increases in information flow from the inferior frontal cortex to inferior temporal regions in the early time window that was induced by subliminal priming. In the later time window, significant enhancement of bidirectional causal flow between these two regions underlying subliminal priming was observed. Results suggest that unconscious processing of words influences not only local activity of individual brain regions but also the dynamics of neural communication between those regions.
Kenzie, Erin S; Parks, Elle L; Bigler, Erin D; Wright, David W; Lim, Miranda M; Chesnutt, James C; Hawryluk, Gregory W J; Gordon, Wayne; Wakeland, Wayne
2018-01-01
Despite increasing public awareness and a growing body of literature on the subject of concussion, or mild traumatic brain injury, an urgent need still exists for reliable diagnostic measures, clinical care guidelines, and effective treatments for the condition. Complexity and heterogeneity complicate research efforts and indicate the need for innovative approaches to synthesize current knowledge in order to improve clinical outcomes. Methods from the interdisciplinary field of systems science, including models of complex systems, have been increasingly applied to biomedical applications and show promise for generating insight for traumatic brain injury. The current study uses causal-loop diagramming to visualize relationships between factors influencing the pathophysiology and recovery trajectories of concussive injury, including persistence of symptoms and deficits. The primary output is a series of preliminary systems maps detailing feedback loops, intrinsic dynamics, exogenous drivers, and hubs across several scales, from micro-level cellular processes to social influences. Key system features, such as the role of specific restorative feedback processes and cross-scale connections, are examined and discussed in the context of recovery trajectories. This systems approach integrates research findings across disciplines and allows components to be considered in relation to larger system influences, which enables the identification of research gaps, supports classification efforts, and provides a framework for interdisciplinary collaboration and communication-all strides that would benefit diagnosis, prognosis, and treatment in the clinic.
Directory of Open Access Journals (Sweden)
Erin S. Kenzie
2018-04-01
Full Text Available Despite increasing public awareness and a growing body of literature on the subject of concussion, or mild traumatic brain injury, an urgent need still exists for reliable diagnostic measures, clinical care guidelines, and effective treatments for the condition. Complexity and heterogeneity complicate research efforts and indicate the need for innovative approaches to synthesize current knowledge in order to improve clinical outcomes. Methods from the interdisciplinary field of systems science, including models of complex systems, have been increasingly applied to biomedical applications and show promise for generating insight for traumatic brain injury. The current study uses causal-loop diagramming to visualize relationships between factors influencing the pathophysiology and recovery trajectories of concussive injury, including persistence of symptoms and deficits. The primary output is a series of preliminary systems maps detailing feedback loops, intrinsic dynamics, exogenous drivers, and hubs across several scales, from micro-level cellular processes to social influences. Key system features, such as the role of specific restorative feedback processes and cross-scale connections, are examined and discussed in the context of recovery trajectories. This systems approach integrates research findings across disciplines and allows components to be considered in relation to larger system influences, which enables the identification of research gaps, supports classification efforts, and provides a framework for interdisciplinary collaboration and communication—all strides that would benefit diagnosis, prognosis, and treatment in the clinic.
Causal knowledge promotes behavioral self-regulation: An example using climate change dynamics.
Directory of Open Access Journals (Sweden)
David K Sewell
Full Text Available Adopting successful climate change mitigation policies requires the public to choose how to balance the sometimes competing goals of managing CO2 emissions and achieving economic growth. It follows that collective action on climate change depends on members of the public to be knowledgeable of the causes and economic ramifications of climate change. The existing literature, however, shows that people often struggle to correctly reason about the fundamental accumulation dynamics that drive climate change. Previous research has focused on using analogy to improve people's reasoning about accumulation, which has been met with some success. However, these existing studies have neglected the role economic factors might play in shaping people's decisions in relation to climate change. Here, we introduce a novel iterated decision task in which people attempt to achieve a specific economic goal by interacting with a causal dynamic system in which human economic activities, CO2 emissions, and warming are all causally interrelated. We show that when the causal links between these factors are highlighted, people's ability to achieve the economic goal of the task is enhanced in a way that approaches optimal responding, and avoids dangerous levels of warming.
Reconstructing Constructivism: Causal Models, Bayesian Learning Mechanisms, and the Theory Theory
Gopnik, Alison; Wellman, Henry M.
2012-01-01
We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework…
Scaling analyses of the spectral dimension in 3-dimensional causal dynamical triangulations
Cooperman, Joshua H.
2018-05-01
The spectral dimension measures the dimensionality of a space as witnessed by a diffusing random walker. Within the causal dynamical triangulations approach to the quantization of gravity (Ambjørn et al 2000 Phys. Rev. Lett. 85 347, 2001 Nucl. Phys. B 610 347, 1998 Nucl. Phys. B 536 407), the spectral dimension exhibits novel scale-dependent dynamics: reducing towards a value near 2 on sufficiently small scales, matching closely the topological dimension on intermediate scales, and decaying in the presence of positive curvature on sufficiently large scales (Ambjørn et al 2005 Phys. Rev. Lett. 95 171301, Ambjørn et al 2005 Phys. Rev. D 72 064014, Benedetti and Henson 2009 Phys. Rev. D 80 124036, Cooperman 2014 Phys. Rev. D 90 124053, Cooperman et al 2017 Class. Quantum Grav. 34 115008, Coumbe and Jurkiewicz 2015 J. High Energy Phys. JHEP03(2015)151, Kommu 2012 Class. Quantum Grav. 29 105003). I report the first comprehensive scaling analysis of the small-to-intermediate scale spectral dimension for the test case of the causal dynamical triangulations of 3-dimensional Einstein gravity. I find that the spectral dimension scales trivially with the diffusion constant. I find that the spectral dimension is completely finite in the infinite volume limit, and I argue that its maximal value is exactly consistent with the topological dimension of 3 in this limit. I find that the spectral dimension reduces further towards a value near 2 as this case’s bare coupling approaches its phase transition, and I present evidence against the conjecture that the bare coupling simply sets the overall scale of the quantum geometry (Ambjørn et al 2001 Phys. Rev. D 64 044011). On the basis of these findings, I advance a tentative physical explanation for the dynamical reduction of the spectral dimension observed within causal dynamical triangulations: branched polymeric quantum geometry on sufficiently small scales. My analyses should facilitate attempts to employ the spectral
The causal pie model: an epidemiological method applied to evolutionary biology and ecology.
Wensink, Maarten; Westendorp, Rudi G J; Baudisch, Annette
2014-05-01
A general concept for thinking about causality facilitates swift comprehension of results, and the vocabulary that belongs to the concept is instrumental in cross-disciplinary communication. The causal pie model has fulfilled this role in epidemiology and could be of similar value in evolutionary biology and ecology. In the causal pie model, outcomes result from sufficient causes. Each sufficient cause is made up of a "causal pie" of "component causes". Several different causal pies may exist for the same outcome. If and only if all component causes of a sufficient cause are present, that is, a causal pie is complete, does the outcome occur. The effect of a component cause hence depends on the presence of the other component causes that constitute some causal pie. Because all component causes are equally and fully causative for the outcome, the sum of causes for some outcome exceeds 100%. The causal pie model provides a way of thinking that maps into a number of recurrent themes in evolutionary biology and ecology: It charts when component causes have an effect and are subject to natural selection, and how component causes affect selection on other component causes; which partitions of outcomes with respect to causes are feasible and useful; and how to view the composition of a(n apparently homogeneous) population. The diversity of specific results that is directly understood from the causal pie model is a test for both the validity and the applicability of the model. The causal pie model provides a common language in which results across disciplines can be communicated and serves as a template along which future causal analyses can be made.
Testing a Model of Diabetes Self-Care Management: A Causal Model Analysis with LISREL.
Nowacek, George A.; And Others
1990-01-01
A diabetes-management model is presented, which includes an attitudinal element and depicts relationships among causal elements. LISREL-VI was used to analyze data from 115 Type-I and 105 Type-II patients. The data did not closely fit the model. Results support the importance of the personal meaning of diabetes. (TJH)
Verification of temporal-causal network models by mathematical analysis
Directory of Open Access Journals (Sweden)
Jan Treur
2016-04-01
Full Text Available Abstract Usually dynamic properties of models can be analysed by conducting simulation experiments. But sometimes, as a kind of prediction properties can also be found by calculations in a mathematical manner, without performing simulations. Examples of properties that can be explored in such a manner are: whether some values for the variables exist for which no change occurs (stationary points or equilibria, and how such values may depend on the values of the parameters of the model and/or the initial values for the variables whether certain variables in the model converge to some limit value (equilibria and how this may depend on the values of the parameters of the model and/or the initial values for the variables whether or not certain variables will show monotonically increasing or decreasing values over time (monotonicity how fast a convergence to a limit value takes place (convergence speed whether situations occur in which no convergence takes place but in the end a specific sequence of values is repeated all the time (limit cycle Such properties found in an analytic mathematical manner can be used for verification of the model by checking them for the values observed in simulation experiments. If one of these properties is not fulfilled, then there will be some error in the implementation of the model. In this paper some methods to analyse such properties of dynamical models will be described and illustrated for the Hebbian learning model, and for dynamic connection strengths in social networks. The properties analysed by the methods discussed cover equilibria, increasing or decreasing trends, recurring patterns (limit cycles, and speed of convergence to equilibria.
Causal reasoning and models of cognitive tasks for naval nuclear power plant operators
International Nuclear Information System (INIS)
Salazar-Ferrer, P.
1995-06-01
In complex industrial process control, causal reasoning appears as a major component in operators' cognitive tasks. It is tightly linked to diagnosis, prediction of normal and failure states, and explanation. This work provides a detailed review of literature in causal reasoning. A synthesis is proposed as a model of causal reasoning in process control. This model integrates distinct approaches in Cognitive Science: especially qualitative physics, Bayesian networks, knowledge-based systems, and cognitive psychology. Our model defines a framework for the analysis of causal human errors in simulated naval nuclear power plant fault management. Through the methodological framework of critical incident analysis we define a classification of errors and difficulties linked to causal reasoning. This classification is based on shallow characteristics of causal reasoning. As an origin of these errors, more elementary component activities in causal reasoning are identified. The applications cover the field of functional specification for man-machine interfaces, operators support systems design as well as nuclear safety. In addition of this study, we integrate the model of causal reasoning in a model of cognitive task in process control. (authors). 106 refs., 49 figs., 8 tabs
Dynamic causal models of neural system dynamics: current state ...
Indian Academy of Sciences (India)
Prakash
2006-09-28
Sep 28, 2006 ... The Boolean nature of θ, i.e. the pattern of absent and present connections, and the ..... statistical inference at the group level, various options exist. The simplest approach is to enter the ... the group level as well (M Garrido, J M Kilner, S J Kiebel, K. E Stephan and K J Friston, unpublished results). Fitted to ...
Causal Agency Theory: Reconceptualizing a Functional Model of Self-Determination
Shogren, Karrie A.; Wehmeyer, Michael L.; Palmer, Susan B.; Forber-Pratt, Anjali J.; Little, Todd J.; Lopez, Shane
2015-01-01
This paper introduces Causal Agency Theory, an extension of the functional model of self-determination. Causal Agency Theory addresses the need for interventions and assessments pertaining to selfdetermination for all students and incorporates the significant advances in understanding of disability and in the field of positive psychology since the…
From Ordinary Differential Equations to Structural Causal Models: the deterministic case
Mooij, J.M.; Janzing, D.; Schölkopf, B.; Nicholson, A.; Smyth, P.
2013-01-01
We show how, and under which conditions, the equilibrium states of a first-order Ordinary Differential Equation (ODE) system can be described with a deterministic Structural Causal Model (SCM). Our exposition sheds more light on the concept of causality as expressed within the framework of
Causal and causally separable processes
Oreshkov, Ognyan; Giarmatzi, Christina
2016-09-01
The idea that events are equipped with a partial causal order is central to our understanding of physics in the tested regimes: given two pointlike events A and B, either A is in the causal past of B, B is in the causal past of A, or A and B are space-like separated. Operationally, the meaning of these order relations corresponds to constraints on the possible correlations between experiments performed in the vicinities of the respective events: if A is in the causal past of B, an experimenter at A could signal to an experimenter at B but not the other way around, while if A and B are space-like separated, no signaling is possible in either direction. In the context of a concrete physical theory, the correlations compatible with a given causal configuration may obey further constraints. For instance, space-like correlations in quantum mechanics arise from local measurements on joint quantum states, while time-like correlations are established via quantum channels. Similarly to other variables, however, the causal order of a set of events could be random, and little is understood about the constraints that causality implies in this case. A main difficulty concerns the fact that the order of events can now generally depend on the operations performed at the locations of these events, since, for instance, an operation at A could influence the order in which B and C occur in A’s future. So far, no formal theory of causality compatible with such dynamical causal order has been developed. Apart from being of fundamental interest in the context of inferring causal relations, such a theory is imperative for understanding recent suggestions that the causal order of events in quantum mechanics can be indefinite. Here, we develop such a theory in the general multipartite case. Starting from a background-independent definition of causality, we derive an iteratively formulated canonical decomposition of multipartite causal correlations. For a fixed number of settings and
Causal and causally separable processes
International Nuclear Information System (INIS)
Oreshkov, Ognyan; Giarmatzi, Christina
2016-01-01
The idea that events are equipped with a partial causal order is central to our understanding of physics in the tested regimes: given two pointlike events A and B , either A is in the causal past of B , B is in the causal past of A , or A and B are space-like separated. Operationally, the meaning of these order relations corresponds to constraints on the possible correlations between experiments performed in the vicinities of the respective events: if A is in the causal past of B , an experimenter at A could signal to an experimenter at B but not the other way around, while if A and B are space-like separated, no signaling is possible in either direction. In the context of a concrete physical theory, the correlations compatible with a given causal configuration may obey further constraints. For instance, space-like correlations in quantum mechanics arise from local measurements on joint quantum states, while time-like correlations are established via quantum channels. Similarly to other variables, however, the causal order of a set of events could be random, and little is understood about the constraints that causality implies in this case. A main difficulty concerns the fact that the order of events can now generally depend on the operations performed at the locations of these events, since, for instance, an operation at A could influence the order in which B and C occur in A ’s future. So far, no formal theory of causality compatible with such dynamical causal order has been developed. Apart from being of fundamental interest in the context of inferring causal relations, such a theory is imperative for understanding recent suggestions that the causal order of events in quantum mechanics can be indefinite. Here, we develop such a theory in the general multipartite case. Starting from a background-independent definition of causality, we derive an iteratively formulated canonical decomposition of multipartite causal correlations. For a fixed number of settings and
DEFF Research Database (Denmark)
Kuhnert, Barbara; Lindner, Felix; Bentzen, Martin Mose
We introduce causal agency models as a modeling technique for representing and reasoning about ethical dilemmas. We find that ethical dilemmas, although they look similar on the surface, have very different causal structures. Based on their structural properties, as identified by the causal agency...... models, we cluster a set of dilemmas in Type 1 and Type 2 dilemmas. We observe that for Type 2 dilemmas but not for Type 1 dilemmas a utilitarian action dominates the possibility of refraining from action. Hence, we hypothesize, based on the model, that Type 2 dilemmas are perceived as less difficult...
A Bayesian Nonparametric Causal Model for Regression Discontinuity Designs
Karabatsos, George; Walker, Stephen G.
2013-01-01
The regression discontinuity (RD) design (Thistlewaite & Campbell, 1960; Cook, 2008) provides a framework to identify and estimate causal effects from a non-randomized design. Each subject of a RD design is assigned to the treatment (versus assignment to a non-treatment) whenever her/his observed value of the assignment variable equals or…
Causal Indicator Models Have Nothing to Do with Measurement
Howell, Roy D.; Breivik, Einar
2016-01-01
In this article, Roy Howell, and Einar Breivik, congratulate Aguirre-Urreta, M. I., Rönkkö, M., & Marakas, G. M., for their work (2016) "Omission of Causal Indicators: Consequences and Implications for Measurement," Measurement: Interdisciplinary Research and Perspectives, 14(3), 75-97. doi:10.1080/15366367.2016.1205935. They call it…
Revisiting Aristotle's causality: model for development in Nigeria ...
African Journals Online (AJOL)
Thus, the development equation must be balanced. Aristotle‟s theory of causality balances the equation. Since Aristotle has no theory of development therefore every individual, nation or industry in pursuit of development should seek to drive economic growth and human capital development together rather than focus on ...
Modeling aspects of wave kinematics in offshore structures dynamics
International Nuclear Information System (INIS)
Spanos, P.D.; Ghanem, R.; Bhattacharjee, S.
1993-01-01
Magnitude and phase related issues of modeling of ocean wave kinematics are addressed. Causal and non-causal filters are examined. It is shown that if for a particular ocean engineering problem only the magnitude representation of wave spectra spatial relation is critical, analog filters can be quite useful models in conjunction with the technique of statistical linearization, for calculating dynamic analyses. This is illustrated by considering the dynamic response of a simple model of a guyed tower
Boué, Stéphanie; Talikka, Marja; Westra, Jurjen Willem; Hayes, William; Di Fabio, Anselmo; Park, Jennifer; Schlage, Walter K; Sewer, Alain; Fields, Brett; Ansari, Sam; Martin, Florian; Veljkovic, Emilija; Kenney, Renee; Peitsch, Manuel C; Hoeng, Julia
2015-01-01
With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed. Moreover, networks may be downloaded for further visualization and evaluation. Database URL: http://causalbionet.com © The Author(s) 2015. Published by Oxford University Press.
On the causal dynamics between emissions, nuclear energy, renewable energy, and economic growth
International Nuclear Information System (INIS)
Apergis, Nicholas; Payne, James E.; Menyah, Kojo; Wolde-Rufael, Yemane
2010-01-01
This paper examines the causal relationship between CO 2 emissions, nuclear energy consumption, renewable energy consumption, and economic growth for a group of 19 developed and developing countries for the period 1984-2007 using a panel error correction model. The long-run estimates indicate that there is a statistically significant negative association between nuclear energy consumption and emissions, but a statistically significant positive relationship between emissions and renewable energy consumption. The results from the panel Granger causality tests suggest that in the short-run nuclear energy consumption plays an important role in reducing CO 2 emissions whereas renewable energy consumption does not contribute to reductions in emissions. This may be due to the lack of adequate storage technology to overcome intermittent supply problems as a result electricity producers have to rely on emission generating energy sources to meet peak load demand. (author)
On the causal dynamics between emissions, nuclear energy, renewable energy, and economic growth
Energy Technology Data Exchange (ETDEWEB)
Apergis, Nicholas [Department of Banking and Financial Management, University of Piraeus, Karaoli and Dimitriou 80, Piraeus, ATTIKI 18534 (Greece); Payne, James E. [Department of Economics, Illinois State University, Normal, IL 61790-4200 (United States); Menyah, Kojo [London Metropolitan Business School, London Metropolitan University, 84 Moorgate, London, EC2M 6SQ (United Kingdom); Wolde-Rufael, Yemane
2010-09-15
This paper examines the causal relationship between CO{sub 2} emissions, nuclear energy consumption, renewable energy consumption, and economic growth for a group of 19 developed and developing countries for the period 1984-2007 using a panel error correction model. The long-run estimates indicate that there is a statistically significant negative association between nuclear energy consumption and emissions, but a statistically significant positive relationship between emissions and renewable energy consumption. The results from the panel Granger causality tests suggest that in the short-run nuclear energy consumption plays an important role in reducing CO{sub 2} emissions whereas renewable energy consumption does not contribute to reductions in emissions. This may be due to the lack of adequate storage technology to overcome intermittent supply problems as a result electricity producers have to rely on emission generating energy sources to meet peak load demand. (author)
Dynamics of charged viscous dissipative cylindrical collapse with full causal approach
Energy Technology Data Exchange (ETDEWEB)
Shah, S.M.; Abbas, G. [The Islamia University of Bahawalpur, Department of Mathematics, Bahawalpur (Pakistan)
2017-11-15
The aim of this paper is to investigate the dynamical aspects of a charged viscous cylindrical source by using the Misner approach. To this end, we have considered the more general charged dissipative fluid enclosed by the cylindrical symmetric spacetime. The dissipative nature of the source is due to the presence of dissipative variables in the stress-energy tensor. The dynamical equations resulting from such charged cylindrical dissipative source have been coupled with the causal transport equations for heat flux, shear and bulk viscosity, in the context of the Israel-Steward theory. In this case, we have the considered Israel-Steward transportation equations without excluding the thermodynamics viscous/heat coupling coefficients. The results are compared with the previous works in which such coefficients were excluded and viscosity variables do not satisfy the casual transportation equations. (orig.)
Jajcay, N.; Kravtsov, S.; Tsonis, A.; Palus, M.
2017-12-01
A better understanding of dynamics in complex systems, such as the Earth's climate is one of the key challenges for contemporary science and society. A large amount of experimental data requires new mathematical and computational approaches. Natural complex systems vary on many temporal and spatial scales, often exhibiting recurring patterns and quasi-oscillatory phenomena. The statistical inference of causal interactions and synchronization between dynamical phenomena evolving on different temporal scales is of vital importance for better understanding of underlying mechanisms and a key for modeling and prediction of such systems. This study introduces and applies information theory diagnostics to phase and amplitude time series of different wavelet components of the observed data that characterizes El Niño. A suite of significant interactions between processes operating on different time scales was detected, and intermittent synchronization among different time scales has been associated with the extreme El Niño events. The mechanisms of these nonlinear interactions were further studied in conceptual low-order and state-of-the-art dynamical, as well as statistical climate models. Observed and simulated interactions exhibit substantial discrepancies, whose understanding may be the key to an improved prediction. Moreover, the statistical framework which we apply here is suitable for direct usage of inferring cross-scale interactions in nonlinear time series from complex systems such as the terrestrial magnetosphere, solar-terrestrial interactions, seismic activity or even human brain dynamics.
Classical Causal Models for Bell and Kochen-Specker Inequality Violations Require Fine-Tuning
Directory of Open Access Journals (Sweden)
Eric G. Cavalcanti
2018-04-01
Full Text Available Nonlocality and contextuality are at the root of conceptual puzzles in quantum mechanics, and they are key resources for quantum advantage in information-processing tasks. Bell nonlocality is best understood as the incompatibility between quantum correlations and the classical theory of causality, applied to relativistic causal structure. Contextuality, on the other hand, is on a more controversial foundation. In this work, I provide a common conceptual ground between nonlocality and contextuality as violations of classical causality. First, I show that Bell inequalities can be derived solely from the assumptions of no signaling and no fine-tuning of the causal model. This removes two extra assumptions from a recent result from Wood and Spekkens and, remarkably, does not require any assumption related to independence of measurement settings—unlike all other derivations of Bell inequalities. I then introduce a formalism to represent contextuality scenarios within causal models and show that all classical causal models for violations of a Kochen-Specker inequality require fine-tuning. Thus, the quantum violation of classical causality goes beyond the case of spacelike-separated systems and already manifests in scenarios involving single systems.
Directory of Open Access Journals (Sweden)
Lina Zgaga
Full Text Available Vitamin D deficiency has been associated with increased risk of colorectal cancer (CRC, but causal relationship has not yet been confirmed. We investigate the direction of causation between vitamin D and CRC by extending the conventional approaches to allow pleiotropic relationships and by explicitly modelling unmeasured confounders.Plasma 25-hydroxyvitamin D (25-OHD, genetic variants associated with 25-OHD and CRC, and other relevant information was available for 2645 individuals (1057 CRC cases and 1588 controls and included in the model. We investigate whether 25-OHD is likely to be causally associated with CRC, or vice versa, by selecting the best modelling hypothesis according to Bayesian predictive scores. We examine consistency for a range of prior assumptions.Model comparison showed preference for the causal association between low 25-OHD and CRC over the reverse causal hypothesis. This was confirmed for posterior mean deviances obtained for both models (11.5 natural log units in favour of the causal model, and also for deviance information criteria (DIC computed for a range of prior distributions. Overall, models ignoring hidden confounding or pleiotropy had significantly poorer DIC scores.Results suggest causal association between 25-OHD and colorectal cancer, and support the need for randomised clinical trials for further confirmations.
Reconstructing constructivism: Causal models, Bayesian learning mechanisms and the theory theory
Gopnik, Alison; Wellman, Henry M.
2012-01-01
We propose a new version of the “theory theory” grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and non-technical way, and review an extensive but ...
Challenges to inferring causality from viral information dispersion in dynamic social networks
Ternovski, John
2014-06-01
Understanding the mechanism behind large-scale information dispersion through complex networks has important implications for a variety of industries ranging from cyber-security to public health. With the unprecedented availability of public data from online social networks (OSNs) and the low cost nature of most OSN outreach, randomized controlled experiments, the "gold standard" of causal inference methodologies, have been used with increasing regularity to study viral information dispersion. And while these studies have dramatically furthered our understanding of how information disseminates through social networks by isolating causal mechanisms, there are still major methodological concerns that need to be addressed in future research. This paper delineates why modern OSNs are markedly different from traditional sociological social networks and why these differences present unique challenges to experimentalists and data scientists. The dynamic nature of OSNs is particularly troublesome for researchers implementing experimental designs, so this paper identifies major sources of bias arising from network mutability and suggests strategies to circumvent and adjust for these biases. This paper also discusses the practical considerations of data quality and collection, which may adversely impact the efficiency of the estimator. The major experimental methodologies used in the current literature on virality are assessed at length, and their strengths and limits identified. Other, as-yetunsolved threats to the efficiency and unbiasedness of causal estimators--such as missing data--are also discussed. This paper integrates methodologies and learnings from a variety of fields under an experimental and data science framework in order to systematically consolidate and identify current methodological limitations of randomized controlled experiments conducted in OSNs.
Concurrency Models with Causality and Events as Psi-calculi
Directory of Open Access Journals (Sweden)
Håkon Normann
2014-10-01
Full Text Available Psi-calculi are a parametric framework for nominal calculi, where standard calculi are found as instances, like the pi-calculus, or the cryptographic spi-calculus and applied-pi. Psi-calculi have an interleaving operational semantics, with a strong foundation on the theory of nominal sets and process algebras. Much of the expressive power of psi-calculi comes from their logical part, i.e., assertions, conditions, and entailment, which are left quite open thus accommodating a wide range of logics. We are interested in how this expressiveness can deal with event-based models of concurrency. We thus take the popular prime event structures model and give an encoding into an instance of psi-calculi. We also take the recent and expressive model of Dynamic Condition Response Graphs (in which event structures are strictly included and give an encoding into another corresponding instance of psi-calculi. The encodings that we achieve look rather natural and intuitive. Additional results about these encodings give us more confidence in their correctness.
Causal dissipation and shock profiles in the relativistic fluid dynamics of pure radiation.
Freistühler, Heinrich; Temple, Blake
2014-06-08
CURRENT THEORIES OF DISSIPATION IN THE RELATIVISTIC REGIME SUFFER FROM ONE OF TWO DEFICITS: either their dissipation is not causal or no profiles for strong shock waves exist. This paper proposes a relativistic Navier-Stokes-Fourier-type viscosity and heat conduction tensor such that the resulting second-order system of partial differential equations for the fluid dynamics of pure radiation is symmetric hyperbolic. This system has causal dissipation as well as the property that all shock waves of arbitrary strength have smooth profiles. Entropy production is positive both on gradients near those of solutions to the dissipation-free equations and on gradients of shock profiles. This shows that the new dissipation stress tensor complies to leading order with the principles of thermodynamics. Whether higher order modifications of the ansatz are required to obtain full compatibility with the second law far from the zero-dissipation equilibrium is left to further investigations. The system has exactly three a priori free parameters χ , η , ζ , corresponding physically to heat conductivity, shear viscosity and bulk viscosity. If the bulk viscosity is zero (as is stated in the literature) and the total stress-energy tensor is trace free, the entire viscosity and heat conduction tensor is determined to within a constant factor.
How to Be Causal: Time, Spacetime and Spectra
Kinsler, Paul
2011-01-01
I explain a simple definition of causality in widespread use, and indicate how it links to the Kramers-Kronig relations. The specification of causality in terms of temporal differential equations then shows us the way to write down dynamical models so that their causal nature "in the sense used here" should be obvious to all. To extend existing…
Accelerating transition dynamics in city regions: A qualitative modeling perspective
P.J. Valkering (Pieter); Yücel, G. (Gönenç); Gebetsroither-Geringer, E. (Ernst); Markvica, K. (Karin); Meynaerts, E. (Erika); N. Frantzeskaki (Niki)
2017-01-01
textabstractIn this article, we take stock of the findings from conceptual and empirical work on the role of transition initiatives for accelerating transitions as input for modeling acceleration dynamics. We applied the qualitative modeling approach of causal loop diagrams to capture the dynamics
Bayesian nonparametric generative models for causal inference with missing at random covariates.
Roy, Jason; Lum, Kirsten J; Zeldow, Bret; Dworkin, Jordan D; Re, Vincent Lo; Daniels, Michael J
2018-03-26
We propose a general Bayesian nonparametric (BNP) approach to causal inference in the point treatment setting. The joint distribution of the observed data (outcome, treatment, and confounders) is modeled using an enriched Dirichlet process. The combination of the observed data model and causal assumptions allows us to identify any type of causal effect-differences, ratios, or quantile effects, either marginally or for subpopulations of interest. The proposed BNP model is well-suited for causal inference problems, as it does not require parametric assumptions about the distribution of confounders and naturally leads to a computationally efficient Gibbs sampling algorithm. By flexibly modeling the joint distribution, we are also able to impute (via data augmentation) values for missing covariates within the algorithm under an assumption of ignorable missingness, obviating the need to create separate imputed data sets. This approach for imputing the missing covariates has the additional advantage of guaranteeing congeniality between the imputation model and the analysis model, and because we use a BNP approach, parametric models are avoided for imputation. The performance of the method is assessed using simulation studies. The method is applied to data from a cohort study of human immunodeficiency virus/hepatitis C virus co-infected patients. © 2018, The International Biometric Society.
A 2D model of causal set quantum gravity: the emergence of the continuum
International Nuclear Information System (INIS)
Brightwell, Graham; Henson, Joe; Surya, Sumati
2008-01-01
Non-perturbative theories of quantum gravity inevitably include configurations that fail to resemble physically reasonable spacetimes at large scales. Often, these configurations are entropically dominant and pose an obstacle to obtaining the desired classical limit. We examine this 'entropy problem' in a model of causal set quantum gravity corresponding to a discretization of 2D spacetimes. Using results from the theory of partial orders we show that, in the large volume or continuum limit, its partition function is dominated by causal sets which approximate to a region of 2D Minkowski space. This model of causal set quantum gravity thus overcomes the entropy problem and predicts the emergence of a physically reasonable geometry
Frisch, Mathias
2014-01-01
Much has been written on the role of causal notions and causal reasoning in the so-called 'special sciences' and in common sense. But does causal reasoning also play a role in physics? Mathias Frisch argues that, contrary to what influential philosophical arguments purport to show, the answer is yes. Time-asymmetric causal structures are as integral a part of the representational toolkit of physics as a theory's dynamical equations. Frisch develops his argument partly through a critique of anti-causal arguments and partly through a detailed examination of actual examples of causal notions in physics, including causal principles invoked in linear response theory and in representations of radiation phenomena. Offering a new perspective on the nature of scientific theories and causal reasoning, this book will be of interest to professional philosophers, graduate students, and anyone interested in the role of causal thinking in science.
Capturing cognitive causal paths in human reliability analysis with Bayesian network models
International Nuclear Information System (INIS)
Zwirglmaier, Kilian; Straub, Daniel; Groth, Katrina M.
2017-01-01
reIn the last decade, Bayesian networks (BNs) have been identified as a powerful tool for human reliability analysis (HRA), with multiple advantages over traditional HRA methods. In this paper we illustrate how BNs can be used to include additional, qualitative causal paths to provide traceability. The proposed framework provides the foundation to resolve several needs frequently expressed by the HRA community. First, the developed extended BN structure reflects the causal paths found in cognitive psychology literature, thereby addressing the need for causal traceability and strong scientific basis in HRA. Secondly, the use of node reduction algorithms allows the BN to be condensed to a level of detail at which quantification is as straightforward as the techniques used in existing HRA. We illustrate the framework by developing a BN version of the critical data misperceived crew failure mode in the IDHEAS HRA method, which is currently under development at the US NRC . We illustrate how the model could be quantified with a combination of expert-probabilities and information from operator performance databases such as SACADA. This paper lays the foundations necessary to expand the cognitive and quantitative foundations of HRA. - Highlights: • A framework for building traceable BNs for HRA, based on cognitive causal paths. • A qualitative BN structure, directly showing these causal paths is developed. • Node reduction algorithms are used for making the BN structure quantifiable. • BN quantified through expert estimates and observed data (Bayesian updating). • The framework is illustrated for a crew failure mode of IDHEAS.
Futures Business Models for an IoT Enabled Healthcare Sector: A Causal Layered Analysis Perspective
Julius Francis Gomes; Sara Moqaddemerad
2016-01-01
Purpose: To facilitate futures business research by proposing a novel way to combine business models as a conceptual tool with futures research techniques. Design: A futures perspective is adopted to foresight business models of the Internet of Things (IoT) enabled healthcare sector by using business models as a futures business research tool. In doing so, business models is coupled with one of the most prominent foresight methodologies, Causal Layered Analysis (CLA). Qualitative analysis...
Causal Models for Mediation Analysis: An Introduction to Structural Mean Models.
Zheng, Cheng; Atkins, David C; Zhou, Xiao-Hua; Rhew, Isaac C
2015-01-01
Mediation analyses are critical to understanding why behavioral interventions work. To yield a causal interpretation, common mediation approaches must make an assumption of "sequential ignorability." The current article describes an alternative approach to causal mediation called structural mean models (SMMs). A specific SMM called a rank-preserving model (RPM) is introduced in the context of an applied example. Particular attention is given to the assumptions of both approaches to mediation. Applying both mediation approaches to the college student drinking data yield notable differences in the magnitude of effects. Simulated examples reveal instances in which the traditional approach can yield strongly biased results, whereas the RPM approach remains unbiased in these cases. At the same time, the RPM approach has its own assumptions that must be met for correct inference, such as the existence of a covariate that strongly moderates the effect of the intervention on the mediator and no unmeasured confounders that also serve as a moderator of the effect of the intervention or the mediator on the outcome. The RPM approach to mediation offers an alternative way to perform mediation analysis when there may be unmeasured confounders.
Ghanem, Bernard; Ahuja, Narendra
2013-01-01
This paper proposes the problem of modeling video sequences of dynamic swarms (DSs). We define a DS as a large layout of stochastically repetitive spatial configurations of dynamic objects (swarm elements) whose motions exhibit local spatiotemporal
The causal nexus between oil prices and equity market in the U.S.: A regime switching model
International Nuclear Information System (INIS)
Balcilar, Mehmet; Ozdemir, Zeynel Abidin
2013-01-01
The aim of this paper is to analyse the causal link between monthly oil futures price changes and a sub-grouping of S and P 500 stock index changes. The causal linkage between oil and stock markets is modelled using a vector autoregressive model with time-varying parameters so as to reflect changes in Granger causality over time. A Markov switching vector autoregressive (MS-VAR) model, in which causal link between the series is stochastic and governed by an unobservable Markov chain, is used for inferring time-varying causality. Although we do not find any lead–lag type Granger causality, the results based on the MS-VAR model clearly show that oil futures price has strong regime prediction power for a sub-grouping of S and P 500 stock index during various sub-periods in the sample, while there is a weak evidence for the regime prediction power of a sub-grouping of S and P 500 stock indexes. The regime-prediction non-causality tests on the MS-VAR model show that both variables are useful for making inference about the regime process and that the evidence on regime-prediction causality is primarily found in the equation describing a sub-grouping of S and P 500 stock market returns. The evidence from the conditional non-causality tests shows that past information on the other series fails to improve the one step ahead prediction for both oil futures and stock returns. - Highlights: • We analyse the causal links between oil futures price and a sub-grouping of S and P 500 index. • The causal links are modelled using a regime switching model. • We do not find any lead–lag type Granger causality between the series. • The results show that oil futures price has regime prediction power for a sub-grouping of S and P 500 stock index
Sex and Self-Control Theory: The Measures and Causal Model May Be Different
Higgins, George E.; Tewksbury, Richard
2006-01-01
This study examines the distribution differences across sexes in key measures of self-control theory and differences in a causal model. Using cross-sectional data from juveniles ("n" = 1,500), the study shows mean-level differences in many of the self-control, risky behavior, and delinquency measures. Structural equation modeling…
Hindsight Bias Doesn't Always Come Easy: Causal Models, Cognitive Effort, and Creeping Determinism
Nestler, Steffen; Blank, Hartmut; von Collani, Gernot
2008-01-01
Creeping determinism, a form of hindsight bias, refers to people's hindsight perceptions of events as being determined or inevitable. This article proposes, on the basis of a causal-model theory of creeping determinism, that the underlying processes are effortful, and hence creeping determinism should disappear when individuals lack the cognitive…
The impact of school leadership on school level factors: validation of a causal model
Krüger, M.L.; Witziers, B.; Sleegers, P.
2007-01-01
This study aims to contribute to a better understanding of the antecedents and effects of educational leadership, and of the influence of the principal's leadership on intervening and outcome variables. A path analysis was conducted to test and validate a causal model. The results show no direct or
Causal Modeling of Secondary Science Students' Intentions to Enroll in Physics.
Crawley, Frank E.; Black, Carolyn B.
1992-01-01
Reports a study using the causal modeling method to verify underlying causes of student interest in enrolling in physics as predicted by the theory of planned behavior. Families were identified as major referents in the social support system for physics enrollment. Course and extracurricular conflicts and fear of failure were primary beliefs…
Rehder, Bob
2017-01-01
This article assesses how people reason with categories whose features are related in causal cycles. Whereas models based on causal graphical models (CGMs) have enjoyed success modeling category-based judgments as well as a number of other cognitive phenomena, CGMs are only able to represent causal structures that are acyclic. A number of new…
Faes, Luca; Nollo, Giandomenico
2010-11-01
The Partial Directed Coherence (PDC) and its generalized formulation (gPDC) are popular tools for investigating, in the frequency domain, the concept of Granger causality among multivariate (MV) time series. PDC and gPDC are formalized in terms of the coefficients of an MV autoregressive (MVAR) model which describes only the lagged effects among the time series and forsakes instantaneous effects. However, instantaneous effects are known to affect linear parametric modeling, and are likely to occur in experimental time series. In this study, we investigate the impact on the assessment of frequency domain causality of excluding instantaneous effects from the model underlying PDC evaluation. Moreover, we propose the utilization of an extended MVAR model including both instantaneous and lagged effects. This model is used to assess PDC either in accordance with the definition of Granger causality when considering only lagged effects (iPDC), or with an extended form of causality, when we consider both instantaneous and lagged effects (ePDC). The approach is first evaluated on three theoretical examples of MVAR processes, which show that the presence of instantaneous correlations may produce misleading profiles of PDC and gPDC, while ePDC and iPDC derived from the extended model provide here a correct interpretation of extended and lagged causality. It is then applied to representative examples of cardiorespiratory and EEG MV time series. They suggest that ePDC and iPDC are better interpretable than PDC and gPDC in terms of the known cardiovascular and neural physiologies.
Discrete causal theory emergent spacetime and the causal metric hypothesis
Dribus, Benjamin F
2017-01-01
This book evaluates and suggests potentially critical improvements to causal set theory, one of the best-motivated approaches to the outstanding problems of fundamental physics. Spacetime structure is of central importance to physics beyond general relativity and the standard model. The causal metric hypothesis treats causal relations as the basis of this structure. The book develops the consequences of this hypothesis under the assumption of a fundamental scale, with smooth spacetime geometry viewed as emergent. This approach resembles causal set theory, but differs in important ways; for example, the relative viewpoint, emphasizing relations between pairs of events, and relationships between pairs of histories, is central. The book culminates in a dynamical law for quantum spacetime, derived via generalized path summation.
Dynamic Latent Classification Model
DEFF Research Database (Denmark)
Zhong, Shengtong; Martínez, Ana M.; Nielsen, Thomas Dyhre
as possible. Motivated by this problem setting, we propose a generative model for dynamic classification in continuous domains. At each time point the model can be seen as combining a naive Bayes model with a mixture of factor analyzers (FA). The latent variables of the FA are used to capture the dynamics...
Causal Analysis of Religious Violence, a Structural Equation Modeling Approach
Directory of Open Access Journals (Sweden)
M Munajat
2015-12-01
[Penelitian ini berusaha mengkaji sebab kekerasan keagamaan dengan menggunakan pendekatan Model Persamaan Struktur (SEM. Penelitian kuantitatif terdahulu dalam bidang gerakan sosial dan kekerasan politik menunjukkan bahwa setidaknya ada tiga faktor yang diduga kuat menjadi penyebab kekerasan kolektif, seperti kekerasan agama, yaitu: 1 semakin fundamentalis seseorang, maka ia akan semakin cenderung menyetujui pernggunaan cara kekerasan, 2 semakin rendah kepercayaan seseorang terhadap pemerintah, maka ia akan semakin menyetujui penggunaan kekerasan, 3 berbeda dengan pendapat ke-dua, hanya orang yang rendah kepercayaanya kepada pemerintah, namun mempunyai semangat politik tinggi, yang akan menyetujui penggunaan cara-cara kekerasan. Berdasarkan pada data yang diambil dari 343 responden dari para aktivis, Front Pembela Islam, Muhammadiyah dan Nahdlatul Ulama, penelitian ini mengkonfirmasi bahwa semakin fundamentalis seseorang, maka ia akan semakin cenderung menyetujui kekerasan, terlepas dari afiliasi organisasi mereka. Namun demikian, penelitian ini tidak mendukung hubungan antara kepercayaan terhadap pemerintah dan kekerasan. Demikian juga, hubungan antara kekerasan dan interaksi antara kepercayaan pemerintah dan semangat politik tidak dapat dibuktikan dari data dalam penelitian ini. Oleh karena itu, penelitian ini menyimpulkan bahwa fundamentalisme, sebagai salah satu bentuk keagamaan, merupakan faktor yang sangat penting dalam menjelaskan kekerasan keagamaan.
Reconstructing constructivism: causal models, Bayesian learning mechanisms, and the theory theory.
Gopnik, Alison; Wellman, Henry M
2012-11-01
We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and nontechnical way, and review an extensive but relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the world and through observations of the actions of others. Studies demonstrate these learning mechanisms in children from 16 months to 4 years old and include research on causal statistical learning, informal experimentation through play, and imitation and informal pedagogy. They also include studies of the variability and progressive character of intuitive theory change, particularly theory of mind. These studies investigate both the physical and the psychological and social domains. We conclude with suggestions for further collaborative projects between developmental and computational cognitive scientists.
International Nuclear Information System (INIS)
Lean, Hooi Hooi; Smyth, Russell
2010-01-01
This paper employs annual data from 1971 to 2006 to examine the causal relationship between aggregate output, electricity consumption, exports, labor and capital in a multivariate model for Malaysia. We find that there is bidirectional Granger causality running between aggregate output and electricity consumption. The policy implication of this result is that Malaysia should adopt the dual strategy of increasing investment in electricity infrastructure and stepping up electricity conservation policies to reduce unnecessary wastage of electricity, in order to avoid the negative effect of reducing electricity consumption on aggregate output. We also find support for the export-led hypothesis which states Granger causality runs from exports to aggregate output. This result is consistent with Malaysia pursuing a successful export-orientated strategy. (author)
Directory of Open Access Journals (Sweden)
Ioana RADU
2013-06-01
Full Text Available The paper tests and evaluates the causality between the dynamics of the Romanian mutual fund market and the economy. Using the Granger causality test, a regression analysis has been developed on quarterly data during 2004Q3 – 2012Q2 for the Romanian economy. Based on this relationship, we can emphasize that the controversial debate upon the economic growth and the mutual fund market has became a complex research subject. Therefore, due to its complexity, the timeliness and the continuous growth of the investment funds area, this paper complements the existing literature by identifying the causal linkage between the mutual fund market and the economy. The paper is organized as it follows. First part presents the main premises that have emphasized our research. Second part presents a brief literature review and extracts the studies that appreciate best the relationship between the analyzed variables. Next section is set on defining the potential correlation between the analyzed variables. Then, section 4 tests the causality by using the R facility. The last part concludes.
Nobuoki, Eshima; Minoru, Tabata; Geng, Zhi; Department of Medical Information Analysis, Faculty of Medicine, Oita Medical University; Department of Applied Mathematics, Faculty of Engineering, Kobe University; Department of Probability and Statistics, Peking University
2001-01-01
This paper discusses path analysis of categorical variables with logistic regression models. The total, direct and indirect effects in fully recursive causal systems are considered by using model parameters. These effects can be explained in terms of log odds ratios, uncertainty differences, and an inner product of explanatory variables and a response variable. A study on food choice of alligators as a numerical exampleis reanalysed to illustrate the present approach.
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Chaido Dritsaki
2014-04-01
Full Text Available Energy plays an important role in economic development worldwide. The increase of energy consumption showed that CO2 emissions in the atmosphere have increased dramatically, and these lead many scientists to push governments of the developing countries to take action for the formulation of environmental policies. Many studies have attempted to look for the direction of causality between energy consumption (EC, economic growth (GDP and CO2 emissions mainly on developing countries. This paper, therefore, applies the panel unit root tests, panel cointegration methods and panel causality test to investigate the relationship between energy consumption (EC, economic growth (GDP and CO2 emissions for three countries of Southern Europe (Greece, Spain, and Portugal covering the annual period 1960-2009. The FMOLS and DOLS are then used to estimate the long run relationship between the variables. The findings of this study reveal that there is a short-run bilateral causal link between the examined variables. However, in the long run, there is a unidirectional causality running from CO2 emissions to energy consumption (EC, and economic growth (GDP and a bilateral causality between energy consumption and economic growth. This indicates that energy is a force for economic growth both in short and long run as it is driven from economic growth. Moreover, to face the heterogeneity on the three countries of Southern Europe we use the FMOLS and DOLS estimation methods.
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Paolo Vineis
2017-06-01
Full Text Available Abstract In the last decades, Systems Biology (including cancer research has been driven by technology, statistical modelling and bioinformatics. In this paper we try to bring biological and philosophical thinking back. We thus aim at making different traditions of thought compatible: (a causality in epidemiology and in philosophical theorizing—notably, the “sufficient-component-cause framework” and the “mark transmission” approach; (b new acquisitions about disease pathogenesis, e.g. the “branched model” in cancer, and the role of biomarkers in this process; (c the burgeoning of omics research, with a large number of “signals” and of associations that need to be interpreted. In the paper we summarize first the current views on carcinogenesis, and then explore the relevance of current philosophical interpretations of “cancer causes”. We try to offer a unifying framework to incorporate biomarkers and omic data into causal models, referring to a position called “evidential pluralism”. According to this view, causal reasoning is based on both “evidence of difference-making” (e.g. associations and on “evidence of underlying biological mechanisms”. We conceptualize the way scientists detect and trace signals in terms of information transmission, which is a generalization of the mark transmission theory developed by philosopher Wesley Salmon. Our approach is capable of helping us conceptualize how heterogeneous factors such as micro and macro-biological and psycho-social—are causally linked. This is important not only to understand cancer etiology, but also to design public health policies that target the right causal factors at the macro-level.
International Nuclear Information System (INIS)
Kim, Do Hun; Mun, Tae Hun; Kim, Dong Hwan
1999-02-01
This book introduces systems thinking and conceptual tool and modeling tool of dynamics system such as tragedy of single thinking, accessible way of system dynamics, feedback structure and causal loop diagram analysis, basic of system dynamics modeling, causal loop diagram and system dynamics modeling, information delay modeling, discovery and application for policy, modeling of crisis of agricultural and stock breeding products, dynamic model and lesson in ecosystem, development and decadence of cites and innovation of education forward system thinking.
Directory of Open Access Journals (Sweden)
Yuanyuan Yu
2017-12-01
Full Text Available Abstract Background Confounders can produce spurious associations between exposure and outcome in observational studies. For majority of epidemiologists, adjusting for confounders using logistic regression model is their habitual method, though it has some problems in accuracy and precision. It is, therefore, important to highlight the problems of logistic regression and search the alternative method. Methods Four causal diagram models were defined to summarize confounding equivalence. Both theoretical proofs and simulation studies were performed to verify whether conditioning on different confounding equivalence sets had the same bias-reducing potential and then to select the optimum adjusting strategy, in which logistic regression model and inverse probability weighting based marginal structural model (IPW-based-MSM were compared. The “do-calculus” was used to calculate the true causal effect of exposure on outcome, then the bias and standard error were used to evaluate the performances of different strategies. Results Adjusting for different sets of confounding equivalence, as judged by identical Markov boundaries, produced different bias-reducing potential in the logistic regression model. For the sets satisfied G-admissibility, adjusting for the set including all the confounders reduced the equivalent bias to the one containing the parent nodes of the outcome, while the bias after adjusting for the parent nodes of exposure was not equivalent to them. In addition, all causal effect estimations through logistic regression were biased, although the estimation after adjusting for the parent nodes of exposure was nearest to the true causal effect. However, conditioning on different confounding equivalence sets had the same bias-reducing potential under IPW-based-MSM. Compared with logistic regression, the IPW-based-MSM could obtain unbiased causal effect estimation when the adjusted confounders satisfied G-admissibility and the optimal
Yu, Yuanyuan; Li, Hongkai; Sun, Xiaoru; Su, Ping; Wang, Tingting; Liu, Yi; Yuan, Zhongshang; Liu, Yanxun; Xue, Fuzhong
2017-12-28
Confounders can produce spurious associations between exposure and outcome in observational studies. For majority of epidemiologists, adjusting for confounders using logistic regression model is their habitual method, though it has some problems in accuracy and precision. It is, therefore, important to highlight the problems of logistic regression and search the alternative method. Four causal diagram models were defined to summarize confounding equivalence. Both theoretical proofs and simulation studies were performed to verify whether conditioning on different confounding equivalence sets had the same bias-reducing potential and then to select the optimum adjusting strategy, in which logistic regression model and inverse probability weighting based marginal structural model (IPW-based-MSM) were compared. The "do-calculus" was used to calculate the true causal effect of exposure on outcome, then the bias and standard error were used to evaluate the performances of different strategies. Adjusting for different sets of confounding equivalence, as judged by identical Markov boundaries, produced different bias-reducing potential in the logistic regression model. For the sets satisfied G-admissibility, adjusting for the set including all the confounders reduced the equivalent bias to the one containing the parent nodes of the outcome, while the bias after adjusting for the parent nodes of exposure was not equivalent to them. In addition, all causal effect estimations through logistic regression were biased, although the estimation after adjusting for the parent nodes of exposure was nearest to the true causal effect. However, conditioning on different confounding equivalence sets had the same bias-reducing potential under IPW-based-MSM. Compared with logistic regression, the IPW-based-MSM could obtain unbiased causal effect estimation when the adjusted confounders satisfied G-admissibility and the optimal strategy was to adjust for the parent nodes of outcome, which
The causal dynamics between coal consumption and growth: Evidence from emerging market economies
International Nuclear Information System (INIS)
Apergis, Nicholas; Payne, James E.
2010-01-01
This study examines the relationship between coal consumption and economic growth for 15 emerging market economies within a multivariate panel framework over the period 1980-2006. The heterogeneous panel cointegration results indicate there is a long-run equilibrium relationship between real GDP, coal consumption, real gross fixed capital formation, and the labor force. While in the long-run both real gross fixed capital formation and the labor force have a significant positive impact on real GDP, coal consumption has a significant negative impact. The panel causality tests show bidirectional causality between coal consumption and economic growth in both the short- and long-run. (author)
Development of face recognition: Dynamic causal modelling of MEG data
Wei He; Blake W. Johnson
2018-01-01
Electrophysiological studies of adults indicate that brain activity is enhanced during viewing of repeated faces, at a latency of about 250 ms after the onset of the face (M250/N250). The present study aimed to determine if this effect was also present in preschool-aged children, whose brain activity was measured in a custom-sized pediatric MEG system. The results showed that, unlike adults, face repetition did not show any significant modulation of M250 amplitude in children; however childre...
The Nonlinear Dynamic Relationship of Exchange Rates: Parametric and Nonparametric Causality testing
Bekiros, S.D.; Diks, C.
2007-01-01
The present study investigates the long-term linear and nonlinear causal linkages among six currencies, namely EUR/USD, GBP/USD, USD/JPY, USD/CHF, AUD/USD and USD/CAD. The prime motivation for choosing these exchange rates comes from the fact that they are the most liquid and widely traded, covering
International Nuclear Information System (INIS)
Ciarreta, A.; Zarraga, A.
2010-01-01
This paper applies recent panel methodology to investigate the long-run and causal relationship between electricity consumption and real GDP for a set of 12 European countries using annual data for the period 1970-2007. The sample countries have moved faster than other neighboring countries towards the creation of a single electricity market over the past 30 years. Energy prices are also included in the study due to their important role in affecting the above variables, thus avoiding the problem of omitted variable bias. Tests for panel unit roots, cointegration in heterogeneous panels and panel causality are employed in a trivariate VECM estimated by system GMM. The results show evidence of a long-run equilibrium relationship between the three series and a negative short-run and strong causality from electricity consumption to GDP. As expected, there is bidirectional causality between energy prices and GDP and weaker evidence between electricity consumption and energy prices. These results support the policies implemented towards the creation of a common European electricity market.
Models for Dynamic Applications
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Sales-Cruz, Mauricio; Morales Rodriguez, Ricardo; Heitzig, Martina
2011-01-01
This chapter covers aspects of the dynamic modelling and simulation of several complex operations that include a controlled blending tank, a direct methanol fuel cell that incorporates a multiscale model, a fluidised bed reactor, a standard chemical reactor and finally a polymerisation reactor...... be applied to formulate, analyse and solve these dynamic problems and how in the case of the fuel cell problem the model consists of coupledmeso and micro scale models. It is shown how data flows are handled between the models and how the solution is obtained within the modelling environment....
From patterns to causal understanding: Structural equation modeling (SEM) in soil ecology
Eisenhauer, Nico; Powell, Jeff R; Grace, James B.; Bowker, Matthew A.
2015-01-01
In this perspectives paper we highlight a heretofore underused statistical method in soil ecological research, structural equation modeling (SEM). SEM is commonly used in the general ecological literature to develop causal understanding from observational data, but has been more slowly adopted by soil ecologists. We provide some basic information on the many advantages and possibilities associated with using SEM and provide some examples of how SEM can be used by soil ecologists to shift focus from describing patterns to developing causal understanding and inspiring new types of experimental tests. SEM is a promising tool to aid the growth of soil ecology as a discipline, particularly by supporting research that is increasingly hypothesis-driven and interdisciplinary, thus shining light into the black box of interactions belowground.
Reininghaus, Ulrich; Depp, Colin A; Myin-Germeys, Inez
2016-03-01
Integrated models of psychotic disorders have posited a number of putative psychological mechanisms that may contribute to the development of psychotic symptoms, but it is only recently that a modest amount of experience sampling research has provided evidence on their role in daily life, outside the research laboratory. A number of methodological challenges remain in evaluating specificity of potential causal links between a given psychological mechanism and psychosis outcomes in a systematic fashion, capitalizing on longitudinal data to investigate temporal ordering. In this article, we argue for testing ecological interventionist causal models that draw on real world and real-time delivered, ecological momentary interventions for generating evidence on several causal criteria (association, time order, and direction/sole plausibility) under real-world conditions, while maximizing generalizability to social contexts and experiences in heterogeneous populations. Specifically, this approach tests whether ecological momentary interventions can (1) modify a putative mechanism and (2) produce changes in the mechanism that lead to sustainable changes in intended psychosis outcomes in individuals' daily lives. Future research using this approach will provide translational evidence on the active ingredients of mobile health and in-person interventions that promote sustained effectiveness of ecological momentary interventions and, thereby, contribute to ongoing efforts that seek to enhance effectiveness of psychological interventions under real-world conditions. © The Author 2015. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.
Reininghaus, Ulrich; Depp, Colin A.; Myin-Germeys, Inez
2016-01-01
Integrated models of psychotic disorders have posited a number of putative psychological mechanisms that may contribute to the development of psychotic symptoms, but it is only recently that a modest amount of experience sampling research has provided evidence on their role in daily life, outside the research laboratory. A number of methodological challenges remain in evaluating specificity of potential causal links between a given psychological mechanism and psychosis outcomes in a systematic fashion, capitalizing on longitudinal data to investigate temporal ordering. In this article, we argue for testing ecological interventionist causal models that draw on real world and real-time delivered, ecological momentary interventions for generating evidence on several causal criteria (association, time order, and direction/sole plausibility) under real-world conditions, while maximizing generalizability to social contexts and experiences in heterogeneous populations. Specifically, this approach tests whether ecological momentary interventions can (1) modify a putative mechanism and (2) produce changes in the mechanism that lead to sustainable changes in intended psychosis outcomes in individuals’ daily lives. Future research using this approach will provide translational evidence on the active ingredients of mobile health and in-person interventions that promote sustained effectiveness of ecological momentary interventions and, thereby, contribute to ongoing efforts that seek to enhance effectiveness of psychological interventions under real-world conditions. PMID:26707864
DEFF Research Database (Denmark)
Andreasen, Martin Møller; Meldrum, Andrew
This paper studies whether dynamic term structure models for US nominal bond yields should enforce the zero lower bound by a quadratic policy rate or a shadow rate specification. We address the question by estimating quadratic term structure models (QTSMs) and shadow rate models with at most four...
Modeling the Effect of Religion on Human Empathy Based on an Adaptive Temporal-Causal Network Model
van Ments, L.I.; Roelofsma, P.H.M.P.; Treur, J.
2018-01-01
Religion is a central aspect of many individuals’ lives around the world, and its influence on human behaviour has been extensively studied from many different perspectives. The current study integrates a number of these perspectives into one adaptive temporal-causal network model describing the
Toward a formalized account of attitudes: The Causal Attitude Network (CAN) model.
Dalege, Jonas; Borsboom, Denny; van Harreveld, Frenk; van den Berg, Helma; Conner, Mark; van der Maas, Han L J
2016-01-01
This article introduces the Causal Attitude Network (CAN) model, which conceptualizes attitudes as networks consisting of evaluative reactions and interactions between these reactions. Relevant evaluative reactions include beliefs, feelings, and behaviors toward the attitude object. Interactions between these reactions arise through direct causal influences (e.g., the belief that snakes are dangerous causes fear of snakes) and mechanisms that support evaluative consistency between related contents of evaluative reactions (e.g., people tend to align their belief that snakes are useful with their belief that snakes help maintain ecological balance). In the CAN model, the structure of attitude networks conforms to a small-world structure: evaluative reactions that are similar to each other form tight clusters, which are connected by a sparser set of "shortcuts" between them. We argue that the CAN model provides a realistic formalized measurement model of attitudes and therefore fills a crucial gap in the attitude literature. Furthermore, the CAN model provides testable predictions for the structure of attitudes and how they develop, remain stable, and change over time. Attitude strength is conceptualized in terms of the connectivity of attitude networks and we show that this provides a parsimonious account of the differences between strong and weak attitudes. We discuss the CAN model in relation to possible extensions, implication for the assessment of attitudes, and possibilities for further study. (c) 2015 APA, all rights reserved).
International Nuclear Information System (INIS)
Mensah, Justice Tei
2014-01-01
Following the recent global economic downturn, attention has gradually shifted towards emerging economies which have experienced robust growth amidst sluggish growth of the world economy. A significant number of these emerging economies are in Africa. Rising growth in these economies is associated with surging demand for energy to propel the engines of growth, with direct implications on emissions into the atmosphere. Further, these economies are constantly being shaped by series of structural reforms with direct and indirect effects on growth, demand for energy, etc. To this end, this paper examines the causal dynamics among energy use, real GDP and CO 2 emissions in the presence of regime shifts in six emerging African economies using the Gregory and Hansen (1996a). J. Econ. 70, 99–126 threshold cointegration and the Toda and Yamamoto (1995). J. Econometrics. 66, 225–250 Granger causality techniques. Results confirm the presence of regime shift effects in the long run inter-linkages among energy use, real GDP and CO 2 emissions in the countries considered, thus indicating that structural changes have both economic and environmental effects. Hence, integration of energy and environmental policies into development plans is imperative towards attaining sustainable growth and development. - Highlights: • The paper examines the causal dynamics among output, energy demand and carbon emissions in the presence of regime shifts. • Regime shift have significant effects on the nexus among energy use, real GDP and CO 2 emissions. • Results suggest that structural changes in selected countries have both economic and environmental effects. • Integration of energy and environmental policies into development plans is desirable
DYNAMICS OF MUTUAL FUNDS IN RELATION TO STOCK MARKET: A VECTOR AUTOREGRESSIVE CAUSALITY ANALYSIS
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Md. Shahadath Hossain
2013-01-01
Full Text Available In Bangladesh, primary and secondary mutual fund markets behave in a completely different way, where initial public offering (IPO investors of mutual funds earn more than 250 percent rerun, whereas secondary market investors cannot even manage to cover the opportunity cost of their investment. There are few other abnormalities present in this market – unlike everywhere in the world, most of the mutual funds are closed-end (92 percent and closed-end mutual funds are barred to issue bonus or right shares. A total of 714 day’s observations, from January 2008 to December 2010, of four variables– DSE (Dhaka Stock Exchange general index return, DSE general index turnover, mutual funds’ return and mutual funds’ turnover– are utilized. Stationarity of the variables are tested with Augmented Dickey-Fuller (ADF unit root test and found that variables are in different order of integration. Long-term equilibrium relationships among the variables are tested with Johansen cointegration and it is found that DSE general index return and mutual funds’ return are cointegrated. Toda-Yamamoto (TY version of granger non-causality test is employed and bidirectional causality is found moving from DSE (Dhaka Stock Exchange general index turnover to DSE general index return, whereas unidirectional causality is found moving from mutual fund’s return to DSE general index return, mutual funds’ return to mutual funds turnover, and DSE general index turnover to mutual funds turnover. This finding helps to conclude that equity shares’ demand drives the mutual funds demand but even higher demand of mutual funds fails to raise its own price unless underlying value of the mutual funds changes.
International Nuclear Information System (INIS)
Nishimura, Hiroshi.
1993-05-01
Object-Oriented Programming has been used extensively to model the LBL Advanced Light Source 1.5 GeV electron storage ring. This paper is on the present status of the class library construction with emphasis on a dynamic modeling
Bun, M.J.G.; Sarafidis, V.
2013-01-01
This Chapter reviews the recent literature on dynamic panel data models with a short time span and a large cross-section. Throughout the discussion we considerlinear models with additional endogenous covariates. First we give a broad overview of available inference methods placing emphasis on GMM.
Modelling the rand and commodity prices: A Granger causality and cointegration analysis
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Xolani Ndlovu
2014-11-01
Full Text Available This paper examines the ‘commodity currency’ hypothesis of the Rand, that is, the postulate that the currency moves in line with commodity prices, and analyses the associated causality using nominal data between 1996 and 2010. We address both the short run and long run relationship between commodity prices and exchange rates. We find that while the levels of the series of both assets are difference stationary, they are not cointegrated. Further, we find the two variables are negatively related, with strong and significant causality running from commodity prices to the exchange rate and not vice versa, implying exogeneity in the determination of commodity prices with respect to the nominal exchange rate. The strength of the relationship is significantly weaker than other OECD commodity currencies. We surmise that the relationship is dynamic over time owing to the portfolio-rebalance argument and the Commodity Terms of Trade (CTT effect and, in the absence of an error correction mechanism, this disconnect may be prolonged. For commodity and currency market participants, this implies that while futures and forward commodity prices may be useful leading indicators of future currency movements, the price risk management strategies may need to be recalibrated over time.
Ghanem, Bernard
2013-01-01
This paper proposes the problem of modeling video sequences of dynamic swarms (DSs). We define a DS as a large layout of stochastically repetitive spatial configurations of dynamic objects (swarm elements) whose motions exhibit local spatiotemporal interdependency and stationarity, i.e., the motions are similar in any small spatiotemporal neighborhood. Examples of DS abound in nature, e.g., herds of animals and flocks of birds. To capture the local spatiotemporal properties of the DS, we present a probabilistic model that learns both the spatial layout of swarm elements (based on low-level image segmentation) and their joint dynamics that are modeled as linear transformations. To this end, a spatiotemporal neighborhood is associated with each swarm element, in which local stationarity is enforced both spatially and temporally. We assume that the prior on the swarm dynamics is distributed according to an MRF in both space and time. Embedding this model in a MAP framework, we iterate between learning the spatial layout of the swarm and its dynamics. We learn the swarm transformations using ICM, which iterates between estimating these transformations and updating their distribution in the spatiotemporal neighborhoods. We demonstrate the validity of our method by conducting experiments on real and synthetic video sequences. Real sequences of birds, geese, robot swarms, and pedestrians evaluate the applicability of our model to real world data. © 2012 Elsevier Inc. All rights reserved.
A quantum causal discovery algorithm
Giarmatzi, Christina; Costa, Fabio
2018-03-01
Finding a causal model for a set of classical variables is now a well-established task—but what about the quantum equivalent? Even the notion of a quantum causal model is controversial. Here, we present a causal discovery algorithm for quantum systems. The input to the algorithm is a process matrix describing correlations between quantum events. Its output consists of different levels of information about the underlying causal model. Our algorithm determines whether the process is causally ordered by grouping the events into causally ordered non-signaling sets. It detects if all relevant common causes are included in the process, which we label Markovian, or alternatively if some causal relations are mediated through some external memory. For a Markovian process, it outputs a causal model, namely the causal relations and the corresponding mechanisms, represented as quantum states and channels. Our algorithm opens the route to more general quantum causal discovery methods.
Discrete dynamic modeling of cellular signaling networks.
Albert, Réka; Wang, Rui-Sheng
2009-01-01
Understanding signal transduction in cellular systems is a central issue in systems biology. Numerous experiments from different laboratories generate an abundance of individual components and causal interactions mediating environmental and developmental signals. However, for many signal transduction systems there is insufficient information on the overall structure and the molecular mechanisms involved in the signaling network. Moreover, lack of kinetic and temporal information makes it difficult to construct quantitative models of signal transduction pathways. Discrete dynamic modeling, combined with network analysis, provides an effective way to integrate fragmentary knowledge of regulatory interactions into a predictive mathematical model which is able to describe the time evolution of the system without the requirement for kinetic parameters. This chapter introduces the fundamental concepts of discrete dynamic modeling, particularly focusing on Boolean dynamic models. We describe this method step-by-step in the context of cellular signaling networks. Several variants of Boolean dynamic models including threshold Boolean networks and piecewise linear systems are also covered, followed by two examples of successful application of discrete dynamic modeling in cell biology.
Energy Technology Data Exchange (ETDEWEB)
Salazar-Ferrer, P
1995-06-01
In complex industrial process control, causal reasoning appears as a major component in operators` cognitive tasks. It is tightly linked to diagnosis, prediction of normal and failure states, and explanation. This work provides a detailed review of literature in causal reasoning. A synthesis is proposed as a model of causal reasoning in process control. This model integrates distinct approaches in Cognitive Science: especially qualitative physics, Bayesian networks, knowledge-based systems, and cognitive psychology. Our model defines a framework for the analysis of causal human errors in simulated naval nuclear power plant fault management. Through the methodological framework of critical incident analysis we define a classification of errors and difficulties linked to causal reasoning. This classification is based on shallow characteristics of causal reasoning. As an origin of these errors, more elementary component activities in causal reasoning are identified. The applications cover the field of functional specification for man-machine interfaces, operators support systems design as well as nuclear safety. In addition of this study, we integrate the model of causal reasoning in a model of cognitive task in process control. (authors). 106 refs., 49 figs., 8 tabs.
The Reactive-Causal Architecture: Introducing an Emotion Model along with Theories of Needs
Aydin, Ali Orhan; Orgun, Mehmet Ali
In the entertainment application area, one of the major aims is to develop believable agents. To achieve this aim, agents should be highly autonomous, situated, flexible, and display affect. The Reactive-Causal Architecture (ReCau) is proposed to simulate these core attributes. In its current form, ReCau cannot explain the effects of emotions on intelligent behaviour. This study aims is to further improve the emotion model of ReCau to explain the effects of emotions on intelligent behaviour. This improvement allows ReCau to be emotional to support the development of believable agents.
Modeling the Effect of Religion on Human Empathy Based on an Adaptive Temporal-Causal Network Model
van Ments, L.I.; Roelofsma, P.H.M.P.; Treur, J.
2018-01-01
Religion is a central aspect of many individuals’ lives around the world, and its influence on human behaviour has been extensively studied from many different perspectives. The current study integrates a number of these perspectives into one adaptive temporal-causal network model describing the mental states involved, their mutual relations, and the adaptation of some of these relations over time due to learning. By first developing a conceptual representation of a network model based on lit...
The relationship of family characteristics and bipolar disorder using causal-pie models.
Chen, Y-C; Kao, C-F; Lu, M-K; Yang, Y-K; Liao, S-C; Jang, F-L; Chen, W J; Lu, R-B; Kuo, P-H
2014-01-01
Many family characteristics were reported to increase the risk of bipolar disorder (BPD). The development of BPD may be mediated through different pathways, involving diverse risk factor profiles. We evaluated the associations of family characteristics to build influential causal-pie models to estimate their contributions on the risk of developing BPD at the population level. We recruited 329 clinically diagnosed BPD patients and 202 healthy controls to collect information in parental psychopathology, parent-child relationship, and conflict within family. Other than logistic regression models, we applied causal-pie models to identify pathways involved with different family factors for BPD. The risk of BPD was significantly increased with parental depression, neurosis, anxiety, paternal substance use problems, and poor relationship with parents. Having a depressed mother further predicted early onset of BPD. Additionally, a greater risk for BPD was observed with higher numbers of paternal/maternal psychopathologies. Three significant risk profiles were identified for BPD, including paternal substance use problems (73.0%), maternal depression (17.6%), and through poor relationship with parents and conflict within the family (6.3%). Our findings demonstrate that different aspects of family characteristics elicit negative impacts on bipolar illness, which can be utilized to target specific factors to design and employ efficient intervention programs. Copyright © 2013 Elsevier Masson SAS. All rights reserved.
Futures Business Models for an IoT Enabled Healthcare Sector: A Causal Layered Analysis Perspective
Directory of Open Access Journals (Sweden)
Julius Francis Gomes
2016-12-01
Full Text Available Purpose: To facilitate futures business research by proposing a novel way to combine business models as a conceptual tool with futures research techniques. Design: A futures perspective is adopted to foresight business models of the Internet of Things (IoT enabled healthcare sector by using business models as a futures business research tool. In doing so, business models is coupled with one of the most prominent foresight methodologies, Causal Layered Analysis (CLA. Qualitative analysis provides deeper understanding of the phenomenon through the layers of CLA; litany, social causes, worldview and myth. Findings: It is di cult to predict the far future for a technology oriented sector like healthcare. This paper presents three scenarios for short-, medium- and long-term future. Based on these scenarios we also present a set of business model elements for different future time frames. This paper shows a way to combine business models with CLA, a foresight methodology; in order to apply business models in futures business research. Besides offering early results for futures business research, this study proposes a conceptual space to work with individual business models for managerial stakeholders. Originality / Value: Much research on business models has offered conceptualization of the phenomenon, innovation through business model and transformation of business models. However, existing literature does not o er much on using business model as a futures research tool. Enabled by futures thinking, we collected key business model elements and building blocks for the futures market and ana- lyzed them through the CLA framework.
Synergy and redundancy in the Granger causal analysis of dynamical networks
International Nuclear Information System (INIS)
Stramaglia, Sebastiano; M Cortes, Jesus; Marinazzo, Daniele
2014-01-01
We analyze, by means of Granger causality (GC), the effect of synergy and redundancy in the inference (from time series data) of the information flow between subsystems of a complex network. While we show that fully conditioned GC (CGC) is not affected by synergy, the pairwise analysis fails to prove synergetic effects. In cases when the number of samples is low, thus making the fully conditioned approach unfeasible, we show that partially conditioned GC (PCGC) is an effective approach if the set of conditioning variables is properly chosen. Here we consider two different strategies (based either on informational content for the candidate driver or on selecting the variables with highest pairwise influences) for PCGC and show that, depending on the data structure, either one or the other might be equally valid. On the other hand, we observe that fully conditioned approaches do not work well in the presence of redundancy, thus suggesting the strategy of separating the pairwise links in two subsets: those corresponding to indirect connections of the CGC (which should thus be excluded) and links that can be ascribed to redundancy effects and, together with the results from the fully connected approach, provide a better description of the causality pattern in the presence of redundancy. Finally we apply these methods to two different real datasets. First, analyzing electrophysiological data from an epileptic brain, we show that synergetic effects are dominant just before seizure occurrences. Second, our analysis applied to gene expression time series from HeLa culture shows that the underlying regulatory networks are characterized by both redundancy and synergy. (paper)
Granger Causality Testing with Intensive Longitudinal Data.
Molenaar, Peter C M
2018-06-01
The availability of intensive longitudinal data obtained by means of ambulatory assessment opens up new prospects for prevention research in that it allows the derivation of subject-specific dynamic networks of interacting variables by means of vector autoregressive (VAR) modeling. The dynamic networks thus obtained can be subjected to Granger causality testing in order to identify causal relations among the observed time-dependent variables. VARs have two equivalent representations: standard and structural. Results obtained with Granger causality testing depend upon which representation is chosen, yet no criteria exist on which this important choice can be based. A new equivalent representation is introduced called hybrid VARs with which the best representation can be chosen in a data-driven way. Partial directed coherence, a frequency-domain statistic for Granger causality testing, is shown to perform optimally when based on hybrid VARs. An application to real data is provided.
International Nuclear Information System (INIS)
Buonomano, V.; Engel, A.
1974-10-01
Some speculations on a causal model that seems to provide a common conceptual foundation for Relativity Gravitation and Quantum Mechanics are presented. The present approach is a unifying of three theories. The first being the repulsive theory of gravitational forces first proposed by Lesage in the eighteenth century. The second of these theories is the Brownian Motion Theory of Quantum Mechanics or Stocastic Mechanics which treats the non-deterministic Nature of Quantum Mechanics as being due to a Brownian motion of all objects. This Brownian motion being caused by the statistical variation in the graviton flux. The above two theories are unified with the Causal Theory of Special Relativity. Within the present context, the time dilations (and other effects) of Relativity are explained by assuming that the rate of a clock is a function of the total number or intensity of gravitons and the average frequency or energy of the gravitons that the clock receives. The Special Theory would then be the special case of the General Theory where the intensity is constant but the average frequency varies. In all the previous it is necessary to assume a particular model of the creation of the universe, namely the Big Bang Theory. This assumption gives us the existence of a preferred reference frame, the frame in which the Big Bang explosion was at rest. The above concepts of graviton distribution and real time dilations become meaningful by assuming the Big Bang Theory along with this preferred frame. An experimental test is proposed
Malafeyev, O. A.; Nemnyugin, S. A.; Rylow, D.; Kolpak, E. P.; Awasthi, Achal
2017-07-01
The corruption dynamics is analyzed by means of the lattice model which is similar to the three-dimensional Ising model. Agents placed at nodes of the corrupt network periodically choose to perfom or not to perform the act of corruption at gain or loss while making decisions based on the process history. The gain value and its dynamics are defined by means of the Markov stochastic process modelling with parameters established in accordance with the influence of external and individual factors on the agent's gain. The model is formulated algorithmically and is studied by means of the computer simulation. Numerical results are obtained which demonstrate asymptotic behaviour of the corruption network under various conditions.
Polverino, Pierpaolo; Frisk, Erik; Jung, Daniel; Krysander, Mattias; Pianese, Cesare
2017-07-01
The present paper proposes an advanced approach for Polymer Electrolyte Membrane Fuel Cell (PEMFC) systems fault detection and isolation through a model-based diagnostic algorithm. The considered algorithm is developed upon a lumped parameter model simulating a whole PEMFC system oriented towards automotive applications. This model is inspired by other models available in the literature, with further attention to stack thermal dynamics and water management. The developed model is analysed by means of Structural Analysis, to identify the correlations among involved physical variables, defined equations and a set of faults which may occur in the system (related to both auxiliary components malfunctions and stack degradation phenomena). Residual generators are designed by means of Causal Computation analysis and the maximum theoretical fault isolability, achievable with a minimal number of installed sensors, is investigated. The achieved results proved the capability of the algorithm to theoretically detect and isolate almost all faults with the only use of stack voltage and temperature sensors, with significant advantages from an industrial point of view. The effective fault isolability is proved through fault simulations at a specific fault magnitude with an advanced residual evaluation technique, to consider quantitative residual deviations from normal conditions and achieve univocal fault isolation.
[Causal analysis approaches in epidemiology].
Dumas, O; Siroux, V; Le Moual, N; Varraso, R
2014-02-01
Epidemiological research is mostly based on observational studies. Whether such studies can provide evidence of causation remains discussed. Several causal analysis methods have been developed in epidemiology. This paper aims at presenting an overview of these methods: graphical models, path analysis and its extensions, and models based on the counterfactual approach, with a special emphasis on marginal structural models. Graphical approaches have been developed to allow synthetic representations of supposed causal relationships in a given problem. They serve as qualitative support in the study of causal relationships. The sufficient-component cause model has been developed to deal with the issue of multicausality raised by the emergence of chronic multifactorial diseases. Directed acyclic graphs are mostly used as a visual tool to identify possible confounding sources in a study. Structural equations models, the main extension of path analysis, combine a system of equations and a path diagram, representing a set of possible causal relationships. They allow quantifying direct and indirect effects in a general model in which several relationships can be tested simultaneously. Dynamic path analysis further takes into account the role of time. The counterfactual approach defines causality by comparing the observed event and the counterfactual event (the event that would have been observed if, contrary to the fact, the subject had received a different exposure than the one he actually received). This theoretical approach has shown limits of traditional methods to address some causality questions. In particular, in longitudinal studies, when there is time-varying confounding, classical methods (regressions) may be biased. Marginal structural models have been developed to address this issue. In conclusion, "causal models", though they were developed partly independently, are based on equivalent logical foundations. A crucial step in the application of these models is the
Louie, Jacob; Shalaby, Amer; Habib, Khandker Nurul
2017-01-01
Most investigations of incident-related delay duration in the transportation context are restricted to highway traffic, with little attention given to delays due to transit service disruptions. Studies of transit-based delay duration are also considerably less comprehensive than their highway counterparts with respect to examining the effects of non-causal variables on the delay duration. However, delays due to incidents in public transit service can have serious consequences on the overall urban transportation system due to the pivotal and vital role of public transit. The ability to predict the durations of various types of transit system incidents is indispensable for better management and mitigation of service disruptions. This paper presents a detailed investigation on incident delay durations in Toronto's subway system over the year 2013, focusing on the effects of the incidents' location and time, the train-type involved, and the non-adherence to proper recovery procedures. Accelerated Failure Time (AFT) hazard models are estimated to investigate the relationship between these factors and the resulting delay duration. The empirical investigation reveals that incident types that impact both safety and operations simultaneously generally have longer expected delays than incident types that impact either safety or operations alone. Incidents at interchange stations are cleared faster than incidents at non-interchange stations. Incidents during peak periods have nearly the same delay durations as off-peak incidents. The estimated models are believed to be useful tools in predicting the relative magnitude of incident delay duration for better management of subway operations. Copyright © 2016 Elsevier Ltd. All rights reserved.
Modelling the effect of religion on human empathy based on an adaptive temporal–causal network model
van Ments, Laila; Roelofsma, Peter; Treur, Jan
2018-01-01
Background Religion is a central aspect of many individuals’ lives around the world, and its influence on human behaviour has been extensively studied from many different perspectives. Methods The current study integrates a number of these perspectives into one adaptive temporal–causal network model describing the mental states involved, their mutual relations, and the adaptation of some of these relations over time due to learning. Results By first developing a conceptual representation of a...
Directory of Open Access Journals (Sweden)
Loktev Aleksey Alekseevich
2013-01-01
Full Text Available The authors present their findings associated with their modeling of a dynamic load damper. According to the authors, the damper is to be installed onto a structure or its element that may be exposed to impact, vibration or any other dynamic loading. The damper is composed of paralleled or consecutively connected viscous and elastic elements. The authors study the influence of viscosity and elasticity parameters of the damper produced onto the regular displacement of points of the structure to be protected and onto the regular acceleration transmitted immediately from the damper to the elements positioned below it.
Causal attributions of vineyard executives â A mental model study of vineyard managementâ
Directory of Open Access Journals (Sweden)
Martin FG Schaffernicht
2017-12-01
Full Text Available This article contributes a reference of causal attributions made by vineyard executives in Chile, where increasing costs and stagnating prices challenge the vineyardsâ profits. The investigation was motivated by the question how executives interpret the industry's mid term future and how they reflect on steering their companies. Based on in-depth interviews, causal maps were elaborated to represent the executivesâ mental models. These are represented as sequences of attributions, connecting variables by causal links. It was found that some mental models guide policies bound to increase the prices, whereas other models suggest taking the prices as givens and control costs. The collection of causal attributions of the vineyard executives (CAVE has been made publicly available. As a result, CAVE can be used by other management scholars to elicit other executivesâ mental models and increase the data base available. Since such research will be cumulative, a minimum size for meaningful statistical analysis can be reached, opening up an avenue for improving the design of business policies. CAVE can also serve executives and consultants in constructing causal argumentations and business policies. Future research and development of supporting software are called for. Keywords: Mental models, Strategy, Business model
Kramers-Kronig relations and causality conditions for graphene in the framework of the Dirac model
Klimchitskaya, G. L.; Mostepanenko, V. M.
2018-04-01
We analyze the concept of causality for the conductivity of graphene described by the Dirac model. It is recalled that the condition of causality leads to the analyticity of conductivity in the upper half-plane of complex frequencies and to the standard symmetry properties for its real and imaginary parts. This results in the Kramers-Kronig relations, which explicit form depends on whether the conductivity has no pole at zero frequency (as in the case of zero temperature when the band gap of graphene is larger than twice the chemical potential) or it has a pole (as in all other cases, specifically, at nonzero temperature). Through the direct analytic calculation it is shown that the real and imaginary parts of graphene conductivity, found recently on the basis of first principles of thermal quantum field theory using the polarization tensor in (2 +1 )-dimensional space-time, satisfy the Kramers-Kronig relations precisely. In so doing, the values of two integrals in the commonly used tables, which are also important for a wider area of dispersion relations in quantum field theory and elementary particle physics, are corrected. The obtained results are not of only fundamental theoretical character, but can be used as a guideline in testing the validity of different phenomenological approaches and for the interpretation of experimental data.
Repeated causal decision making.
Hagmayer, York; Meder, Björn
2013-01-01
Many of our decisions refer to actions that have a causal impact on the external environment. Such actions may not only allow for the mere learning of expected values or utilities but also for acquiring knowledge about the causal structure of our world. We used a repeated decision-making paradigm to examine what kind of knowledge people acquire in such situations and how they use their knowledge to adapt to changes in the decision context. Our studies show that decision makers' behavior is strongly contingent on their causal beliefs and that people exploit their causal knowledge to assess the consequences of changes in the decision problem. A high consistency between hypotheses about causal structure, causally expected values, and actual choices was observed. The experiments show that (a) existing causal hypotheses guide the interpretation of decision feedback, (b) consequences of decisions are used to revise existing causal beliefs, and (c) decision makers use the experienced feedback to induce a causal model of the choice situation even when they have no initial causal hypotheses, which (d) enables them to adapt their choices to changes of the decision problem. (PsycINFO Database Record (c) 2013 APA, all rights reserved).
Hoțoiu, Maria; Tavella, Federico; Treur, Jan
2018-01-01
This paper presents a computational network model for a person with a Borderline Personality Disorder. It was designed according to a Network-Oriented Modeling approach as a temporal-causal network based on neuropsychological background knowledge. Some example simulations are discussed. The model
Comparison of phase space dynamics of Kopenhagen and causal interpretations of quantum mechanics
Energy Technology Data Exchange (ETDEWEB)
Tempel, Christoph; Schleich, Wolfgang P. [Institut fuer Quantenphysik, Universitaet Ulm, D-89069 Ulm (Germany)
2013-07-01
Recent publications pursue the attempt to reconstruct Bohm trajectories experimentally utilizing the technique of weak measurements. We study the phase space dynamics of a specific double slit setup in terms of the Bohm de-Broglie formulation of quantum mechanics. We want to compare the results of those Bohmian phase space dynamics to the usual quantum mechanical phase space formulation with the Wigner function as a quasi probability density.
Causal modeling of secondary science students' intentions to enroll in physics
Crawley, Frank E.; Black, Carolyn B.
The purpose of this study was to explore the utility of the theory of planned behavior model developed by social psychologists for understanding and predicting the behavioral intentions of secondary science students regarding enrolling in physics. In particular, the study used a three-stage causal model to investigate the links from external variables to behavioral, normative, and control beliefs; from beliefs to attitudes, subjective norm, and perceived behavioral control; and from attitudes, subjective norm, and perceived behavioral control to behavioral intentions. The causal modeling method was employed to verify the underlying causes of secondary science students' interest in enrolling physics as predicted in the theory of planned behavior. Data were collected from secondary science students (N = 264) residing in a central Texas city who were enrolled in earth science (8th grade), biology (9th grade), physical science (10th grade), or chemistry (11th grade) courses. Cause-and-effect relationships were analyzed using path analysis to test the direct effects of model variables specified in the theory of planned behavior. Results of this study indicated that students' intention to enroll in a high school physics course was determined by their attitude toward enrollment and their degree of perceived behavioral control. Attitude, subjective norm, and perceived behavioral control were, in turn, formed as a result of specific beliefs that students held about enrolling in physics. Grade level and career goals were found to be instrumental in shaping students' attitude. Immediate family members were identified as major referents in the social support system for enrolling in physics. Course and extracurricular conflicts and the fear of failure were shown to be the primary beliefs obstructing students' perception of control over physics enrollment. Specific recommendations are offered to researchers and practitioners for strengthening secondary school students
Dynamic wake meandering modeling
Energy Technology Data Exchange (ETDEWEB)
Larsen, Gunner C.; Aagaard Madsen, H.; Bingoel, F. (and others)
2007-06-15
We present a consistent, physically based theory for the wake meandering phenomenon, which we consider of crucial importance for the overall description of wind turbine loadings in wind farms. In its present version the model is confined to single wake situations. The model philosophy does, however, have the potential to include also mutual wake interaction phenomenons. The basic conjecture behind the dynamic wake meandering model is that wake transportation in the atmospheric boundary layer is driven by the large scale lateral- and vertical turbulence components. Based on this conjecture a stochastic model of the downstream wake meandering is formulated. In addition to the kinematic formulation of the dynamics of the 'meandering frame of reference', models characterizing the mean wake deficit as well as the added wake turbulence, described in the meandering frame of reference, are an integrated part the model complex. For design applications, the computational efficiency of wake deficit prediction is a key issue. Two computationally low cost models are developed for this purpose. The character of the added wake turbulence, generated by the up-stream turbine in the form of shed and trailed vorticity, has been approached by analytical as well as by numerical studies. The dynamic wake meandering philosophy has been verified by comparing model predictions with extensive full-scale measurements. These comparisons have demonstrated good agreement, both qualitatively and quantitatively, concerning both flow characteristics and turbine load characteristics. Contrary to previous attempts to model wake loading, the dynamic wake meandering approach opens for a unifying description in the sense that turbine power and load aspects can be treated simultaneously. This capability is a direct and attractive consequence of the model being based on the underlying physical process, and it potentially opens for optimization of wind farm topology, of wind farm operation as
International Nuclear Information System (INIS)
Colanero, K.; Chu, M.-C.
2002-01-01
We study a dynamical chiral bag model, in which massless fermions are confined within an impenetrable but movable bag coupled to meson fields. The self-consistent motion of the bag is obtained by solving the equations of motion exactly assuming spherical symmetry. When the bag interacts with an external meson wave we find three different kinds of resonances: fermionic, geometric, and σ resonances. We discuss the phenomenological implications of our results
Model for macroevolutionary dynamics.
Maruvka, Yosef E; Shnerb, Nadav M; Kessler, David A; Ricklefs, Robert E
2013-07-02
The highly skewed distribution of species among genera, although challenging to macroevolutionists, provides an opportunity to understand the dynamics of diversification, including species formation, extinction, and morphological evolution. Early models were based on either the work by Yule [Yule GU (1925) Philos Trans R Soc Lond B Biol Sci 213:21-87], which neglects extinction, or a simple birth-death (speciation-extinction) process. Here, we extend the more recent development of a generic, neutral speciation-extinction (of species)-origination (of genera; SEO) model for macroevolutionary dynamics of taxon diversification. Simulations show that deviations from the homogeneity assumptions in the model can be detected in species-per-genus distributions. The SEO model fits observed species-per-genus distributions well for class-to-kingdom-sized taxonomic groups. The model's predictions for the appearance times (the time of the first existing species) of the taxonomic groups also approximately match estimates based on molecular inference and fossil records. Unlike estimates based on analyses of phylogenetic reconstruction, fitted extinction rates for large clades are close to speciation rates, consistent with high rates of species turnover and the relatively slow change in diversity observed in the fossil record. Finally, the SEO model generally supports the consistency of generic boundaries based on morphological differences between species and provides a comparator for rates of lineage splitting and morphological evolution.
Three Cs in Measurement Models: Causal Indicators, Composite Indicators, and Covariates
Bollen, Kenneth A.; Bauldry, Shawn
2011-01-01
In the last two decades attention to causal (and formative) indicators has grown. Accompanying this growth has been the belief that we can classify indicators into two categories, effect (reflective) indicators and causal (formative) indicators. This paper argues that the dichotomous view is too simple. Instead, there are effect indicators and three types of variables on which a latent variable depends: causal indicators, composite (formative) indicators, and covariates (the “three Cs”). Caus...
International Nuclear Information System (INIS)
McFadden, J.H.; Paulsen, M.P.; Gose, G.C.
1981-01-01
Thermal-hydraulic codes in general use for system calculations are based on extensive analyses of loss-of-coolant accidents following the postulated rupture of a large coolant pipe. In this study, time-dependent equation for the slip velocity in a two-phase flow condition has been incorporated into the RETRAN-02 computer code. This model addition was undertaken to remove a limitation in RETRAN-01 associated with the homogeneous equilibrium mixture model. The dynamic slip equation was derived from a set of two-fluid conservation equations. 18 refs
Infertile individuals' marital relationship status, happiness, and mental health: a causal model.
Ahmadi Forooshany, Seyed Habiballah; Yazdkhasti, Fariba; Safari Hajataghaie, Saiede; Nasr Esfahani, Mohammad Hossein
2014-10-01
This study examined the causal model of relation between marital relation- ship status, happiness, and mental health in infertile individuals. In this descriptive study, 155 subjects (men: 52 and women: 78), who had been visited in one of the infertility Centers, voluntarily participated in a self-evaluation. Golombok Rust Inventory of Marital Status, Oxford Happiness Ques- tionnaire, and General Health Questionnaire were used as instruments of the study. Data was analyzed by SPSS17 and Amos 5 software using descriptive statistics, independent sample t test, and path analysis. Disregarding the gender factor, marital relationship status was directly related to happiness (phappiness was directly related to mental health, (phappiness and mental health was significant (phappiness had a mediator role in relation between marital relationship status and mental health in infertile individu- als disregarding the gender factor. Also, considering the gender factor, only in infertile women, marital relationship status can directly and indirectly affect happiness and mental health.
Ellis, George FR; Pabjan, Tadeusz
2013-01-01
Written by philosophers, cosmologists, and physicists, this collection of essays deals with causality, which is a core issue for both science and philosophy. Readers will learn about different types of causality in complex systems and about new perspectives on this issue based on physical and cosmological considerations. In addition, the book includes essays pertaining to the problem of causality in ancient Greek philosophy, and to the problem of God's relation to the causal structures of nature viewed in the light of contemporary physics and cosmology.
Glaese, John R.; Tobbe, Patrick A.
1986-01-01
The Space Station Mechanism Test Bed consists of a hydraulically driven, computer controlled six degree of freedom (DOF) motion system with which docking, berthing, and other mechanisms can be evaluated. Measured contact forces and moments are provided to the simulation host computer to enable representation of orbital contact dynamics. This report describes the development of a generalized math model which represents the relative motion between two rigid orbiting vehicles. The model allows motion in six DOF for each body, with no vehicle size limitation. The rotational and translational equations of motion are derived. The method used to transform the forces and moments from the sensor location to the vehicles' centers of mass is also explained. Two math models of docking mechanisms, a simple translational spring and the Remote Manipulator System end effector, are presented along with simulation results. The translational spring model is used in an attempt to verify the simulation with compensated hardware in the loop results.
Causal Analysis After Haavelmo
Heckman, James; Pinto, Rodrigo
2014-01-01
Haavelmo's seminal 1943 and 1944 papers are the first rigorous treatment of causality. In them, he distinguished the definition of causal parameters from their identification. He showed that causal parameters are defined using hypothetical models that assign variation to some of the inputs determining outcomes while holding all other inputs fixed. He thus formalized and made operational Marshall's (1890) ceteris paribus analysis. We embed Haavelmo's framework into the recursive framework of Directed Acyclic Graphs (DAGs) used in one influential recent approach to causality (Pearl, 2000) and in the related literature on Bayesian nets (Lauritzen, 1996). We compare the simplicity of an analysis of causality based on Haavelmo's methodology with the complex and nonintuitive approach used in the causal literature of DAGs—the “do-calculus” of Pearl (2009). We discuss the severe limitations of DAGs and in particular of the do-calculus of Pearl in securing identification of economic models. We extend our framework to consider models for simultaneous causality, a central contribution of Haavelmo. In general cases, DAGs cannot be used to analyze models for simultaneous causality, but Haavelmo's approach naturally generalizes to cover them. PMID:25729123
Directory of Open Access Journals (Sweden)
Basabdatta Sen Bhattacharya
2016-11-01
Full Text Available Experimental studies on the Lateral Geniculate Nucleus (LGN of mammals and rodents show that the inhibitory interneurons (IN receive around 47.1% of their afferents from the retinal spiking neurons, and constitute around 20 - 25% of the LGN cell population. However, there is a definite gap in knowledge about the role and impact of IN on thalamocortical dynamics in both experimental and model-based research. We use a neural mass computational model of the LGN with three neural populations viz. IN, thalamocortical relay (TCR, thalamic reticular nucleus (TRN, to study the causality of IN on LGN oscillations and state-transitions. The synaptic information transmission in the model is implemented with kinetic modelling, facilitating the linking of low-level cellular attributes with high-level population dynamics. The model is parameterised and tuned to simulate both Local Field Potential (LFP of LGN and electroencephalogram (EEG of visual cortex in an awake resting state with eyes closed and dominant frequency within the alpha (8-13 Hz band. The results show that: First, the response of the TRN is suppressed in the presence of IN in the circuit; disconnecting the IN from the circuit effects a dramatic change in the model output, displaying high amplitude synchronous oscillations within the alpha band in both TCR and TRN. These observations conform to experimental reports implicating the IN as the primary inhibitory modulator of LGN dynamics in a cognitive state, and that reduced cognition is achieved by suppressing the TRN response. Second, the model validates steady state visually evoked potential response in humans corresponding to periodic input stimuli; however, when the IN is disconnected from the circuit, the output power spectra do not reflect the input frequency. This agrees with experimental reports underpinning the role of IN in efficient retino-geniculate information transmission. Third, a smooth transition from alpha to theta band is
Dynamic Materials do the Trick in Participatory Business Modeling
DEFF Research Database (Denmark)
Caglio, Agnese; Buur, Jacob
In this position paper we suggest that design material with dynamic behaviour is particularly suited to scaffold groups of diverse participants in discussing the ‘if – then’ causalities of business models. Based on video data from a number of innovation project workshops we present a comparison...... matrix of five different material types for participatory business modeling. The comparison matrix highlights patterns in the use of materials, and how they allow people to participate, negotiate and make meaning....
Irvine, Kathryn M.; Miller, Scott; Al-Chokhachy, Robert K.; Archer, Erik; Roper, Brett B.; Kershner, Jeffrey L.
2015-01-01
Conceptual models are an integral facet of long-term monitoring programs. Proposed linkages between drivers, stressors, and ecological indicators are identified within the conceptual model of most mandated programs. We empirically evaluate a conceptual model developed for a regional aquatic and riparian monitoring program using causal models (i.e., Bayesian path analysis). We assess whether data gathered for regional status and trend estimation can also provide insights on why a stream may deviate from reference conditions. We target the hypothesized causal pathways for how anthropogenic drivers of road density, percent grazing, and percent forest within a catchment affect instream biological condition. We found instream temperature and fine sediments in arid sites and only fine sediments in mesic sites accounted for a significant portion of the maximum possible variation explainable in biological condition among managed sites. However, the biological significance of the direct effects of anthropogenic drivers on instream temperature and fine sediments were minimal or not detected. Consequently, there was weak to no biological support for causal pathways related to anthropogenic drivers’ impact on biological condition. With weak biological and statistical effect sizes, ignoring environmental contextual variables and covariates that explain natural heterogeneity would have resulted in no evidence of human impacts on biological integrity in some instances. For programs targeting the effects of anthropogenic activities, it is imperative to identify both land use practices and mechanisms that have led to degraded conditions (i.e., moving beyond simple status and trend estimation). Our empirical evaluation of the conceptual model underpinning the long-term monitoring program provided an opportunity for learning and, consequently, we discuss survey design elements that require modification to achieve question driven monitoring, a necessary step in the practice of
Gao, Xiangyun; Huang, Shupei; Sun, Xiaoqi; Hao, Xiaoqing; An, Feng
2018-03-01
Microscopic factors are the basis of macroscopic phenomena. We proposed a network analysis paradigm to study the macroscopic financial system from a microstructure perspective. We built the cointegration network model and the Granger causality network model based on econometrics and complex network theory and chose stock price time series of the real estate industry and its upstream and downstream industries as empirical sample data. Then, we analysed the cointegration network for understanding the steady long-term equilibrium relationships and analysed the Granger causality network for identifying the diffusion paths of the potential risks in the system. The results showed that the influence from a few key stocks can spread conveniently in the system. The cointegration network and Granger causality network are helpful to detect the diffusion path between the industries. We can also identify and intervene in the transmission medium to curb risk diffusion.
Programs as Causal Models: Speculations on Mental Programs and Mental Representation
Chater, Nick; Oaksford, Mike
2013-01-01
Judea Pearl has argued that counterfactuals and causality are central to intelligence, whether natural or artificial, and has helped create a rich mathematical and computational framework for formally analyzing causality. Here, we draw out connections between these notions and various current issues in cognitive science, including the nature of…
On generally covariant quantum field theory and generalized causal and dynamical structures
International Nuclear Information System (INIS)
Bannier, U.
1988-01-01
We give an example of a generally covariant quasilocal algebra associated with the massive free field. Maximal, two-sided ideals of this algebra are algebraic representatives of external metric fields. In some sense, this algebra may be regarded as a concrete realization of Ekstein's ideas of presymmetry in quantum field theory. Using ideas from our example and from usual algebraic quantum field theory, we discuss a generalized scheme, in which maximal ideals are viewed as algebraic representatives of dynamical equations or Lagrangians. The considered frame is no quantum gravity, but may lead to further insight into the relation between quantum theory and space-time geometry. (orig.)
Structure and Strength in Causal Induction
Griffiths, Thomas L.; Tenenbaum, Joshua B.
2005-01-01
We present a framework for the rational analysis of elemental causal induction--learning about the existence of a relationship between a single cause and effect--based upon causal graphical models. This framework makes precise the distinction between causal structure and causal strength: the difference between asking whether a causal relationship…
On the detection of effective marketing instruments and causality in VAR models
Horváth, C.; Otter, P.W.
2000-01-01
Dynamic multivariate models become more and more popular in analyzing the behavior of competive marketing environments. Takada and Bass (1998), Dekimpe, Hanssens and Silva-Rosso (1999), and Dekimpe and Hanssens (1999) recommend to use Vector Autoregressive (VAR) models because they provide
Chen, Mei-Chih; Chang, Kaowen
2014-11-06
Many city governments choose to supply more developable land and transportation infrastructure with the hope of attracting people and businesses to their cities. However, like those in Taiwan, major cities worldwide suffer from traffic congestion. This study applies the system thinking logic of the causal loops diagram (CLD) model in the System Dynamics (SD) approach to analyze the issue of traffic congestion and other issues related to roads and land development in Taiwan's cities. Comparing the characteristics of development trends with yearbook data for 2002 to 2013 for all of Taiwan's cities, this study explores the developing phenomenon of unlimited city sprawl and identifies the cause and effect relationships in the characteristics of development trends in traffic congestion, high-density population aggregation in cities, land development, and green land disappearance resulting from city sprawl. This study provides conclusions for Taiwan's cities' sustainability and development (S&D). When developing S&D policies, during decision making processes concerning city planning and land use management, governments should think with a holistic view of carrying capacity with the assistance of system thinking to clarify the prejudices in favor of the unlimited developing phenomena resulting from city sprawl.
International Nuclear Information System (INIS)
Bruneton, Jean-Philippe
2007-01-01
Field theories with Lorentz (or diffeomorphism invariant) action can exhibit superluminal behavior through the breaking of local Lorentz invariance. Quantum induced superluminal velocities are well-known examples of this effect. The issue of the causal behavior of such propagation is somewhat controversial in the literature and we intend to clarify it. We provide a careful analysis of the meaning of causality in classical relativistic field theories and stress the role played by the Cauchy problem and the notion of chronology. We show that, in general, superluminal behavior threatens causality only if one assumes that a prior chronology in spacetime exists. In the case where superluminal propagation occurs, however, there are at least two nonconformally related metrics in spacetime and thus two available notions of chronology. These two chronologies are on equal footing, and it would thus be misleading to choose ab initio one of them to define causality. Rather, we provide a formulation of causality in which no prior chronology is assumed. We argue that this is the only way to deal with the issue of causality in the case where some degrees of freedom propagate faster than others. In that framework, then, it is shown that superluminal propagation is not necessarily noncausal, the final answer depending on the existence of an initial data formulation. This also depends on global properties of spacetime that we discuss in detail. As an illustration of these conceptual issues, we consider two field theories, namely, k-essence scalar fields and bimetric theories of gravity, and we derive the conditions imposed by causality. We discuss various applications such as the dark energy problem, modified-Newtonian-dynamics-like theories of gravity, and varying speed of light theories
Encoding dependence in Bayesian causal networks
Bayesian networks (BNs) represent complex, uncertain spatio-temporal dynamics by propagation of conditional probabilities between identifiable states with a testable causal interaction model. Typically, they assume random variables are discrete in time and space with a static network structure that ...
Benbenishty, Rami; Astor, Ron Avi; Roziner, Ilan; Wrabel, Stephani L.
2016-01-01
The present study explores the causal link between school climate, school violence, and a school's general academic performance over time using a school-level, cross-lagged panel autoregressive modeling design. We hypothesized that reductions in school violence and climate improvement would lead to schools' overall improved academic performance.…
Directory of Open Access Journals (Sweden)
Ämin Baumeler
2017-07-01
Full Text Available Computation models such as circuits describe sequences of computation steps that are carried out one after the other. In other words, algorithm design is traditionally subject to the restriction imposed by a fixed causal order. We address a novel computing paradigm beyond quantum computing, replacing this assumption by mere logical consistency: We study non-causal circuits, where a fixed time structure within a gate is locally assumed whilst the global causal structure between the gates is dropped. We present examples of logically consistent non-causal circuits outperforming all causal ones; they imply that suppressing loops entirely is more restrictive than just avoiding the contradictions they can give rise to. That fact is already known for correlations as well as for communication, and we here extend it to computation.
Infertile Individuals’ Marital Relationship Status, Happiness, and Mental Health: A Causal Model
Directory of Open Access Journals (Sweden)
Seyed Habiballah Ahmadi Forooshany
2014-11-01
Full Text Available Background: This study examined the causal model of relation between marital relationship status, happiness, and mental health in infertile individuals. Materials and Methods: In this descriptive study, 155 subjects (men: 52 and women: 78, who had been visited in one of the infertility Centers, voluntarily participated in a self-evaluation. Golombok Rust Inventory of Marital Status, Oxford Happiness Questionnaire, and General Health Questionnaire were used as instruments of the study. Data was analyzed by SPSS17 and Amos 5 software using descriptive statistics, independent sample t test, and path analysis. Results: Disregarding the gender factor, marital relationship status was directly related to happiness (p<0.05 and happiness was directly related to mental health, (p<0.05. Also, indirect relation between marital relationship status and mental health was significant (p<0.05. These results were confirmed in women participants but in men participants only the direct relation between happiness and mental health was significant (p<0.05. Conclusion: Based on goodness of model fit in fitness indexes, happiness had a mediator role in relation between marital relationship status and mental health in infertile individuals disregarding the gender factor. Also, considering the gender factor, only in infertile women, marital relationship status can directly and indirectly affect happiness and mental health.
International Nuclear Information System (INIS)
Bouri, Elie
2015-01-01
Within the new developed causality-in-variance approach, this paper builds up a broad methodological framework to more accurately capture the risk spillover effects between global oil prices and Jordanian stock market returns during the period 1 March 2003–31 January 2014. The sample period is divided, on the basis of the 2008 financial crisis, into pre-crisis and post-crisis periods. Results for the pre-crisis period show a lack of risk spillovers between global oil and the Jordanian stock market. After the crisis, however, we find evidence for one-way risk spillover running from the oil market. These findings have implications for the design of appropriate asset allocation and regulatory policies to manage risk spillover effects. -- Highlights: •A broad methodological framework accurately seizes dynamic risk spillover between oil prices and Jordanian stock returns. •We find insignificant risk spillover until the start of the financial crisis. •Crude oil transmits its risk to the Jordanian stock market
Network-Oriented Modeling of Multi-Criteria Homophily and Opinion Dynamics in Social Media
Kozyreva, Olga; Pechina, Anna; Treur, J.
2018-01-01
In this paper we model the opinion dynamics in social groups in combination with adaptation of the connections based on a multicriteria homophily principle. The adaptive network model has been designed according to a Network-Oriented Modeling approach based on temporal-causal networks. The model has
Presenting the students’ academic achievement causal model based on goal orientation
Directory of Open Access Journals (Sweden)
EBRAHIM NASIRI
2017-10-01
Full Text Available Introduction: Several factors play a role in academic achievement, individual’s excellence and capability to do actions and tasks that the learner is in charge of in learning areas. The main goal of this study was to present academic achievement causal model based on the dimensions of goal orientation and learning approaches among the students of Medical Science and Dentistry courses in Guilan University of Medical Sciences in 2013. Methods: This study is based on a cross-sectional model. The participants included 175 first and second year students of the Medical and Dentistry schools in Guilan University of Medical Sciences selected by random cluster sampling [121 persons (69% Medical Basic Science students and 54 (30.9% Dentistry students]. The measurement tool included the Goal Orientation Scale of Bouffard and Study Process Questionnaire of Biggs and the students’ Grade Point Average. The study data were analyzed using Pearson correlation coefficient and structural equations modeling. SPSS 14 and Amos were used to analyze the data. Results: The results indicated a significant relationship between goal orientation and learning strategies (P<0.05. In addition, the results revealed that a significant relationship exists between learning strategies [Deep Learning (r=0.37, P<0.05, Surface Learning (r=-0.21, P<0.05], and academic achievement. The suggested model of research is fitted to the data of the research. Conclusion: Results showed that the students’ academic achievement model fits with experimental data, so it can be used in learning principles which lead to students’ achievement in learning.
Campagnoli, Patrizia; Petris, Giovanni
2009-01-01
State space models have gained tremendous popularity in as disparate fields as engineering, economics, genetics and ecology. Introducing general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. It illustrates the fundamental steps needed to use dynamic linear models in practice, using R package.
GIS and dynamic phenomena modeling
Czech Academy of Sciences Publication Activity Database
Klimešová, Dana
2006-01-01
Roč. 4, č. 4 (2006), s. 11-15 ISSN 0139-570X Institutional research plan: CEZ:AV0Z10750506 Keywords : dynamic modelling * temporal analysis * dynamics evaluation * temporal space Subject RIV: BC - Control Systems Theory
Directory of Open Access Journals (Sweden)
Sebastian Sippel
Full Text Available Data analysis and model-data comparisons in the environmental sciences require diagnostic measures that quantify time series dynamics and structure, and are robust to noise in observational data. This paper investigates the temporal dynamics of environmental time series using measures quantifying their information content and complexity. The measures are used to classify natural processes on one hand, and to compare models with observations on the other. The present analysis focuses on the global carbon cycle as an area of research in which model-data integration and comparisons are key to improving our understanding of natural phenomena. We investigate the dynamics of observed and simulated time series of Gross Primary Productivity (GPP, a key variable in terrestrial ecosystems that quantifies ecosystem carbon uptake. However, the dynamics, patterns and magnitudes of GPP time series, both observed and simulated, vary substantially on different temporal and spatial scales. We demonstrate here that information content and complexity, or Information Theory Quantifiers (ITQ for short, serve as robust and efficient data-analytical and model benchmarking tools for evaluating the temporal structure and dynamical properties of simulated or observed time series at various spatial scales. At continental scale, we compare GPP time series simulated with two models and an observations-based product. This analysis reveals qualitative differences between model evaluation based on ITQ compared to traditional model performance metrics, indicating that good model performance in terms of absolute or relative error does not imply that the dynamics of the observations is captured well. Furthermore, we show, using an ensemble of site-scale measurements obtained from the FLUXNET archive in the Mediterranean, that model-data or model-model mismatches as indicated by ITQ can be attributed to and interpreted as differences in the temporal structure of the respective
On modeling HIV and T cells in vivo: assessing causal estimators in vaccine trials.
Directory of Open Access Journals (Sweden)
W David Wick
2006-06-01
Full Text Available The first efficacy trials--named STEP--of a T cell vaccine against HIV/AIDS began in 2004. The unprecedented structure of these trials raised new modeling and statistical challenges. Is it plausible that memory T cells, as opposed to antibodies, can actually prevent infection? If they fail at prevention, to what extent can they ameliorate disease? And how do we estimate efficacy in a vaccine trial with two primary endpoints, one traditional, one entirely novel (viral load after infection, and where the latter may be influenced by selection bias due to the former? In preparation for the STEP trials, biostatisticians developed novel techniques for estimating a causal effect of a vaccine on viral load, while accounting for post-randomization selection bias. But these techniques have not been tested in biologically plausible scenarios. We introduce new stochastic models of T cell and HIV kinetics, making use of new estimates of the rate that cytotoxic T lymphocytes--CTLs; the so-called killer T cells--can kill HIV-infected cells. Based on these models, we make the surprising discovery that it is not entirely implausible that HIV-specific CTLs might prevent infection--as the designers explicitly acknowledged when they chose the endpoints of the STEP trials. By simulating thousands of trials, we demonstrate that the new statistical methods can correctly identify an efficacious vaccine, while protecting against a false conclusion that the vaccine exacerbates disease. In addition to uncovering a surprising immunological scenario, our results illustrate the utility of mechanistic modeling in biostatistics.
Infertile Individuals’ Marital Relationship Status, Happiness, and Mental Health: A Causal Model
Ahmadi Forooshany, Seyed Habiballah; Yazdkhasti, Fariba; Safari Hajataghaie, Saiede; Nasr Esfahani, Mohammad Hossein
2014-01-01
Background This study examined the causal model of relation between marital relation- ship status, happiness, and mental health in infertile individuals. Materials and Methods In this descriptive study, 155 subjects (men: 52 and women: 78), who had been visited in one of the infertility Centers, voluntarily participated in a self-evaluation. Golombok Rust Inventory of Marital Status, Oxford Happiness Ques- tionnaire, and General Health Questionnaire were used as instruments of the study. Data was analyzed by SPSS17 and Amos 5 software using descriptive statistics, independent sample t test, and path analysis. Results Disregarding the gender factor, marital relationship status was directly related to happiness (phappiness was directly related to mental health, (phappiness and mental health was significant (phappiness had a mediator role in relation between marital relationship status and mental health in infertile individu- als disregarding the gender factor. Also, considering the gender factor, only in infertile women, marital relationship status can directly and indirectly affect happiness and mental health. PMID:25379161
Causal learning and inference as a rational process: the new synthesis.
Holyoak, Keith J; Cheng, Patricia W
2011-01-01
Over the past decade, an active line of research within the field of human causal learning and inference has converged on a general representational framework: causal models integrated with bayesian probabilistic inference. We describe this new synthesis, which views causal learning and inference as a fundamentally rational process, and review a sample of the empirical findings that support the causal framework over associative alternatives. Causal events, like all events in the distal world as opposed to our proximal perceptual input, are inherently unobservable. A central assumption of the causal approach is that humans (and potentially nonhuman animals) have been designed in such a way as to infer the most invariant causal relations for achieving their goals based on observed events. In contrast, the associative approach assumes that learners only acquire associations among important observed events, omitting the representation of the distal relations. By incorporating bayesian inference over distributions of causal strength and causal structures, along with noisy-logical (i.e., causal) functions for integrating the influences of multiple causes on a single effect, human judgments about causal strength and structure can be predicted accurately for relatively simple causal structures. Dynamic models of learning based on the causal framework can explain patterns of acquisition observed with serial presentation of contingency data and are consistent with available neuroimaging data. The approach has been extended to a diverse range of inductive tasks, including category-based and analogical inferences.
Korzeniewska, Anna; Franaszczuk, Piotr J; Crainiceanu, Ciprian M; Kuś, Rafał; Crone, Nathan E
2011-06-15
Intracranial EEG studies in humans have shown that functional brain activation in a variety of functional-anatomic domains of human cortex is associated with an increase in power at a broad range of high gamma (>60Hz) frequencies. Although these electrophysiological responses are highly specific for the location and timing of cortical processing and in animal recordings are highly correlated with increased population firing rates, there has been little direct empirical evidence for causal interactions between different recording sites at high gamma frequencies. Such causal interactions are hypothesized to occur during cognitive tasks that activate multiple brain regions. To determine whether such causal interactions occur at high gamma frequencies and to investigate their functional significance, we used event-related causality (ERC) analysis to estimate the dynamics, directionality, and magnitude of event-related causal interactions using subdural electrocorticography (ECoG) recorded during two word production tasks: picture naming and auditory word repetition. A clinical subject who had normal hearing but was skilled in American Signed Language (ASL) provided a unique opportunity to test our hypothesis with reference to a predictable pattern of causal interactions, i.e. that language cortex interacts with different areas of sensorimotor cortex during spoken vs. signed responses. Our ERC analyses confirmed this prediction. During word production with spoken responses, perisylvian language sites had prominent causal interactions with mouth/tongue areas of motor cortex, and when responses were gestured in sign language, the most prominent interactions involved hand and arm areas of motor cortex. Furthermore, we found that the sites from which the most numerous and prominent causal interactions originated, i.e. sites with a pattern of ERC "divergence", were also sites where high gamma power increases were most prominent and where electrocortical stimulation mapping
Causal modeling of self-concept, job satisfaction, and retention of nurses.
Cowin, Leanne S; Johnson, Maree; Craven, Rhonda G; Marsh, Herbert W
2008-10-01
The critical shortage of nurses experienced throughout the western world has prompted researchers to examine one major component of this complex problem - the impact of nurses' professional identity and job satisfaction on retention. A descriptive correlational design with a longitudinal element was used to examine a causal model of nurses' self-concept, job satisfaction, and retention plans in 2002. A random sample of 2000 registered nurses was selected from the state registering authority listing. A postal survey assessing multiple dimensions of nurses' self-concept (measured by the nurse self-concept questionnaire), job satisfaction (measured by the index of work satisfaction) was undertaken at Time 1 (n=528) and 8 months later at Time 2 (n=332) (including retention plans (measured by the Nurse Retention Index). Using confirmatory factor analysis, correlation matrices and path analysis, measurement and structural models were examined on matching pairs of data from T1 and T2 (total sample N=332). Nurses' self-concept was found to have a stronger association with nurses' retention plans (B=.45) than job satisfaction (B=.28). Aspects of pay and task were not significantly related to retention plans, however, professional status (r=.51), and to a lesser extent, organizational policies (r=.27) were significant factors. Nurses' general self-concept was strongly related (r=.57) to retention plans. Strategies or interventions requiring implementation and evaluation include: counseling to improve nurse general self-concept, education programs and competencies in health communication between health professionals, reporting of nurse-initiated programs with substantial patient benefit, nurse-friendly organizational policies, common health team learning opportunities, and autonomous practice models.
Causality and headache triggers
Turner, Dana P.; Smitherman, Todd A.; Martin, Vincent T.; Penzien, Donald B.; Houle, Timothy T.
2013-01-01
Objective The objective of this study was to explore the conditions necessary to assign causal status to headache triggers. Background The term “headache trigger” is commonly used to label any stimulus that is assumed to cause headaches. However, the assumptions required for determining if a given stimulus in fact has a causal-type relationship in eliciting headaches have not been explicated. Methods A synthesis and application of Rubin’s Causal Model is applied to the context of headache causes. From this application the conditions necessary to infer that one event (trigger) causes another (headache) are outlined using basic assumptions and examples from relevant literature. Results Although many conditions must be satisfied for a causal attribution, three basic assumptions are identified for determining causality in headache triggers: 1) constancy of the sufferer; 2) constancy of the trigger effect; and 3) constancy of the trigger presentation. A valid evaluation of a potential trigger’s effect can only be undertaken once these three basic assumptions are satisfied during formal or informal studies of headache triggers. Conclusions Evaluating these assumptions is extremely difficult or infeasible in clinical practice, and satisfying them during natural experimentation is unlikely. Researchers, practitioners, and headache sufferers are encouraged to avoid natural experimentation to determine the causal effects of headache triggers. Instead, formal experimental designs or retrospective diary studies using advanced statistical modeling techniques provide the best approaches to satisfy the required assumptions and inform causal statements about headache triggers. PMID:23534872
Modelling dynamic roughness during floods
Paarlberg, Andries; Dohmen-Janssen, Catarine M.; Hulscher, Suzanne J.M.H.; Termes, A.P.P.
2007-01-01
In this paper, we present a dynamic roughness model to predict water levels during floods. Hysteresis effects of dune development are explicitly included. It is shown that differences between the new dynamic roughness model, and models where the roughness coefficient is calibrated, are most
Kammerdiner, Alla; Xanthopoulos, Petros; Pardalos, Panos M.
2007-11-01
In this chapter a potential problem with application of the Granger-causality based on the simple vector autoregressive (VAR) modeling to EEG data is investigated. Although some initial studies tested whether the data support the stationarity assumption of VAR, the stability of the estimated model is rarely (if ever) been verified. In fact, in cases when the stability condition is violated the process may exhibit a random walk like behavior or even be explosive. The problem is illustrated by an example.
Morabia, Alfredo
2005-01-01
Epidemiological methods, which combine population thinking and group comparisons, can primarily identify causes of disease in populations. There is therefore a tension between our intuitive notion of a cause, which we want to be deterministic and invariant at the individual level, and the epidemiological notion of causes, which are invariant only at the population level. Epidemiologists have given heretofore a pragmatic solution to this tension. Causal inference in epidemiology consists in checking the logical coherence of a causality statement and determining whether what has been found grossly contradicts what we think we already know: how strong is the association? Is there a dose-response relationship? Does the cause precede the effect? Is the effect biologically plausible? Etc. This approach to causal inference can be traced back to the English philosophers David Hume and John Stuart Mill. On the other hand, the mode of establishing causality, devised by Jakob Henle and Robert Koch, which has been fruitful in bacteriology, requires that in every instance the effect invariably follows the cause (e.g., inoculation of Koch bacillus and tuberculosis). This is incompatible with epidemiological causality which has to deal with probabilistic effects (e.g., smoking and lung cancer), and is therefore invariant only for the population.
Causality violation, gravitational shockwaves and UV completion
Energy Technology Data Exchange (ETDEWEB)
Hollowood, Timothy J.; Shore, Graham M. [Department of Physics, Swansea University,Swansea, SA2 8PP (United Kingdom)
2016-03-18
The effective actions describing the low-energy dynamics of QFTs involving gravity generically exhibit causality violations. These may take the form of superluminal propagation or Shapiro time advances and allow the construction of “time machines”, i.e. spacetimes admitting closed non-spacelike curves. Here, we discuss critically whether such causality violations may be used as a criterion to identify unphysical effective actions or whether, and how, causality problems may be resolved by embedding the action in a fundamental, UV complete QFT. We study in detail the case of photon scattering in an Aichelburg-Sexl gravitational shockwave background and calculate the phase shifts in QED for all energies, demonstrating their smooth interpolation from the causality-violating effective action values at low-energy to their manifestly causal high-energy limits. At low energies, these phase shifts may be interpreted as backwards-in-time coordinate jumps as the photon encounters the shock wavefront, and we illustrate how the resulting causality problems emerge and are resolved in a two-shockwave time machine scenario. The implications of our results for ultra-high (Planck) energy scattering, in which graviton exchange is modelled by the shockwave background, are highlighted.
Directory of Open Access Journals (Sweden)
John F Magnotti
2017-02-01
Full Text Available Audiovisual speech integration combines information from auditory speech (talker's voice and visual speech (talker's mouth movements to improve perceptual accuracy. However, if the auditory and visual speech emanate from different talkers, integration decreases accuracy. Therefore, a key step in audiovisual speech perception is deciding whether auditory and visual speech have the same source, a process known as causal inference. A well-known illusion, the McGurk Effect, consists of incongruent audiovisual syllables, such as auditory "ba" + visual "ga" (AbaVga, that are integrated to produce a fused percept ("da". This illusion raises two fundamental questions: first, given the incongruence between the auditory and visual syllables in the McGurk stimulus, why are they integrated; and second, why does the McGurk effect not occur for other, very similar syllables (e.g., AgaVba. We describe a simplified model of causal inference in multisensory speech perception (CIMS that predicts the perception of arbitrary combinations of auditory and visual speech. We applied this model to behavioral data collected from 60 subjects perceiving both McGurk and non-McGurk incongruent speech stimuli. The CIMS model successfully predicted both the audiovisual integration observed for McGurk stimuli and the lack of integration observed for non-McGurk stimuli. An identical model without causal inference failed to accurately predict perception for either form of incongruent speech. The CIMS model uses causal inference to provide a computational framework for studying how the brain performs one of its most important tasks, integrating auditory and visual speech cues to allow us to communicate with others.
Fenton, N.; Neil, M.; Lagnado, D.; Marsh, W.; Yet, B.; Constantinou, A.
2016-01-01
We show that existing Bayesian network (BN) modelling techniques cannot capture the correct intuitive reasoning in the important case when a set of mutually exclusive events need to be modelled as separate nodes instead of states of a single node. A previously proposed ‘solution’, which introduces a simple constraint node that enforces mutual exclusivity, fails to preserve the prior probabilities of the events, while other proposed solutions involve major changes to the original model. We pro...
Hybrid dynamics for currency modeling
Theodosopoulos, Ted; Trifunovic, Alex
2006-01-01
We present a simple hybrid dynamical model as a tool to investigate behavioral strategies based on trend following. The multiplicative symbolic dynamics are generated using a lognormal diffusion model for the at-the-money implied volatility term structure. Thus, are model exploits information from derivative markets to obtain qualititative properties of the return distribution for the underlier. We apply our model to the JPY-USD exchange rate and the corresponding 1mo., 3mo., 6mo. and 1yr. im...
The Mediation Formula: A Guide to the Assessment of Causal Pathways in Nonlinear Models
2011-10-27
There, too, the restrictions imposed by the “principal-strata” framework lead to surrogacy criteria that are incompatible with the practical aims of... surrogacy (see Pearl (2011)). 28 cally manipulable. After all, if our aim is to uncover causal mechanisms, it is hard to accept the PSDE restriction that
A Causal Model of Career Development and Quality of Life of College Students with Disabilities
Chun, Jina
2017-01-01
Researchers have assumed that social cognitive factors play significant roles in the career development of transition youth and young adults with disabilities and those without disabilities. However, research on the influence of the career decision-making process as a primary causal agent in one's psychosocial outcomes such as perceived level of…
Asakawa, Masami; Okano, Masao
2009-01-01
This study examined the factors influencing consumers' perception of online shopping and developed a causal model that explains how this perception affects their online-shopping behavior. We administered a questionnaire survey to 297 college students. By utilizing the answers to 13 questions pertaining to consumer perceptions, we conducted a factor analysis that identified the following three factors: "convenience", "anxiety regarding security" and "poor navigation". On the basis of this resu...
Chen, Yonghong; Bressler, Steven L.; Knuth, Kevin H.; Truccolo, Wilson A.; Ding, Mingzhou
2006-06-01
In this article we consider the stochastic modeling of neurobiological time series from cognitive experiments. Our starting point is the variable-signal-plus-ongoing-activity model. From this model a differentially variable component analysis strategy is developed from a Bayesian perspective to estimate event-related signals on a single trial basis. After subtracting out the event-related signal from recorded single trial time series, the residual ongoing activity is treated as a piecewise stationary stochastic process and analyzed by an adaptive multivariate autoregressive modeling strategy which yields power, coherence, and Granger causality spectra. Results from applying these methods to local field potential recordings from monkeys performing cognitive tasks are presented.
Entropy for theories with indefinite causal structure
International Nuclear Information System (INIS)
Markes, Sonia; Hardy, Lucien
2011-01-01
Any theory with definite causal structure has a defined past and future, be it defined by light cones or an absolute time scale. Entropy is a concept that has traditionally been reliant on a definite notion of causality. However, without a definite notion of causality, the concept of entropy is not all lost. Indefinite causal structure results from combining probabilistic predictions and dynamical space-time. The causaloid framework lays the mathematical groundwork to be able to treat indefinite causal structure. In this paper, we build on the causaloid mathematics and define a causally-unbiased entropy for an indefinite causal structure. In defining a causally-unbiased entropy, there comes about an emergent idea of causality in the form of a measure of causal connectedness, termed the Q factor.
Causal inference in econometrics
Kreinovich, Vladik; Sriboonchitta, Songsak
2016-01-01
This book is devoted to the analysis of causal inference which is one of the most difficult tasks in data analysis: when two phenomena are observed to be related, it is often difficult to decide whether one of them causally influences the other one, or whether these two phenomena have a common cause. This analysis is the main focus of this volume. To get a good understanding of the causal inference, it is important to have models of economic phenomena which are as accurate as possible. Because of this need, this volume also contains papers that use non-traditional economic models, such as fuzzy models and models obtained by using neural networks and data mining techniques. It also contains papers that apply different econometric models to analyze real-life economic dependencies.
van Ments, Laila; Roelofsma, Peter; Treur, Jan
2018-01-01
Religion is a central aspect of many individuals' lives around the world, and its influence on human behaviour has been extensively studied from many different perspectives. The current study integrates a number of these perspectives into one adaptive temporal-causal network model describing the mental states involved, their mutual relations, and the adaptation of some of these relations over time due to learning. By first developing a conceptual representation of a network model based on the literature, and then formalizing this model into a numerical representation, simulations can be done for almost any kind of religion and person, showing different behaviours for persons with different religious backgrounds and characters. The focus was mainly on the influence of religion on human empathy and dis-empathy, a topic very relevant today. The developed model could be valuable for many uses, involving support for a better understanding, and even prediction, of the behaviour of religious individuals. It is illustrated for a number of different scenarios based on different characteristics of the persons and of the religion.
Directory of Open Access Journals (Sweden)
Marinela eCapanu
2015-05-01
Full Text Available Identifying the small number of rare causal variants contributing to disease has beena major focus of investigation in recent years, but represents a formidable statisticalchallenge due to the rare frequencies with which these variants are observed. In thiscommentary we draw attention to a formal statistical framework, namely hierarchicalmodeling, to combine functional genomic annotations with sequencing data with theobjective of enhancing our ability to identify rare causal variants. Using simulations weshow that in all configurations studied, the hierarchical modeling approach has superiordiscriminatory ability compared to a recently proposed aggregate measure of deleteriousness,the Combined Annotation-Dependent Depletion (CADD score, supportingour premise that aggregate functional genomic measures can more accurately identifycausal variants when used in conjunction with sequencing data through a hierarchicalmodeling approach
Computer Modelling of Dynamic Processes
Directory of Open Access Journals (Sweden)
B. Rybakin
2000-10-01
Full Text Available Results of numerical modeling of dynamic problems are summed in the article up. These problems are characteristic for various areas of human activity, in particular for problem solving in ecology. The following problems are considered in the present work: computer modeling of dynamic effects on elastic-plastic bodies, calculation and determination of performances of gas streams in gas cleaning equipment, modeling of biogas formation processes.
Toward a Comprehensive Model of Antisocial Development: A Dynamic Systems Approach
Granic, Isabela; Patterson, Gerald R.
2006-01-01
The purpose of this article is to develop a preliminary comprehensive model of antisocial development based on dynamic systems principles. The model is built on the foundations of behavioral research on coercion theory. First, the authors focus on the principles of multistability, feedback, and nonlinear causality to reconceptualize real-time…
Directory of Open Access Journals (Sweden)
K. Agyapong-Kodua
2012-01-01
Full Text Available Enterprise modelling techniques support business process (reengineering by capturing existing processes and based on perceived outputs, support the design of future process models capable of meeting enterprise requirements. System dynamics modelling tools on the other hand are used extensively for policy analysis and modelling aspects of dynamics which impact on businesses. In this paper, the use of enterprise and system dynamics modelling techniques has been integrated to facilitate qualitative and quantitative reasoning about the structures and behaviours of processes and resource systems used by a Manufacturing Enterprise during the production of composite bearings. The case study testing reported has led to the specification of a new modelling methodology for analysing and managing dynamics and complexities in production systems. This methodology is based on a systematic transformation process, which synergises the use of a selection of public domain enterprise modelling, causal loop and continuous simulation modelling techniques. The success of the modelling process defined relies on the creation of useful CIMOSA process models which are then converted to causal loops. The causal loop models are then structured and translated to equivalent dynamic simulation models using the proprietary continuous simulation modelling tool iThink.
Causality discovery technology
Chen, M.; Ertl, T.; Jirotka, M.; Trefethen, A.; Schmidt, A.; Coecke, B.; Bañares-Alcántara, R.
2012-11-01
Causality is the fabric of our dynamic world. We all make frequent attempts to reason causation relationships of everyday events (e.g., what was the cause of my headache, or what has upset Alice?). We attempt to manage causality all the time through planning and scheduling. The greatest scientific discoveries are usually about causality (e.g., Newton found the cause for an apple to fall, and Darwin discovered natural selection). Meanwhile, we continue to seek a comprehensive understanding about the causes of numerous complex phenomena, such as social divisions, economic crisis, global warming, home-grown terrorism, etc. Humans analyse and reason causality based on observation, experimentation and acquired a priori knowledge. Today's technologies enable us to make observations and carry out experiments in an unprecedented scale that has created data mountains everywhere. Whereas there are exciting opportunities to discover new causation relationships, there are also unparalleled challenges to benefit from such data mountains. In this article, we present a case for developing a new piece of ICT, called Causality Discovery Technology. We reason about the necessity, feasibility and potential impact of such a technology.
An Empirical Analysis of the ASEAN-4’s Causality between Exports and Output Growth
Lam, Tri Dung
2016-01-01
This paper specifically focuses on analysing the causality between real GDP and real export of goods and services of the ASEAN-4 countries (Indonesia, Malaysia, Thailand and the Philippines) by using comprehensive econometric techniques such as the unit root test, cointegration test, and error correction model. This study reveals that for short-run dynamics, while bi-directional Granger-causality exists in Malaysia, the Philippines, and Thailand; the unidirectional Granger-causality runs from...
Fuertes Casals, Alba; Casals Casanova, Miquel; Gangolells Solanellas, Marta; Forcada Matheu, Núria; Macarulla Martí, Marcel; Roca Ramon, Xavier
2013-01-01
Despite the increasing efforts made by the construction sector to reduce the environmental impact of their processes, construction sites are still a major source of pollution and adverse impacts on the environment. This paper aims to improve the understanding of construction-related environmental impacts by identifying on-site causal factors and associated immediate circumstances during construc- tion processes for residential building projects. Based on the literature and focus g...
Causal compositional models in valuation-based systems with examples in specific theories
Czech Academy of Sciences Publication Activity Database
Jiroušek, Radim; Shenoy, P. P.
2016-01-01
Roč. 72, č. 1 (2016), s. 95-112 ISSN 0888-613X Grant - others:GA ČR(CZ) GA15-00215S Institutional support: RVO:67985556 Keywords : operator of composition * causality * belief function Subject RIV: AH - Economics OBOR OECD: Economic Theory Impact factor: 2.845, year: 2016 http://library.utia.cas.cz/separaty/2017/MTR/jirousek-0481260.pdf
Bulk viscous cosmology with causal transport theory
International Nuclear Information System (INIS)
Piattella, Oliver F.; Fabris, Júlio C.; Zimdahl, Winfried
2011-01-01
We consider cosmological scenarios originating from a single imperfect fluid with bulk viscosity and apply Eckart's and both the full and the truncated Müller-Israel-Stewart's theories as descriptions of the non-equilibrium processes. Our principal objective is to investigate if the dynamical properties of Dark Matter and Dark Energy can be described by a single viscous fluid and how such description changes when a causal theory (Müller-Israel-Stewart's, both in its full and truncated forms) is taken into account instead of Eckart's non-causal one. To this purpose, we find numerical solutions for the gravitational potential and compare its behaviour with the corresponding ΛCDM case. Eckart's and the full causal theory seem to be disfavoured, whereas the truncated theory leads to results similar to those of the ΛCDM model for a bulk viscous speed in the interval 10 −11 || cb 2 ∼ −8
Komperda, Regis
The purpose of this dissertation is to test a model of relationships among factors characterizing aspects of a student-centered constructivist learning environment and student outcomes of satisfaction and academic achievement in introductory undergraduate chemistry courses. Constructivism was chosen as the theoretical foundation for this research because of its widespread use in chemical education research and practice. In a constructivist learning environment the role of the teacher shifts from delivering content towards facilitating active student engagement in activities that encourage individual knowledge construction through discussion and application of content. Constructivist approaches to teaching introductory chemistry courses have been adopted by some instructors as a way to improve student outcomes, but little research has been done on the causal relationships among particular aspects of the learning environment and student outcomes. This makes it difficult for classroom teachers to know which aspects of a constructivist teaching approach are critical to adopt and which may be modified to better suit a particular learning environment while still improving student outcomes. To investigate a model of these relationships, a survey designed to measure student perceptions of three factors characterizing a constructivist learning environment in online courses was adapted for use in face-to-face chemistry courses. These three factors, teaching presence, social presence, and cognitive presence, were measured using a slightly modified version of the Community of Inquiry (CoI) instrument. The student outcomes investigated in this research were satisfaction and academic achievement, as measured by standardized American Chemical Society (ACS) exam scores and course grades. Structural equation modeling (SEM) was used to statistically model relationships among the three presence factors and student outcome variables for 391 students enrolled in six sections of a
Understanding bicycling in cities using system dynamics modelling.
Macmillan, Alexandra; Woodcock, James
2017-12-01
Increasing urban bicycling has established net benefits for human and environmental health. Questions remain about which policies are needed and in what order, to achieve an increase in cycling while avoiding negative consequences. Novel ways of considering cycling policy are needed, bringing together expertise across policy, community and research to develop a shared understanding of the dynamically complex cycling system. In this paper we use a collaborative learning process to develop a dynamic causal model of urban cycling to develop consensus about the nature and order of policies needed in different cycling contexts to optimise outcomes. We used participatory system dynamics modelling to develop causal loop diagrams (CLDs) of cycling in three contrasting contexts: Auckland, London and Nijmegen. We combined qualitative interviews and workshops to develop the CLDs. We used the three CLDs to compare and contrast influences on cycling at different points on a "cycling trajectory" and drew out policy insights. The three CLDs consisted of feedback loops dynamically influencing cycling, with significant overlap between the three diagrams. Common reinforcing patterns emerged: growing numbers of people cycling lifts political will to improve the environment; cycling safety in numbers drives further growth; and more cycling can lead to normalisation across the population. By contrast, limits to growth varied as cycling increases. In Auckland and London, real and perceived danger was considered the main limit, with added barriers to normalisation in London. Cycling congestion and "market saturation" were important in the Netherlands. A generalisable, dynamic causal theory for urban cycling enables a more ordered set of policy recommendations for different cities on a cycling trajectory. Participation meant the collective knowledge of cycling stakeholders was represented and triangulated with research evidence. Extending this research to further cities, especially in low
Structural dynamic modifications via models
Indian Academy of Sciences (India)
The study shows that as many as half of the matrix ... the dynamicist's analytical modelling skill which would appear both in the numerator as. Figure 2. ..... Brandon J A 1990 Strategies for structural dynamic modification (New York: John Wiley).
Dynamic programming models and applications
Denardo, Eric V
2003-01-01
Introduction to sequential decision processes covers use of dynamic programming in studying models of resource allocation, methods for approximating solutions of control problems in continuous time, production control, more. 1982 edition.
Moment problems and the causal set approach to quantum gravity
International Nuclear Information System (INIS)
Ash, Avner; McDonald, Patrick
2003-01-01
We study a collection of discrete Markov chains related to the causal set approach to modeling discrete theories of quantum gravity. The transition probabilities of these chains satisfy a general covariance principle, a causality principle, and a renormalizability condition. The corresponding dynamics are completely determined by a sequence of non-negative real coupling constants. Using techniques related to the classical moment problem, we give a complete description of any such sequence of coupling constants. We prove a representation theorem: every discrete theory of quantum gravity arising from causal set dynamics satisfying covariance, causality, and renormalizability corresponds to a unique probability distribution function on the non-negative real numbers, with the coupling constants defining the theory given by the moments of the distribution
Directory of Open Access Journals (Sweden)
Bongsuk Sung
2018-04-01
Full Text Available This study explores how political–economic forces could affect export performance in the renewable energy technologies market. We conduct panel framework analyses to verify the characteristics of panel data for 19 countries before establishing the panel estimator meant to test the effects of political–economic forces on export specialization. We consider the results of the panel framework analyses and develop an empirical model to test casual dynamic relationships between political–economic forces and export performance. The results from the least squares dummy variable-corrected estimation indicate that the major factors promoting the export specialization of renewable energy technologies are, in order of decreasing importance, public pressure, market size, and government demand-pull policy. However, the traditional energy industry has no significant effect on export performance. Finally, this study finds that dynamic effects exist in all estimations.
Dynamical models of the Galaxy
Directory of Open Access Journals (Sweden)
McMillan P.J.
2012-02-01
Full Text Available I discuss the importance of dynamical models for exploiting survey data, focusing on the advantages of “torus” models. I summarize a number of applications of these models to the study of the Milky Way, including the determination of the peculiar Solar velocity and investigation of the Hyades moving group.
Causal inference based on counterfactuals
Directory of Open Access Journals (Sweden)
Höfler M
2005-09-01
Full Text Available Abstract Background The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies. Discussion This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when estimating causal effects are reviewed. These include causal interactions, imperfect experiments, adjustment for confounding, time-varying exposures, competing risks and the probability of causation. It is argued that the counterfactual model of causal effects captures the main aspects of causality in health sciences and relates to many statistical procedures. Summary Counterfactuals are the basis of causal inference in medicine and epidemiology. Nevertheless, the estimation of counterfactual differences pose several difficulties, primarily in observational studies. These problems, however, reflect fundamental barriers only when learning from observations, and this does not invalidate the counterfactual concept.
Sinha, Shriprakash
2016-12-01
Simulation study in systems biology involving computational experiments dealing with Wnt signaling pathways abound in literature but often lack a pedagogical perspective that might ease the understanding of beginner students and researchers in transition, who intend to work on the modeling of the pathway. This paucity might happen due to restrictive business policies which enforce an unwanted embargo on the sharing of important scientific knowledge. A tutorial introduction to computational modeling of Wnt signaling pathway in a human colorectal cancer dataset using static Bayesian network models is provided. The walkthrough might aid biologists/informaticians in understanding the design of computational experiments that is interleaved with exposition of the Matlab code and causal models from Bayesian network toolbox. The manuscript elucidates the coding contents of the advance article by Sinha (Integr. Biol. 6:1034-1048, 2014) and takes the reader in a step-by-step process of how (a) the collection and the transformation of the available biological information from literature is done, (b) the integration of the heterogeneous data and prior biological knowledge in the network is achieved, (c) the simulation study is designed, (d) the hypothesis regarding a biological phenomena is transformed into computational framework, and (e) results and inferences drawn using d -connectivity/separability are reported. The manuscript finally ends with a programming assignment to help the readers get hands-on experience of a perturbation project. Description of Matlab files is made available under GNU GPL v3 license at the Google code project on https://code.google.com/p/static-bn-for-wnt-signaling-pathway and https: //sites.google.com/site/shriprakashsinha/shriprakashsinha/projects/static-bn-for-wnt-signaling-pathway. Latest updates can be found in the latter website.
Henderson, Nicole L; Dressler, William W
2017-12-01
This study examines the knowledge individuals use to make judgments about persons with substance use disorder. First, we show that there is a cultural model of addiction causality that is both shared and contested. Second, we examine how individuals' understanding of that model is associated with stigma attribution. Research was conducted among undergraduate students at the University of Alabama. College students in the 18-25 age range are especially at risk for developing substance use disorder, and they are, perhaps more than any other population group, intensely targeted by drug education. The elicited cultural model includes different types of causes distributed across five distinct themes: Biological, Self-Medication, Familial, Social, and Hedonistic. Though there was cultural consensus among respondents overall, residual agreement analysis showed that the cultural model of addiction causality is a multicentric domain. Two centers of the model, the moral and the medical, were discovered. Differing adherence to these centers is associated with the level of stigma attributed towards individuals with substance use disorder. The results suggest that current approaches to substance use education could contribute to stigma attribution, which may or may not be inadvertent. The significance of these results for both theory and the treatment of addiction are discussed.
Covariation in Natural Causal Induction.
Cheng, Patricia W.; Novick, Laura R.
1991-01-01
Biases and models usually offered by cognitive and social psychology and by philosophy to explain causal induction are evaluated with respect to focal sets (contextually determined sets of events over which covariation is computed). A probabilistic contrast model is proposed as underlying covariation computation in natural causal induction. (SLD)
Lepley, Lindsey K; McKeon, Patrick O; Fitzpatrick, Shane G; Beckemeyer, Catherine L; Uhl, Timothy L; Butterfield, Timothy A
2016-10-01
The mechanisms that contribute to the development of chronic ankle instability are not understood. Investigators have developed a hypothetical model in which neuromuscular alterations that stem from damaged ankle ligaments are thought to affect periarticular and proximal muscle activity. However, the retrospective nature of these studies does not allow a causal link to be established. To assess temporal alterations in the activity of 2 periarticular muscles of the rat ankle and 2 proximal muscles of the rat hind limb after an ankle sprain. Controlled laboratory study. Laboratory. Five healthy adult male Long Evans rats (age = 16 weeks, mass = 400.0 ± 13.5 g). Indwelling fine-wire electromyography (EMG) electrodes were implanted surgically into the biceps femoris, medial gastrocnemius, vastus lateralis, and tibialis anterior muscles of the rats. We recorded baseline EMG measurements while the rats walked on a motor-driven treadmill and then induced a closed lateral ankle sprain by overextending the lateral ankle ligaments. After ankle sprain, the rats were placed on the treadmill every 24 hours for 7 days, and we recorded postsprain EMG data. Onset time of muscle activity, phase duration, sample entropy, and minimal detectable change (MDC) were assessed and compared with baseline using 2-tailed dependent t tests. Compared with baseline, delayed onset time of muscle activity was exhibited in the biceps femoris (baseline = -16.7 ± 54.0 milliseconds [ms]) on day 0 (5.2 ± 64.1 ms; t 4 = -4.655, P = .043) and tibialis anterior (baseline = 307.0 ± 64.2 ms) muscles on day 3 (362.5 ± 55.9 ms; t 4 = -5.427, P = .03) and day 6 (357.3 ± 39.6 ms; t 4 = -3.802, P = .02). Longer phase durations were observed for the vastus lateralis (baseline = 321.9 ± 92.6 ms) on day 3 (401.3 ± 101.2 ms; t 3 = -4.001, P = .03), day 4 (404.1 ± 93.0 ms; t 3 = -3.320, P = .048), and day 5 (364.6 ± 105.2 ms; t 3 = -3.963, P = .03) and for the tibialis anterior (baseline = 103.9 ± 16.4 ms
Causal Diagrams for Empirical Research
Pearl, Judea
1994-01-01
The primary aim of this paper is to show how graphical models can be used as a mathematical language for integrating statistical and subject-matter information. In particular, the paper develops a principled, nonparametric framework for causal inference, in which diagrams are queried to determine if the assumptions available are sufficient for identifiying causal effects from non-experimental data. If so the diagrams can be queried to produce mathematical expressions for causal effects in ter...
Directory of Open Access Journals (Sweden)
Hilary Zelko
Full Text Available BACKGROUND: Although scientific innovation has been a long-standing topic of interest for historians, philosophers and cognitive scientists, few studies in biomedical research have examined from researchers' perspectives how high impact publications are developed and why they are consistently produced by a small group of researchers. Our objective was therefore to interview a group of researchers with a track record of high impact publications to explore what mechanism they believe contribute to the generation of high impact publications. METHODOLOGY/PRINCIPAL FINDINGS: Researchers were located in universities all over the globe and interviews were conducted by phone. All interviews were transcribed using standard qualitative methods. A Grounded Theory approach was used to code each transcript, later aggregating concept and categories into overarching explanation model. The model was then translated into a System Dynamics mathematical model to represent its structure and behavior. Five emerging themes were found in our study. First, researchers used heuristics or rules of thumb that came naturally to them. Second, these heuristics were reinforced by positive feedback from their peers and mentors. Third, good communication skills allowed researchers to provide feedback to their peers, thus closing a positive feedback loop. Fourth, researchers exhibited a number of psychological attributes such as curiosity or open-mindedness that constantly motivated them, even when faced with discouraging situations. Fifth, the system is dominated by randomness and serendipity and is far from a linear and predictable environment. Some researchers, however, took advantage of this randomness by incorporating mechanisms that would allow them to benefit from random findings. The aggregation of these themes into a policy model represented the overall expected behavior of publications and their impact achieved by high impact researchers. CONCLUSIONS: The proposed
International Nuclear Information System (INIS)
McFadden, J.H.; Paulsen, M.P.; Gose, G.C.
1981-01-01
A time dependent equation for the slip velocity in a two-phase flow condition has been incorporated into a developmental version of the RETRAN computer code. This model addition has been undertaken to remove a limitation in RETRAN-01 associated with the homogeneous equilibrium mixture model. In this paper, the development of the slip model is summarized and the corresponding constitutive equations are discussed. Comparisons of RETRAN analyses with steady-state void fraction data and data from the Semiscale S-02-6 small break test are also presented
Modeling Propellant Tank Dynamics
National Aeronautics and Space Administration — The main objective of my work will be to develop accurate models of self-pressurizing propellant tanks for use in designing hybrid rockets. The first key goal is to...
Directory of Open Access Journals (Sweden)
Ibrahim Delibalta
2017-01-01
Full Text Available We provide a causal inference framework to model the effects of machine learning algorithms on user preferences. We then use this mathematical model to prove that the overall system can be tuned to alter those preferences in a desired manner. A user can be an online shopper or a social media user, exposed to digital interventions produced by machine learning algorithms. A user preference can be anything from inclination towards a product to a political party affiliation. Our framework uses a state-space model to represent user preferences as latent system parameters which can only be observed indirectly via online user actions such as a purchase activity or social media status updates, shares, blogs, or tweets. Based on these observations, machine learning algorithms produce digital interventions such as targeted advertisements or tweets. We model the effects of these interventions through a causal feedback loop, which alters the corresponding preferences of the user. We then introduce algorithms in order to estimate and later tune the user preferences to a particular desired form. We demonstrate the effectiveness of our algorithms through experiments in different scenarios.
Zigzagging causality EPR model: answer to Vigier and coworkers and to Sutherland
Energy Technology Data Exchange (ETDEWEB)
de Beauregard, O.C.
1987-08-01
The concept of propagation in time of Vigier and co-workers (V et al.) implies the ideal of a supertime; it is thus alien to most Minkowskian pictures and certainly to the authors. From this stems much of V et al.'s misunderstandings of his position. In steady motion of a classical fluid nobody thinks that momentum conservation is violated, or that momentum is shot upstream without cause because of the suction from the sinks. Similarly with momentum-energy in spacetime and the acceptance of an advanced causality. As for the CT invariance of the Feynman propagator, the causality asymmetry it entails is factlike, not lawlike. The geometrical counterpart of the symmetry between prediction and retrodiction and between retarded and advanced waves, as expressed in the alternative expressions
On the zigzagging causality EPR model: Answer to Vigier and coworkers and to Sutherland
Costa de Beauregard, O.
1987-08-01
The concept of “propagation in time” of Vigier and co-workers (V et al.) implies the idea of a supertime; it is thus alien to most Minkowskian pictures and certainly to mine. From this stems much of V et al.'s misunderstandings of my position. In steady motion of a classical fluid nobody thinks that “momentum conservation is violated,” or that “momentum is shot upstream without cause” because of the suction from the sinks! Similarly with momentum-energy in space-time and the acceptance of an advanced causality. As for the CT invariance of the Feynman propagator, the causality asymmetry it entails is factlike, not lawlike. The geometrical counterpart of the symmetry between prediction and retrodiction and between retarded and advanced waves, as expressed in the alternative expressions == for a transition amplitude between a preparation |A> and a measurement |B>, is CPT-invariant, not PT-invariant. These three expressions respectively illustrate the collapse, the retrocollapse, and the symmetric collapse-and-retrocollapse concepts. As for Sutherland's argument, what it “falsifies” is not my retrocausation concept but the hidden-variables assumption he has unwittingly made.
Modelling group dynamic animal movement
DEFF Research Database (Denmark)
Langrock, Roland; Hopcraft, J. Grant C.; Blackwell, Paul G.
2014-01-01
makes its movement decisions relative to the group centroid. The basic idea is framed within the flexible class of hidden Markov models, extending previous work on modelling animal movement by means of multi-state random walks. While in simulation experiments parameter estimators exhibit some bias......, to date, practical statistical methods which can include group dynamics in animal movement models have been lacking. We consider a flexible modelling framework that distinguishes a group-level model, describing the movement of the group's centre, and an individual-level model, such that each individual......Group dynamic movement is a fundamental aspect of many species' movements. The need to adequately model individuals' interactions with other group members has been recognised, particularly in order to differentiate the role of social forces in individual movement from environmental factors. However...
On causality of extreme events
Directory of Open Access Journals (Sweden)
Massimiliano Zanin
2016-06-01
Full Text Available Multiple metrics have been developed to detect causality relations between data describing the elements constituting complex systems, all of them considering their evolution through time. Here we propose a metric able to detect causality within static data sets, by analysing how extreme events in one element correspond to the appearance of extreme events in a second one. The metric is able to detect non-linear causalities; to analyse both cross-sectional and longitudinal data sets; and to discriminate between real causalities and correlations caused by confounding factors. We validate the metric through synthetic data, dynamical and chaotic systems, and data representing the human brain activity in a cognitive task. We further show how the proposed metric is able to outperform classical causality metrics, provided non-linear relationships are present and large enough data sets are available.
Modeling Internet Topology Dynamics
Haddadi, H.; Uhlig, S.; Moore, A.; Mortier, R.; Rio, M.
Despite the large number of papers on network topology modeling and inference, there still exists ambiguity about the real nature of the Internet AS and router level topology. While recent findings have illustrated the inaccuracies in maps inferred from BGP peering and traceroute measurements,
Generative Models of Conformational Dynamics
Langmead, Christopher James
2014-01-01
Atomistic simulations of the conformational dynamics of proteins can be performed using either Molecular Dynamics or Monte Carlo procedures. The ensembles of three-dimensional structures produced during simulation can be analyzed in a number of ways to elucidate the thermodynamic and kinetic properties of the system. The goal of this chapter is to review both traditional and emerging methods for learning generative models from atomistic simulation data. Here, the term ‘generative’ refers to a...
International Nuclear Information System (INIS)
Fukui, Hirokazu; Yoshida, Michio; Yamaura, Kazuho
2000-01-01
For those who run an organization, it is critical to identify the causal relationship between the organization's characteristics and the safety-checking action of its staff, in order to effectively implement activities for promoting safety. In this research. a causal model of the safety-checking action was developed and factors affecting it were studied. A questionnaire survey, which includes safety awareness, attitude toward safety, safety culture and others, was conducted at three nuclear power plants and eight factors were extracted by means of factor analysis of the questionnaire items. The extracted eight interrelated factors were as follows: work norm, supervisory action, interest in training, recognition of importance, safety-checking action, the subject of safety, knowledge/skills, and the attitude of an organization. Among them, seven factors except the recognition of importance were defined as latent variables and a causal model of safety-checking action was constructed. By means of covariance structure analysis, it was found that the three factors: the attitude of an organization, supervisory action and the subject of safety, have a significant effect on the safety-checking action. Moreover, it was also studied that workplaces in which these three factors are highly regarded form social environment where safety-checking action is fully supported by the workplace as a whole, while workplaces in which these three factors are poorly regarded do not fully form social environment where safety-checking action is supported. Therefore, the workplaces form an organizational environment where safety-checking action tends to depend strongly upon the knowledge or skills of individuals. On top of these, it was noted that the attitude of an organization and supervisory action are important factors that serve as the first trigger affecting the formation of the organizational climate for safety. (author)
Energy Technology Data Exchange (ETDEWEB)
Fukui, Hirokazu [Institute of Nuclear Safety System Inc., Seika, Kyoto (Japan); Yoshida, Michio; Yamaura, Kazuho [Japan Institute for Group Dynamics, Fukuoka (Japan)
2000-09-01
For those who run an organization, it is critical to identify the causal relationship between the organization's characteristics and the safety-checking action of its staff, in order to effectively implement activities for promoting safety. In this research. a causal model of the safety-checking action was developed and factors affecting it were studied. A questionnaire survey, which includes safety awareness, attitude toward safety, safety culture and others, was conducted at three nuclear power plants and eight factors were extracted by means of factor analysis of the questionnaire items. The extracted eight interrelated factors were as follows: work norm, supervisory action, interest in training, recognition of importance, safety-checking action, the subject of safety, knowledge/skills, and the attitude of an organization. Among them, seven factors except the recognition of importance were defined as latent variables and a causal model of safety-checking action was constructed. By means of covariance structure analysis, it was found that the three factors: the attitude of an organization, supervisory action and the subject of safety, have a significant effect on the safety-checking action. Moreover, it was also studied that workplaces in which these three factors are highly regarded form social environment where safety-checking action is fully supported by the workplace as a whole, while workplaces in which these three factors are poorly regarded do not fully form social environment where safety-checking action is supported. Therefore, the workplaces form an organizational environment where safety-checking action tends to depend strongly upon the knowledge or skills of individuals. On top of these, it was noted that the attitude of an organization and supervisory action are important factors that serve as the first trigger affecting the formation of the organizational climate for safety. (author)
Causality Statistical Perspectives and Applications
Berzuini, Carlo; Bernardinell, Luisa
2012-01-01
A state of the art volume on statistical causality Causality: Statistical Perspectives and Applications presents a wide-ranging collection of seminal contributions by renowned experts in the field, providing a thorough treatment of all aspects of statistical causality. It covers the various formalisms in current use, methods for applying them to specific problems, and the special requirements of a range of examples from medicine, biology and economics to political science. This book:Provides a clear account and comparison of formal languages, concepts and models for statistical causality. Addr
Dynamics of safety performance and culture: a group model building approach.
Goh, Yang Miang; Love, Peter E D; Stagbouer, Greg; Annesley, Chris
2012-09-01
The management of occupational health and safety (OHS) including safety culture interventions is comprised of complex problems that are often hard to scope and define. Due to the dynamic nature and complexity of OHS management, the concept of system dynamics (SD) is used to analyze accident prevention. In this paper, a system dynamics group model building (GMB) approach is used to create a causal loop diagram of the underlying factors influencing the OHS performance of a major drilling and mining contractor in Australia. While the organization has invested considerable resources into OHS their disabling injury frequency rate (DIFR) has not been decreasing. With this in mind, rich individualistic knowledge about the dynamics influencing the DIFR was acquired from experienced employees with operations, health and safety and training background using a GMB workshop. Findings derived from the workshop were used to develop a series of causal loop diagrams that includes a wide range of dynamics that can assist in better understanding the causal influences OHS performance. The causal loop diagram provides a tool for organizations to hypothesize the dynamics influencing effectiveness of OHS management, particularly the impact on DIFR. In addition the paper demonstrates that the SD GMB approach has significant potential in understanding and improving OHS management. Copyright © 2011 Elsevier Ltd. All rights reserved.
Structural Equations and Causal Explanations: Some Challenges for Causal SEM
Markus, Keith A.
2010-01-01
One common application of structural equation modeling (SEM) involves expressing and empirically investigating causal explanations. Nonetheless, several aspects of causal explanation that have an impact on behavioral science methodology remain poorly understood. It remains unclear whether applications of SEM should attempt to provide complete…
Energy Technology Data Exchange (ETDEWEB)
Lu, Fengbin, E-mail: fblu@amss.ac.cn [Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190 (China); Qiao, Han, E-mail: qiaohan@ucas.ac.cn [School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190 (China); Wang, Shouyang, E-mail: sywang@amss.ac.cn [School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190 (China); Lai, Kin Keung, E-mail: mskklai@cityu.edu.hk [Department of Management Sciences, City University of Hong Kong (Hong Kong); Li, Yuze, E-mail: richardyz.li@mail.utoronto.ca [Department of Industrial Engineering, University of Toronto (Canada)
2017-01-15
This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor’s 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model.
International Nuclear Information System (INIS)
Lu, Fengbin; Qiao, Han; Wang, Shouyang; Lai, Kin Keung; Li, Yuze
2017-01-01
This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor’s 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model.
Lu, Fengbin; Qiao, Han; Wang, Shouyang; Lai, Kin Keung; Li, Yuze
2017-01-01
This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor's 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model. Copyright Â© 2016 Elsevier Ltd. All rights reserved.
Explaining through causal mechanisms
Biesbroek, Robbert; Dupuis, Johann; Wellstead, Adam
2017-01-01
This paper synthesizes and builds on recent critiques of the resilience literature; namely that the field has largely been unsuccessful in capturing the complexity of governance processes, in particular cause–effects relationships. We demonstrate that absence of a causal model is reflected in the
System Dynamics Modeling for the Resilience in Nuclear Power Plants
International Nuclear Information System (INIS)
Florah, Kamanj; Kim, Jonghyun
2013-01-01
This paper aims to model and evaluate emergency operation system (EOS) resilience using the System Dynamics. System Dynamics is the study of causal interactions between elements of a complex system. This paper identifies the EOS resilience attributes and their interactions by constructing a causal loop diagram. Then, the interactions are quantified based on literature review and simulated to analyze resilience dynamics. This paper describes the use of system dynamics to improve understanding of the resilience dynamics of complex systems such as emergency operation systems. This paper takes into account two aspects; the strength of resilience attributes interactions and the quantification of dynamic behaviour of resilience over time. This model can be applied to review NPP safety in terms of the resilience level and organization. Simulation results can give managers insights to support their decisions in safety management. A nuclear power plant (NPP) is classified as a safety critical organization whose safety objective is to control hazards that can cause significant harm to the environment, public, or personnel. There has been a significant improvement of safety designs as well as risk analysis tools and methods applied in nuclear power plants over the last decade. Conventional safety analysis methods such as PSA have several limitations they primarily focus on technical dimension, the analysis are linear and sequential, they are dominated by static models, they do not take a systemic view into account, and they focus primarily on why accidents happen and not how success is achieved. Hence new approaches to risk analysis for NPPs are needed to complement the conventional approaches. Resilience is the intrinsic ability of a system to adjust to its functioning prior to, during, or following changes and disturbances, so that it can sustain required operations under both expected and unexpected conditions. An EOS in a NPP refers to a system consisting of personnel
System Dynamics Modeling for the Resilience in Nuclear Power Plants
Energy Technology Data Exchange (ETDEWEB)
Florah, Kamanj; Kim, Jonghyun [KEPCO International Nuclear Graduate School, Ulsan (Korea, Republic of)
2013-10-15
This paper aims to model and evaluate emergency operation system (EOS) resilience using the System Dynamics. System Dynamics is the study of causal interactions between elements of a complex system. This paper identifies the EOS resilience attributes and their interactions by constructing a causal loop diagram. Then, the interactions are quantified based on literature review and simulated to analyze resilience dynamics. This paper describes the use of system dynamics to improve understanding of the resilience dynamics of complex systems such as emergency operation systems. This paper takes into account two aspects; the strength of resilience attributes interactions and the quantification of dynamic behaviour of resilience over time. This model can be applied to review NPP safety in terms of the resilience level and organization. Simulation results can give managers insights to support their decisions in safety management. A nuclear power plant (NPP) is classified as a safety critical organization whose safety objective is to control hazards that can cause significant harm to the environment, public, or personnel. There has been a significant improvement of safety designs as well as risk analysis tools and methods applied in nuclear power plants over the last decade. Conventional safety analysis methods such as PSA have several limitations they primarily focus on technical dimension, the analysis are linear and sequential, they are dominated by static models, they do not take a systemic view into account, and they focus primarily on why accidents happen and not how success is achieved. Hence new approaches to risk analysis for NPPs are needed to complement the conventional approaches. Resilience is the intrinsic ability of a system to adjust to its functioning prior to, during, or following changes and disturbances, so that it can sustain required operations under both expected and unexpected conditions. An EOS in a NPP refers to a system consisting of personnel
Vehicle dynamics modeling and simulation
Schramm, Dieter; Bardini, Roberto
2014-01-01
The authors examine in detail the fundamentals and mathematical descriptions of the dynamics of automobiles. In this context different levels of complexity will be presented, starting with basic single-track models up to complex three-dimensional multi-body models. A particular focus is on the process of establishing mathematical models on the basis of real cars and the validation of simulation results. The methods presented are explained in detail by means of selected application scenarios.
The System Dynamics Model for Development of Organic Agriculture
Rozman, Črtomir; Škraba, Andrej; Kljajić, Miroljub; Pažek, Karmen; Bavec, Martina; Bavec, Franci
2008-10-01
Organic agriculture is the highest environmentally valuable agricultural system, and has strategic importance at national level that goes beyond the interests of agricultural sector. In this paper we address development of organic farming simulation model based on a system dynamics methodology (SD). The system incorporates relevant variables, which affect the development of the organic farming. The group decision support system (GDSS) was used in order to identify most relevant variables for construction of causal loop diagram and further model development. The model seeks answers to strategic questions related to the level of organically utilized area, levels of production and crop selection in a long term dynamic context and will be used for simulation of different policy scenarios for organic farming and their impact on economic and environmental parameters of organic production at an aggregate level.
Horvath, C.; Leeflang, P.S.H.; Otter, P.W.
Dynamic multivariate models ha e become popular in analyzing the behavior of competitive marketing systems because they are capable of incorporating all the relationships in a competitive marketing environment. In this paper we consider VAR models, the most frequently used dynamic multivariate
Mejía-Aranguré, Juan Manuel
Acute leukemias have a huge morphological, cytogenetic and molecular heterogeneity and genetic polymorphisms associated with susceptibility. Every leukemia presents causal factors associated with the development of the disease. Particularly, when three factors are present, they result in the development of acute leukemia. These phenomena are susceptibility, environmental exposure and a period that, for this model, has been called the period of vulnerability. This framework shows how the concepts of molecular epidemiology have established a reference from which it is more feasible to identify the environmental factors associated with the development of leukemia in children. Subsequently, the arguments show that only susceptible children are likely to develop leukemia once exposed to an environmental factor. For additional exposure, if the child is not susceptible to leukemia, the disease does not develop. In addition, this exposure should occur during a time window when hematopoietic cells and their environment are more vulnerable to such interaction, causing the development of leukemia. This model seeks to predict the time when the leukemia develops and attempts to give a context in which the causality of childhood leukemia should be studied. This information can influence and reduce the risk of a child developing leukemia. Copyright © 2016 Hospital Infantil de México Federico Gómez. Publicado por Masson Doyma México S.A. All rights reserved.
System dynamics and control with bond graph modeling
Kypuros, Javier
2013-01-01
Part I Dynamic System ModelingIntroduction to System DynamicsIntroductionSystem Decomposition and Model ComplexityMathematical Modeling of Dynamic SystemsAnalysis and Design of Dynamic SystemsControl of Dynamic SystemsDiagrams of Dynamic SystemsA Graph-Centered Approach to ModelingSummaryPracticeExercisesBasic Bond Graph ElementsIntroductionPower and Energy VariablesBasic 1-Port ElementsBasic 2-Ports ElementsJunction ElementsSimple Bond Graph ExamplesSummaryPracticeExercisesBond Graph Synthesis and Equation DerivationIntroductionGeneral GuidelinesMechanical TranslationMechanical RotationElectrical CircuitsHydraulic CircuitsMixed SystemsState Equation DerivationState-Space RepresentationsAlgebraic Loops and Derivative CausalitySummaryPracticeExercisesImpedance Bond GraphsIntroductionLaplace Transform of the State-Space EquationBasic 1-Port ImpedancesImpedance Bond Graph SynthesisJunctions, Transformers, and GyratorsEffort and Flow DividersSign ChangesTransfer Function DerivationAlternative Derivation of Transf...
Containing Terrorism: A Dynamic Model
Directory of Open Access Journals (Sweden)
Giti Zahedzadeh
2017-06-01
Full Text Available The strategic interplay between counterterror measures and terror activity is complex. Herein, we propose a dynamic model to depict this interaction. The model generates stylized prognoses: (i under conditions of inefficient counterterror measures, terror groups enjoy longer period of activity but only if recruitment into terror groups remains low; high recruitment shortens the period of terror activity (ii highly efficient counterterror measures effectively contain terror activity, but only if recruitment remains low. Thus, highly efficient counterterror measures can effectively contain terrorism if recruitment remains restrained. We conclude that the trajectory of the dynamics between counterterror measures and terror activity is heavily altered by recruitment.
A dynamical model of terrorism
Directory of Open Access Journals (Sweden)
Firdaus Udwadia
2006-01-01
Full Text Available This paper develops a dynamical model of terrorism. We consider the population in a given region as being made up of three primary components: terrorists, those susceptible to both terrorist and pacifist propaganda, and nonsusceptibles, or pacifists. The dynamical behavior of these three populations is studied using a model that incorporates the effects of both direct military/police intervention to reduce the terrorist population, and nonviolent, persuasive intervention to influence the susceptibles to become pacifists. The paper proposes a new paradigm for studying terrorism, and looks at the long-term dynamical evolution in time of these three population components when such interventions are carried out. Many important features—some intuitive, others not nearly so—of the nature of terrorism emerge from the dynamical model proposed, and they lead to several important policy implications for the management of terrorism. The different circumstances in which nonviolent intervention and/or military/police intervention may be beneficial, and the specific conditions under which each mode of intervention, or a combination of both, may be useful, are obtained. The novelty of the model presented herein is that it deals with the time evolution of terrorist activity. It appears to be one of the few models that can be tested, evaluated, and improved upon, through the use of actual field data.
Directory of Open Access Journals (Sweden)
Thanyatorn Amornkitpinyo
2015-02-01
Full Text Available The objective of this study is to design a framework for a causal relationship model of the Information and Communication Technology skills that affect the Technology Acceptance Process (TAP for undergraduate students in the 21ST Century. This research uses correlational analysis. A consideration of the research methodology is divided into two sections. The first section involves a synthesis concept framework for process acceptance of the causal relationship model of the Information and Communication Technology skills that affect the Technology Acceptance Process for undergraduate students in the 21ST Century. The second section proposes the design concept framework of the model. The research findings are as follows: 1 The exogenous latent variables included in the causal relationship model of the Information and Communication Technology skills that affect the Technology Acceptance Process for undergraduate students in the 21ST Century are basic ICT skills and self-efficacy. 2 The mediating latent variables of the causal relationship model of the Information and Communication Technology skills that affect the Technology Acceptance Process for undergraduate students in the 21ST Century are from the TAM Model, these includes three components: 1 perceived usefulness, 2 perceived ease of use and 3 attitudes. 3 The outcome latent variable of the causal relationship model of the Information and Communication Technology skills that affect the Technology Acceptance Process for undergraduate students in the 21ST Century is behavioural intention.
THE CAUSALITY TEST BETWEEN THE VARIANCES OF SPOT AND FUTURE MARKET PRICES
Directory of Open Access Journals (Sweden)
EMRAH İSMAİL ÇEVİK
2013-06-01
Full Text Available Volatility in financial markets urges importance of risk management with respect to investors and especially firms. Information and interaction between spot and futures markets plays an important role on formation of market prices. In this study, causality and information flows are examined on spot and futures prices of ISE 100 Index, US Dollar, and Euro which are traded at Turkish Derivatives Exchange (VOB. Dynamic causality test that is originally created by Cheung and Ng (1996 is applied. Dynamic causality test results show that in the ISE 100 Index model spot prices affect futures prices and in the exchange model futures prices affect spot prices.
International Nuclear Information System (INIS)
Zhang, Chuanguo; Xu, Jiao
2012-01-01
The increasing attention on energy policy needs has provided a renewed stimulus to research the linkages between energy consumption and economic performance in China. This paper examined the causal relationship between energy consumption and economic growth in the regional and sectoral aspects by adopting provincial panel data in China from 1995 to 2008. The results indicate that economic growth causes more energy consumption in China not only at the national level but also at the regional and sectoral levels. Then the Eastern Region and the industrial sector show results quite similar to that of the whole country, in which a bidirectional causality relationship exists between energy consumption and economic growth. The implication for energy policies in China is that the Eastern Region and the industrial sector should play a leading role in the adjustment of energy consumption patterns and the transformation of the economy structure. Energy prices have limited effects on energy consumption but do have effects on economic growth because the energy price mechanism is more government-oriented than market-oriented in China.
Generative Models of Conformational Dynamics
Langmead, Christopher James
2014-01-01
Atomistic simulations of the conformational dynamics of proteins can be performed using either Molecular Dynamics or Monte Carlo procedures. The ensembles of three-dimensional structures produced during simulation can be analyzed in a number of ways to elucidate the thermodynamic and kinetic properties of the system. The goal of this chapter is to review both traditional and emerging methods for learning generative models from atomistic simulation data. Here, the term ‘generative’ refers to a model of the joint probability distribution over the behaviors of the constituent atoms. In the context of molecular modeling, generative models reveal the correlation structure between the atoms, and may be used to predict how the system will respond to structural perturbations. We begin by discussing traditional methods, which produce multivariate Gaussian models. We then discuss GAMELAN (GrAphical Models of Energy LANdscapes), which produces generative models of complex, non-Gaussian conformational dynamics (e.g., allostery, binding, folding, etc) from long timescale simulation data. PMID:24446358
Experimental Modeling of Dynamic Systems
DEFF Research Database (Denmark)
Knudsen, Morten Haack
2006-01-01
An engineering course, Simulation and Experimental Modeling, has been developed that is based on a method for direct estimation of physical parameters in dynamic systems. Compared with classical system identification, the method appears to be easier to understand, apply, and combine with physical...
Paradoxical Behavior of Granger Causality
Witt, Annette; Battaglia, Demian; Gail, Alexander
2013-03-01
Granger causality is a standard tool for the description of directed interaction of network components and is popular in many scientific fields including econometrics, neuroscience and climate science. For time series that can be modeled as bivariate auto-regressive processes we analytically derive an expression for spectrally decomposed Granger Causality (SDGC) and show that this quantity depends only on two out of four groups of model parameters. Then we present examples of such processes whose SDGC expose paradoxical behavior in the sense that causality is high for frequency ranges with low spectral power. For avoiding misinterpretations of Granger causality analysis we propose to complement it by partial spectral analysis. Our findings are illustrated by an example from brain electrophysiology. Finally, we draw implications for the conventional definition of Granger causality. Bernstein Center for Computational Neuroscience Goettingen
Sustainable Deforestation Evaluation Model and System Dynamics Analysis
Feng, Huirong; Lim, C. W.; Chen, Liqun; Zhou, Xinnian; Zhou, Chengjun; Lin, Yi
2014-01-01
The current study used the improved fuzzy analytic hierarchy process to construct a sustainable deforestation development evaluation system and evaluation model, which has refined a diversified system to evaluate the theory of sustainable deforestation development. Leveraging the visual image of the system dynamics causal and power flow diagram, we illustrated here that sustainable forestry development is a complex system that encompasses the interaction and dynamic development of ecology, economy, and society and has reflected the time dynamic effect of sustainable forestry development from the three combined effects. We compared experimental programs to prove the direct and indirect impacts of the ecological, economic, and social effects of the corresponding deforest techniques and fully reflected the importance of developing scientific and rational ecological harvesting and transportation technologies. Experimental and theoretical results illustrated that light cableway skidding is an ecoskidding method that is beneficial for the sustainable development of resources, the environment, the economy, and society and forecasted the broad potential applications of light cableway skidding in timber production technology. Furthermore, we discussed the sustainable development countermeasures of forest ecosystems from the aspects of causality, interaction, and harmony. PMID:25254225
Sustainable deforestation evaluation model and system dynamics analysis.
Feng, Huirong; Lim, C W; Chen, Liqun; Zhou, Xinnian; Zhou, Chengjun; Lin, Yi
2014-01-01
The current study used the improved fuzzy analytic hierarchy process to construct a sustainable deforestation development evaluation system and evaluation model, which has refined a diversified system to evaluate the theory of sustainable deforestation development. Leveraging the visual image of the system dynamics causal and power flow diagram, we illustrated here that sustainable forestry development is a complex system that encompasses the interaction and dynamic development of ecology, economy, and society and has reflected the time dynamic effect of sustainable forestry development from the three combined effects. We compared experimental programs to prove the direct and indirect impacts of the ecological, economic, and social effects of the corresponding deforest techniques and fully reflected the importance of developing scientific and rational ecological harvesting and transportation technologies. Experimental and theoretical results illustrated that light cableway skidding is an ecoskidding method that is beneficial for the sustainable development of resources, the environment, the economy, and society and forecasted the broad potential applications of light cableway skidding in timber production technology. Furthermore, we discussed the sustainable development countermeasures of forest ecosystems from the aspects of causality, interaction, and harmony.
Sustainable Deforestation Evaluation Model and System Dynamics Analysis
Directory of Open Access Journals (Sweden)
Huirong Feng
2014-01-01
Full Text Available The current study used the improved fuzzy analytic hierarchy process to construct a sustainable deforestation development evaluation system and evaluation model, which has refined a diversified system to evaluate the theory of sustainable deforestation development. Leveraging the visual image of the system dynamics causal and power flow diagram, we illustrated here that sustainable forestry development is a complex system that encompasses the interaction and dynamic development of ecology, economy, and society and has reflected the time dynamic effect of sustainable forestry development from the three combined effects. We compared experimental programs to prove the direct and indirect impacts of the ecological, economic, and social effects of the corresponding deforest techniques and fully reflected the importance of developing scientific and rational ecological harvesting and transportation technologies. Experimental and theoretical results illustrated that light cableway skidding is an ecoskidding method that is beneficial for the sustainable development of resources, the environment, the economy, and society and forecasted the broad potential applications of light cableway skidding in timber production technology. Furthermore, we discussed the sustainable development countermeasures of forest ecosystems from the aspects of causality, interaction, and harmony.
Schnitzer, Mireille E.; Lok, Judith J.; Gruber, Susan
2015-01-01
This paper investigates the appropriateness of the integration of flexible propensity score modeling (nonparametric or machine learning approaches) in semiparametric models for the estimation of a causal quantity, such as the mean outcome under treatment. We begin with an overview of some of the issues involved in knowledge-based and statistical variable selection in causal inference and the potential pitfalls of automated selection based on the fit of the propensity score. Using a simple example, we directly show the consequences of adjusting for pure causes of the exposure when using inverse probability of treatment weighting (IPTW). Such variables are likely to be selected when using a naive approach to model selection for the propensity score. We describe how the method of Collaborative Targeted minimum loss-based estimation (C-TMLE; van der Laan and Gruber, 2010) capitalizes on the collaborative double robustness property of semiparametric efficient estimators to select covariates for the propensity score based on the error in the conditional outcome model. Finally, we compare several approaches to automated variable selection in low-and high-dimensional settings through a simulation study. From this simulation study, we conclude that using IPTW with flexible prediction for the propensity score can result in inferior estimation, while Targeted minimum loss-based estimation and C-TMLE may benefit from flexible prediction and remain robust to the presence of variables that are highly correlated with treatment. However, in our study, standard influence function-based methods for the variance underestimated the standard errors, resulting in poor coverage under certain data-generating scenarios. PMID:26226129
A theory of causal learning in children: causal maps and Bayes nets.
Gopnik, Alison; Glymour, Clark; Sobel, David M; Schulz, Laura E; Kushnir, Tamar; Danks, David
2004-01-01
The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism.
System Dynamics Modeling for Emergency Operating System Resilience
Energy Technology Data Exchange (ETDEWEB)
Eng, Ang Wei; Kim, Jong Hyun [KEPCO International Nuclear Graduate School, Ulsan (Korea, Republic of)
2014-10-15
The purpose of this paper is to present a causal model which explain human error cause-effect relationships of emergency operating system (EOS) by using system dynamics (SD) approach. The causal model will further quantified by analyzes nuclear power plant incidents/accidents data in Korea for simulation modeling. Emergency Operating System (EOS) is generally defined as a system which consists personnel, human-machine interface and procedures; and how these components interact and coordinate to respond to an incident or accident. Understanding the behavior of EOS especially personnel behavior and the factors influencing it during accident will contribute in human reliability evaluation. Human Reliability Analysis (HRA) is a method which assesses how human decisions and actions affect to system risk and further used to reduce the human errors probability. There are many HRA method used performance influencing factors (PIFs) to identify the causes of human errors. However, these methods have several limitations. In HRA, PIFs are assumed independent each other and relationship between them are not been study. Through the SD simulation, users able to simulate various situation of nuclear power plant respond to emergency from human and organizational aspects. The simulation also provides users a comprehensive view on how to improve the safety in plants. This paper presents a causal model that explained cause-effect relationships of EOS human. Through SD simulation, users able to identify the main contribution of human error easily. Users can also use SD simulation to predict when and how a human error occurs over time. In future work, the SD model can be expanded more on low level factors. The relationship within low level factors can investigated by using correlation method and further included in the model. This can enables users to study more detailed human error cause-effect relationships and the behavior of EOS. Another improvement can be made is on EOS factors
System Dynamics Modeling for Emergency Operating System Resilience
International Nuclear Information System (INIS)
Eng, Ang Wei; Kim, Jong Hyun
2014-01-01
The purpose of this paper is to present a causal model which explain human error cause-effect relationships of emergency operating system (EOS) by using system dynamics (SD) approach. The causal model will further quantified by analyzes nuclear power plant incidents/accidents data in Korea for simulation modeling. Emergency Operating System (EOS) is generally defined as a system which consists personnel, human-machine interface and procedures; and how these components interact and coordinate to respond to an incident or accident. Understanding the behavior of EOS especially personnel behavior and the factors influencing it during accident will contribute in human reliability evaluation. Human Reliability Analysis (HRA) is a method which assesses how human decisions and actions affect to system risk and further used to reduce the human errors probability. There are many HRA method used performance influencing factors (PIFs) to identify the causes of human errors. However, these methods have several limitations. In HRA, PIFs are assumed independent each other and relationship between them are not been study. Through the SD simulation, users able to simulate various situation of nuclear power plant respond to emergency from human and organizational aspects. The simulation also provides users a comprehensive view on how to improve the safety in plants. This paper presents a causal model that explained cause-effect relationships of EOS human. Through SD simulation, users able to identify the main contribution of human error easily. Users can also use SD simulation to predict when and how a human error occurs over time. In future work, the SD model can be expanded more on low level factors. The relationship within low level factors can investigated by using correlation method and further included in the model. This can enables users to study more detailed human error cause-effect relationships and the behavior of EOS. Another improvement can be made is on EOS factors
A theory of causal learning in children: Causal maps and Bayes nets
Gopnik, A; Glymour, C; Sobel, D M; Schulz, L E; Kushnir, T; Danks, D
2004-01-01
The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computatio...
Wang, Wei; Nelson, Suchitra; Albert, Jeffrey M
2013-10-30
Mediators are intermediate variables in the causal pathway between an exposure and an outcome. Mediation analysis investigates the extent to which exposure effects occur through these variables, thus revealing causal mechanisms. In this paper, we consider the estimation of the mediation effect when the outcome is binary and multiple mediators of different types exist. We give a precise definition of the total mediation effect as well as decomposed mediation effects through individual or sets of mediators using the potential outcomes framework. We formulate a model of joint distribution (probit-normal) using continuous latent variables for any binary mediators to account for correlations among multiple mediators. A mediation formula approach is proposed to estimate the total mediation effect and decomposed mediation effects based on this parametric model. Estimation of mediation effects through individual or subsets of mediators requires an assumption involving the joint distribution of multiple counterfactuals. We conduct a simulation study that demonstrates low bias of mediation effect estimators for two-mediator models with various combinations of mediator types. The results also show that the power to detect a nonzero total mediation effect increases as the correlation coefficient between two mediators increases, whereas power for individual mediation effects reaches a maximum when the mediators are uncorrelated. We illustrate our approach by applying it to a retrospective cohort study of dental caries in adolescents with low and high socioeconomic status. Sensitivity analysis is performed to assess the robustness of conclusions regarding mediation effects when the assumption of no unmeasured mediator-outcome confounders is violated. Copyright © 2013 John Wiley & Sons, Ltd.
Nelson, Suchitra; Albert, Jeffrey M.
2013-01-01
Mediators are intermediate variables in the causal pathway between an exposure and an outcome. Mediation analysis investigates the extent to which exposure effects occur through these variables, thus revealing causal mechanisms. In this paper, we consider the estimation of the mediation effect when the outcome is binary and multiple mediators of different types exist. We give a precise definition of the total mediation effect as well as decomposed mediation effects through individual or sets of mediators using the potential outcomes framework. We formulate a model of joint distribution (probit-normal) using continuous latent variables for any binary mediators to account for correlations among multiple mediators. A mediation formula approach is proposed to estimate the total mediation effect and decomposed mediation effects based on this parametric model. Estimation of mediation effects through individual or subsets of mediators requires an assumption involving the joint distribution of multiple counterfactuals. We conduct a simulation study that demonstrates low bias of mediation effect estimators for two-mediator models with various combinations of mediator types. The results also show that the power to detect a non-zero total mediation effect increases as the correlation coefficient between two mediators increases, while power for individual mediation effects reaches a maximum when the mediators are uncorrelated. We illustrate our approach by applying it to a retrospective cohort study of dental caries in adolescents with low and high socioeconomic status. Sensitivity analysis is performed to assess the robustness of conclusions regarding mediation effects when the assumption of no unmeasured mediator-outcome confounders is violated. PMID:23650048
Falasca, N W; D'Ascenzo, S; Di Domenico, A; Onofrj, M; Tommasi, L; Laeng, B; Franciotti, R
2015-04-01
Magnetoencephalography was recorded during a matching-to-sample plus cueing paradigm, in which participants judged the occurrence of changes in either categorical (CAT) or coordinate (COO) spatial relations. Previously, parietal and frontal lobes were identified as key areas in processing spatial relations and it was shown that each hemisphere was differently involved and modulated by the scope of the attention window (e.g. a large and small cue). In this study, Granger analysis highlighted the patterns of causality among involved brain areas--the direction of information transfer ran from the frontal to the visual cortex in the right hemisphere, whereas it ran in the opposite direction in the left side. Thus, the right frontal area seems to exert top-down influence, supporting the idea that, in this task, top-down signals are selectively related to the right side. Additionally, for CAT change preceded by a small cue, the right frontal gyrus was not involved in the information transfer, indicating a selective specialization of the left hemisphere for this condition. The present findings strengthen the conclusion of the presence of a remarkable hemispheric specialization for spatial relation processing and illustrate the complex interactions between the lateralized parts of the neural network. Moreover, they illustrate how focusing attention over large or small regions of the visual field engages these lateralized networks differently, particularly in the frontal regions of each hemisphere, consistent with the theory that spatial relation judgements require a fronto-parietal network in the left hemisphere for categorical relations and on the right hemisphere for coordinate spatial processing. © 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.
Business model dynamics and innovation
DEFF Research Database (Denmark)
Cavalcante, Sergio Andre; Kesting, Peter; Ulhøi, John Parm
2011-01-01
the impact of specific changes to a firm's business model. Such a tool would be particularly useful in identifying path dependencies and resistance at the process level, and would therefore allow a firm's management to take focused action on this in advance. Originality/value – The paper makes two main...... and specifies four different types of business model change: business model creation, extension, revision, and termination. Each type of business model change is associated with specific challenges. Practical implications – The proposed typology can serve as a basis for developing a management tool to evaluate......Purpose – This paper aims to discuss the need to dynamize the existing conceptualization of business model, and proposes a new typology to distinguish different types of business model change. Design/methodology/approach – The paper integrates basic insights of innovation, business process...
On whole Abelian model dynamics
Energy Technology Data Exchange (ETDEWEB)
Chauca, J.; Doria, R. [CBPF, Rio de Janeiro (Brazil); Aprendanet, Petropolis, 25600 (Brazil)
2012-09-24
Physics challenge is to determine the objects dynamics. However, there are two ways for deciphering the part. The first one is to search for the ultimate constituents; the second one is to understand its behaviour in whole terms. Therefore, the parts can be defined either from elementary constituents or as whole functions. Historically, science has been moving through the first aspect, however, quarks confinement and complexity are interrupting this usual approach. These relevant facts are supporting for a systemic vision be introduced. Our effort here is to study on the whole meaning through gauge theory. Consider a systemic dynamics oriented through the U(1) - systemic gauge parameter which function is to collect a fields set {l_brace}A{sub {mu}I}{r_brace}. Derive the corresponding whole gauge invariant Lagrangian, equations of motion, Bianchi identities, Noether relationships, charges and Ward-Takahashi equations. Whole Lorentz force and BRST symmetry are also studied. These expressions bring new interpretations further than the usual abelian model. They are generating a systemic system governed by 2N+ 10 classical equations plus Ward-Takahashi identities. A whole dynamics based on the notions of directive and circumstance is producing a set determinism where the parts dynamics are inserted in the whole evolution. A dynamics based on state, collective and individual equations with a systemic interdependence.
Relating structure and dynamics in organisation models
Jonkers, C.M.; Treur, J.
2002-01-01
To understand how an organisational structure relates to dynamics is an interesting fundamental challenge in the area of social modelling. Specifications of organisational structure usually have a diagrammatic form that abstracts from more detailed dynamics. Dynamic properties of agent systems,
Modelling MIZ dynamics in a global model
Rynders, Stefanie; Aksenov, Yevgeny; Feltham, Daniel; Nurser, George; Naveira Garabato, Alberto
2016-04-01
Exposure of large, previously ice-covered areas of the Arctic Ocean to the wind and surface ocean waves results in the Arctic pack ice cover becoming more fragmented and mobile, with large regions of ice cover evolving into the Marginal Ice Zone (MIZ). The need for better climate predictions, along with growing economic activity in the Polar Oceans, necessitates climate and forecasting models that can simulate fragmented sea ice with a greater fidelity. Current models are not fully fit for the purpose, since they neither model surface ocean waves in the MIZ, nor account for the effect of floe fragmentation on drag, nor include sea ice rheology that represents both the now thinner pack ice and MIZ ice dynamics. All these processes affect the momentum transfer to the ocean. We present initial results from a global ocean model NEMO (Nucleus for European Modelling of the Ocean) coupled to the Los Alamos sea ice model CICE. The model setup implements a novel rheological formulation for sea ice dynamics, accounting for ice floe collisions, thus offering a seamless framework for pack ice and MIZ simulations. The effect of surface waves on ice motion is included through wave pressure and the turbulent kinetic energy of ice floes. In the multidecadal model integrations we examine MIZ and basin scale sea ice and oceanic responses to the changes in ice dynamics. We analyse model sensitivities and attribute them to key sea ice and ocean dynamical mechanisms. The results suggest that the effect of the new ice rheology is confined to the MIZ. However with the current increase in summer MIZ area, which is projected to continue and may become the dominant type of sea ice in the Arctic, we argue that the effects of the combined sea ice rheology will be noticeable in large areas of the Arctic Ocean, affecting sea ice and ocean. With this study we assert that to make more accurate sea ice predictions in the changing Arctic, models need to include MIZ dynamics and physics.
Social Dynamics Modeling and Inference
2018-03-29
the experiment(s)/ theory and equipment or analyses. Development of innovative theoretical model and methodologies with experimental verifications...information. The methodology based on communication and information theory (thanks to leave at MIT supported by this research) is described in [J1], [C2...a dynamic system [C1] and as a social learning mechanism in details [J4]. Furthermore, by incentive seeding and rewiring connections, information
Behavioural Pattern of Causality Parameter of Autoregressive ...
African Journals Online (AJOL)
In this paper, a causal form of Autoregressive Moving Average process, ARMA (p, q) of various orders and behaviour of the causality parameter of ARMA model is investigated. It is deduced that the behaviour of causality parameter ψi depends on positive and negative values of autoregressive parameter φ and moving ...
Representing Personal Determinants in Causal Structures.
Bandura, Albert
1984-01-01
Responds to Staddon's critique of the author's earlier article and addresses issues raised by Staddon's (1984) alternative models of causality. The author argues that it is not the formalizability of causal processes that is the issue but whether cognitive determinants of behavior are reducible to past stimulus inputs in causal structures.…
System dynamics models as decision-making tools in agritourism
Directory of Open Access Journals (Sweden)
Jere Jakulin Tadeja
2016-12-01
Full Text Available Agritourism as a type of niche tourism is a complex and softly defined phaenomenon. The demands for fast and integrated decision regarding agritourism and its interconnections with environment, economy (investments, traffic and social factors (tourists is urgent. Many different methodologies and methods master softly structured questions and dilemmas with global and local properties. Here we present methods of systems thinking and system dynamics, which were first brought into force in the educational and training area in the form of different computer simulations and later as tools for decision-making and organisational re-engineering. We develop system dynamics models in order to present accuracy of methodology. These models are essentially simple and can serve only as describers of the activity of basic mutual influences among variables. We will pay the attention to the methodology for parameter model values determination and the so-called mental model. This one is the basis of causal connections among model variables. At the end, we restore a connection between qualitative and quantitative models in frame of system dynamics.
Hattori, Masasi; Oaksford, Mike
2007-01-01
In this article, 41 models of covariation detection from 2 x 2 contingency tables were evaluated against past data in the literature and against data from new experiments. A new model was also included based on a limiting case of the normative phi-coefficient under an extreme rarity assumption, which has been shown to be an important factor in…
Berger, Joseph B.; Milem, Jeffrey F.
1999-01-01
This study refined and applied an integrated model of undergraduate persistence (accounting for both behavioral and perceptual components) to examine first-year retention at a private, highly selective research university. Results suggest that including behaviorally based measures of involvement improves the model's explanatory power concerning…
Capturing connectivity and causality in complex industrial processes
Yang, Fan; Shah, Sirish L; Chen, Tongwen
2014-01-01
This brief reviews concepts of inter-relationship in modern industrial processes, biological and social systems. Specifically ideas of connectivity and causality within and between elements of a complex system are treated; these ideas are of great importance in analysing and influencing mechanisms, structural properties and their dynamic behaviour, especially for fault diagnosis and hazard analysis. Fault detection and isolation for industrial processes being concerned with root causes and fault propagation, the brief shows that, process connectivity and causality information can be captured in two ways: · from process knowledge: structural modeling based on first-principles structural models can be merged with adjacency/reachability matrices or topology models obtained from process flow-sheets described in standard formats; and · from process data: cross-correlation analysis, Granger causality and its extensions, frequency domain methods, information-theoretical methods, and Bayesian ne...
Classical planning and causal implicatures
DEFF Research Database (Denmark)
Blackburn, Patrick Rowan; Benotti, Luciana
In this paper we motivate and describe a dialogue manager (called Frolog) which uses classical planning to infer causal implicatures. A causal implicature is a type of Gricean relation implicature, a highly context dependent form of inference. As we shall see, causal implicatures are important...... to generate clarification requests"; as a result we can model task-oriented dialogue as an interactive process locally structured by negotiation of the underlying task. We give several examples of Frolog-human dialog, discuss the limitations imposed by the classical planning paradigm, and indicate...
Multiscale modeling of pedestrian dynamics
Cristiani, Emiliano; Tosin, Andrea
2014-01-01
This book presents mathematical models and numerical simulations of crowd dynamics. The core topic is the development of a new multiscale paradigm, which bridges the microscopic and macroscopic scales taking the most from each of them for capturing the relevant clues of complexity of crowds. The background idea is indeed that most of the complex trends exhibited by crowds are due to an intrinsic interplay between individual and collective behaviors. The modeling approach promoted in this book pursues actively this intuition and profits from it for designing general mathematical structures susceptible of application also in fields different from the inspiring original one. The book considers also the two most traditional points of view: the microscopic one, in which pedestrians are tracked individually, and the macroscopic one, in which pedestrians are assimilated to a continuum. Selected existing models are critically analyzed. The work is addressed to researchers and graduate students.
Directory of Open Access Journals (Sweden)
Paula Medina Maçaira
Full Text Available ABSTRACT The Brazilian electricity energy matrix is essentially formed by hydraulic sources which currently account for 70% of the installed capacity. One of the most important characteristics of a generation system with hydro predominance is the strong dependence on the inflow regimes. Nowadays, the Brazilian power sector uses the PAR(p model to generate scenarios for hydrological inflows. This approach does not consider any exogenous information that may affect hydrological regimes. The main objective of this paper is to infer on the influence of climatic events in water inflows as a way to improve the model’s performance. The proposed model is called “causal PAR(p” and considers exogenous variables, such as El Niño and Sunspots, to generate scenarios for some Brazilian reservoirs. The result shows that the error measures decrease approximately 3%. This improvement indicates that the inclusion of climate variables to model and simulate the inflows time series is a valid exercise and should be taken into consideration.
New Product Development and Innovation in the Maquiladora Industry: A Causal Model
Directory of Open Access Journals (Sweden)
Jorge Luis García-Alcaraz
2016-07-01
Full Text Available Companies seek to stand out from their competitors and react to other competitive threats. Making a difference means doing things differently in order to create a product that other companies cannot provide. This can be achieved through an innovation process. This article analyses, by means of a structural equation model, the current situation of Mexican maquiladora companies, which face the constant challenge of product innovation. The model associates three success factors for new product development (product, organization, and production process characteristics as independent latent variables with benefits gained by customers and companies (dependent latent variables. Results show that, in the Mexican maquiladora sector, organizational characteristics and production processes characteristics explain only 31% of the variability (R2 = 0.31, and it seems necessary to integrate other aspects. The relationship between customer benefits and company benefits explains 58% of the variability, the largest proportion in the model (R2 = 0.58.
Directory of Open Access Journals (Sweden)
Antonio Belmonte
2018-04-01
Full Text Available This paper presents a new algorithm for the resolution of over-constrained lumped process systems, where partial differential equations of a continuous time and space model of the system are reduced into ordinary differential equations with a finite number of parameters and where the model equations outnumber the unknown model variables. Our proposal is aimed at the study and improvement of the algorithm proposed by Hangos-Szerkenyi-Tuza. This new algorithm improves the computational cost and solves some of the internal problems of the aforementioned algorithm in its original formulation. The proposed algorithm is based on parameter relaxation that can be modified easily. It retains the necessary information of the lumped process system to reduce the time cost after introducing changes during the system formulation. It also allows adjustment of the system formulations that change its differential index between simulations.
Ren, Su-Li; Li, Yi-Han; Ou, Da; Guo, Yan-Jun; Qureshi, Jawwad A; Stansly, Philip A; Qiu, Bao-Li
2018-03-23
Wolbachia is a group of intracellular bacteria that infect a wide range of arthropods including the Asian citrus psyllid (ACP), Diaphorina citri Kuwayama. This insect is the vector of Candidatus Liberibacter asiaticus (CLas), the causal pathogen of Huanglongbing or citrus greening disease. Here, we investigated the localization pattern and infection dynamics of Wolbachia in different developmental stages of ACP. Results revealed that all developmental stages of ACP including egg, 1st-5th instar nymphs, and adults of both gender were infected with Wolbachia. FISH visualization of an ACP egg showed that Wolbachia moved from the egg stalk of newly laid eggs to a randomly distributed pattern throughout the egg prior to hatching. The infection rate varied between nymphal instars. The titers of Wolbachia in fourth and fifth instar nymphs were significantly higher than those in the first and second instar nymphs. Wolbachia were scattered in all nymphal stages, but with highest intensity in the U-shaped bacteriome located in the abdomen of the nymph. Wolbachia was confined to two symmetrical organizations in the abdomen of newly emerged female and male adults. The potential mechanisms of Wolbachia infection dynamics are discussed. © 2018 The Authors. MicrobiologyOpen published by John Wiley & Sons Ltd.
Possel, Patrick; Seemann, Simone; Ahrens, Stefanie; Hautzinger, Martin
2006-01-01
In Dodge's model of "social information processing" depression is the result of a linear sequence of five stages of information processing ("Annu Rev Psychol" 44: 559-584, 1993). These stages follow a person's reaction to situational stimuli, such that each stage of information processing mediates the relationship between earlier and later stages.…
Causal Client Models in Selecting Effective Interventions: A Cognitive Mapping Study
de Kwaadsteniet, Leontien; Hagmayer, York; Krol, Nicole P. C. M.; Witteman, Cilia L. M.
2010-01-01
An important reason to choose an intervention to treat psychological problems of clients is the expectation that the intervention will be effective in alleviating the problems. The authors investigated whether clinicians base their ratings of the effectiveness of interventions on models that they construct representing the factors causing and…
Directory of Open Access Journals (Sweden)
Arup Kumar Baksi
2012-08-01
Full Text Available Information technology induced communications (ICTs have revolutionized the operational aspects of service sector and have triggered a perceptual shift in service quality as rapid dis-intermediation has changed the access-mode of services on part of the consumers. ICT-enabled services further stimulated the perception of automated service quality with renewed dimensions and there subsequent significance to influence the behavioural outcomes of the consumers. Customer Relationship Management (CRM has emerged as an offshoot to technological breakthrough as it ensured service-encapsulation by integrating people, process and technology. This paper attempts to explore the relationship between automated service quality and its behavioural consequences in a relatively novel business-philosophy – CRM. The study has been conducted on the largest public sector bank of India - State bank of India (SBI at Kolkata which has successfully completed its decade-long operational automation in the year 2008. The study used structural equation modeling (SEM to justify the proposed model construct and causal loop diagramming (CLD to depict the negative and positive linkages between the variables.
A System Dynamics Model for Planning Cardiovascular Disease Interventions
Homer, Jack; Evans, Elizabeth; Zielinski, Ann
2010-01-01
Planning programs for the prevention and treatment of cardiovascular disease (CVD) is a challenge to every community that wants to make the best use of its limited resources. Selecting programs that provide the greatest impact is difficult because of the complex set of causal pathways and delays that link risk factors to CVD. We describe a system dynamics simulation model developed for a county health department that incorporates and tracks the effects of those risk factors over time on both first-time and recurrent events. We also describe how the model was used to evaluate the potential impacts of various intervention strategies for reducing the county's CVD burden and present the results of those policy tests. PMID:20167899
Characterizing and Modeling Citation Dynamics
Eom, Young-Ho; Fortunato, Santo
2011-01-01
Citation distributions are crucial for the analysis and modeling of the activity of scientists. We investigated bibliometric data of papers published in journals of the American Physical Society, searching for the type of function which best describes the observed citation distributions. We used the goodness of fit with Kolmogorov-Smirnov statistics for three classes of functions: log-normal, simple power law and shifted power law. The shifted power law turns out to be the most reliable hypothesis for all citation networks we derived, which correspond to different time spans. We find that citation dynamics is characterized by bursts, usually occurring within a few years since publication of a paper, and the burst size spans several orders of magnitude. We also investigated the microscopic mechanisms for the evolution of citation networks, by proposing a linear preferential attachment with time dependent initial attractiveness. The model successfully reproduces the empirical citation distributions and accounts for the presence of citation bursts as well. PMID:21966387
MATHEMATICAL MODEL FOR RIVERBOAT DYNAMICS
Directory of Open Access Journals (Sweden)
Aleksander Grm
2017-01-01
Full Text Available Present work describes a simple dynamical model for riverboat motion based on the square drag law. Air and water interactions with the boat are determined from aerodynamic coefficients. CFX simulations were performed with fully developed turbulent flow to determine boat aerodynamic coefficients for an arbitrary angle of attack for the air and water portions separately. The effect of wave resistance is negligible compared to other forces. Boat movement analysis considers only two-dimensional motion, therefore only six aerodynamics coefficients are required. The proposed model is solved and used to determine the critical environmental parameters (wind and current under which river navigation can be conducted safely. Boat simulator was tested in a single area on the Ljubljanica river and estimated critical wind velocity.
Energy Technology Data Exchange (ETDEWEB)
Cipiti, Benjamin B. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2017-03-01
The Co-Decontamination (CoDCon) Demonstration project is designed to test the separation of a mixed U and Pu product from dissolved spent nuclear fuel. The primary purpose of the project is to quantify the accuracy and precision to which a U/Pu mass ratio can be achieved without removing a pure Pu product. The system includes an on-line monitoring system using spectroscopy to monitor the ratios throughout the process. A dynamic model of the CoDCon flowsheet and on-line monitoring system was developed in order to expand the range of scenarios that can be examined for process control and determine overall measurement uncertainty. The model development and initial results are presented here.
Modeling the dynamics of choice.
Baum, William M; Davison, Michael
2009-06-01
A simple linear-operator model both describes and predicts the dynamics of choice that may underlie the matching relation. We measured inter-food choice within components of a schedule that presented seven different pairs of concurrent variable-interval schedules for 12 food deliveries each with no signals indicating which pair was in force. This measure of local choice was accurately described and predicted as obtained reinforcer sequences shifted it to favor one alternative or the other. The effect of a changeover delay was reflected in one parameter, the asymptote, whereas the effect of a difference in overall rate of food delivery was reflected in the other parameter, rate of approach to the asymptote. The model takes choice as a primary dependent variable, not derived by comparison between alternatives-an approach that agrees with the molar view of behaviour.
2015-11-01
independent. The PFT model is deliberately not that of a rational actor doing cost-benefit calculations. Real individuals are affected by emotions ...use the TLWS and PF methods discussed earlier. Our quasi-Bayesian method is “quasi” because we used heuristic methods to determine the weight given...are often justified heuristically on a case-by-case basis. One way to think about the structural issues around which we had to design is to think of
From meta-omics to causality: experimental models for human microbiome research.
Fritz, Joëlle V; Desai, Mahesh S; Shah, Pranjul; Schneider, Jochen G; Wilmes, Paul
2013-05-03
Large-scale 'meta-omic' projects are greatly advancing our knowledge of the human microbiome and its specific role in governing health and disease states. A myriad of ongoing studies aim at identifying links between microbial community disequilibria (dysbiosis) and human diseases. However, due to the inherent complexity and heterogeneity of the human microbiome, cross-sectional, case-control and longitudinal studies may not have enough statistical power to allow causation to be deduced from patterns of association between variables in high-resolution omic datasets. Therefore, to move beyond reliance on the empirical method, experiments are critical. For these, robust experimental models are required that allow the systematic manipulation of variables to test the multitude of hypotheses, which arise from high-throughput molecular studies. Particularly promising in this respect are microfluidics-based in vitro co-culture systems, which allow high-throughput first-pass experiments aimed at proving cause-and-effect relationships prior to testing of hypotheses in animal models. This review focuses on widely used in vivo, in vitro, ex vivo and in silico approaches to study host-microbial community interactions. Such systems, either used in isolation or in a combinatory experimental approach, will allow systematic investigations of the impact of microbes on the health and disease of the human host. All the currently available models present pros and cons, which are described and discussed. Moreover, suggestions are made on how to develop future experimental models that not only allow the study of host-microbiota interactions but are also amenable to high-throughput experimentation.
Towards the Accuracy of Cybernetic Strategy Planning Models: Causal Proof and Function Approximation
Directory of Open Access Journals (Sweden)
Christian A. Hillbrand
2003-04-01
Full Text Available All kind of strategic tasks within an enterprise require a deep understanding of its critical key success factors and their interrelations as well as an in-depth analysis of relevant environmental influences. Due to the openness of the underlying system, there seems to be an indefinite number of unknown variables influencing strategic goals. Cybernetic or systemic planning techniques try to overcome this intricacy by modeling the most important cause-and-effect relations within such a system. Although it seems to be obvious that there are specific influences between business variables, it is mostly impossible to identify the functional dependencies underlying such relations. Hence simulation or evaluation techniques based on such hypothetically assumed models deliver inaccurate results or fail completely. This paper addresses the need for accurate strategy planning models and proposes an approach to prove their cause-andeffect relations by empirical evidence. Based on this foundation an approach for the approximation of the underlying cause-andeffect function by the means of Artificial Neural Networks is developed.
Dynamical modeling of tidal streams
International Nuclear Information System (INIS)
Bovy, Jo
2014-01-01
I present a new framework for modeling the dynamics of tidal streams. The framework consists of simple models for the initial action-angle distribution of tidal debris, which can be straightforwardly evolved forward in time. Taking advantage of the essentially one-dimensional nature of tidal streams, the transformation to position-velocity coordinates can be linearized and interpolated near a small number of points along the stream, thus allowing for efficient computations of a stream's properties in observable quantities. I illustrate how to calculate the stream's average location (its 'track') in different coordinate systems, how to quickly estimate the dispersion around its track, and how to draw mock stream data. As a generative model, this framework allows one to compute the full probability distribution function and marginalize over or condition it on certain phase-space dimensions as well as convolve it with observational uncertainties. This will be instrumental in proper data analysis of stream data. In addition to providing a computationally efficient practical tool for modeling the dynamics of tidal streams, the action-angle nature of the framework helps elucidate how the observed width of the stream relates to the velocity dispersion or mass of the progenitor, and how the progenitors of 'orphan' streams could be located. The practical usefulness of the proposed framework crucially depends on the ability to calculate action-angle variables for any orbit in any gravitational potential. A novel method for calculating actions, frequencies, and angles in any static potential using a single orbit integration is described in the Appendix.
α-Decomposition for estimating parameters in common cause failure modeling based on causal inference
International Nuclear Information System (INIS)
Zheng, Xiaoyu; Yamaguchi, Akira; Takata, Takashi
2013-01-01
The traditional α-factor model has focused on the occurrence frequencies of common cause failure (CCF) events. Global α-factors in the α-factor model are defined as fractions of failure probability for particular groups of components. However, there are unknown uncertainties in the CCF parameters estimation for the scarcity of available failure data. Joint distributions of CCF parameters are actually determined by a set of possible causes, which are characterized by CCF-triggering abilities and occurrence frequencies. In the present paper, the process of α-decomposition (Kelly-CCF method) is developed to learn about sources of uncertainty in CCF parameter estimation. Moreover, it aims to evaluate CCF risk significances of different causes, which are named as decomposed α-factors. Firstly, a Hybrid Bayesian Network is adopted to reveal the relationship between potential causes and failures. Secondly, because all potential causes have different occurrence frequencies and abilities to trigger dependent failures or independent failures, a regression model is provided and proved by conditional probability. Global α-factors are expressed by explanatory variables (causes’ occurrence frequencies) and parameters (decomposed α-factors). At last, an example is provided to illustrate the process of hierarchical Bayesian inference for the α-decomposition process. This study shows that the α-decomposition method can integrate failure information from cause, component and system level. It can parameterize the CCF risk significance of possible causes and can update probability distributions of global α-factors. Besides, it can provide a reliable way to evaluate uncertainty sources and reduce the uncertainty in probabilistic risk assessment. It is recommended to build databases including CCF parameters and corresponding causes’ occurrence frequency of each targeted system
Wang, Wei; Nelson, Suchitra; Albert, Jeffrey M.
2013-01-01
Mediators are intermediate variables in the causal pathway between an exposure and an outcome. Mediation analysis investigates the extent to which exposure effects occur through these variables, thus revealing causal mechanisms. In this paper, we consider the estimation of the mediation effect when the outcome is binary and multiple mediators of different types exist. We give a precise definition of the total mediation effect as well as decomposed mediation effects through individual or sets ...
International Nuclear Information System (INIS)
Killip, Ian Richmond
2002-01-01
This thesis investigates the range, distribution and causes of high radon levels in dwellings in the Brighton area of Southeast England. Indoor radon levels were measured in more than 1000 homes. The results show that high radon levels can arise in an area previously considered to offer low radon potential from local geological sources. Climate and building-related factors were found to affect significantly the radon levels in dwellings. Multiple regression was used to determine the influence of the various factors on indoor radon levels and an empirical model develop to predict indoor radon levels. The radon hazard, independent of building-related effects, was determined for each surveyed location by adjusting the radon measurement to that expected on the ground floor of a 'model' dwelling. This standardised set of radon levels was entered into a geographical information system (GIS) and related to surface geology. The geometric mean radon level for each lithological unit was plotted to produce a radon hazard map for the area. The highest radon levels were found to be associated with the youngest Chalk Formations, particularly where they meet overlying Tertiary deposits, and with Clay-with-Flints Quaternary deposits in the area. The results were also converted to the radon activity equivalent to that expected from the NRPB's standard dual-detector dwelling survey method and analysed by lognormal modelling to estimate the proportion of dwellings likely to exceed the UK Action Level of 200 Bq/m 3 for each lithological unit. The likely percentages of dwellings affected by radon thus obtained were mapped to lithological boundaries to produce a radon potential map. The radon hazard map and the empirical radon model facilitate the prediction of radon levels in dwellings of comparable construction and above similar geology and should further the understanding of the behaviour of radon gas in buildings to allow indoor radon concentrations to be controlled. The radon
Causes in the construction of causal law: A psycho-ecological model.
Young, Gerald
2010-01-01
The article presents an integrated psycho-ecological model of the construction of law, with implications for practice in law and mental health. The model is based on a series of concentric circles, each representing a layer of influence on the construction of law. The circle furthest removed from the center represents the influence of culture, society and industry, in particular, and the circle at the center of the circle represents the case at hand, for example, about individual complainant or mass action. The article begins by arguing that basic terms in relation to cause need clarification and also work is needed to disambiguate the concepts involved. After dealing with these issues, the article examines science and mental health. Is the scientific evidence presented by the expert sufficiently reliable and valid to meet admissibility standards of good compared to poor or junk science? Is the research undertaken for court or presented to court biased, with factors hidden, such as links to industry. Are individual evaluations conducted with biased science serving to justify partial conclusions? The dangers of powerful influences on the construction of law are highlighted, for example, related to the individual complainant malingering and the insurance industry protecting its financial interests at the expense of genuinely injured patients. In conclusion, suggestions for empirical research are offered. Copyright 2009 Elsevier Ltd. All rights reserved.
Wang, Wei; Albert, Jeffrey M
2017-08-01
An important problem within the social, behavioral, and health sciences is how to partition an exposure effect (e.g. treatment or risk factor) among specific pathway effects and to quantify the importance of each pathway. Mediation analysis based on the potential outcomes framework is an important tool to address this problem and we consider the estimation of mediation effects for the proportional hazards model in this paper. We give precise definitions of the total effect, natural indirect effect, and natural direct effect in terms of the survival probability, hazard function, and restricted mean survival time within the standard two-stage mediation framework. To estimate the mediation effects on different scales, we propose a mediation formula approach in which simple parametric models (fractional polynomials or restricted cubic splines) are utilized to approximate the baseline log cumulative hazard function. Simulation study results demonstrate low bias of the mediation effect estimators and close-to-nominal coverage probability of the confidence intervals for a wide range of complex hazard shapes. We apply this method to the Jackson Heart Study data and conduct sensitivity analysis to assess the impact on the mediation effects inference when the no unmeasured mediator-outcome confounding assumption is violated.
Shimizu, Takahiro
2014-01-01
The author developed a new scale aimed at measuring beliefs about "hypnotic states" and investigated the influence of such beliefs and attitudes on hypnotic responses in a large sample of Japanese undergraduate students. Exploratory factor analysis of this new questionnaire examining beliefs about hypnotic states yielded four factors: Dissociative or Depersonalized Experience, Loss of Self-Control, Therapeutic Expectation, and Arousing Extraordinary Ability. The results of structural equation modeling showed that Therapeutic Expectation and Arousing Extraordinary Ability influenced hypnotizability through attitudes toward hypnosis, while also directly affecting subjective experiences without mediating attitudes. Present findings suggest that it is more effective to enhance therapeutic expectations than to correct misconceptions about hypnotic states in modification of patients' beliefs before initiating treatment.
Jones, Robert
2010-03-01
There are a wide range of views on causality. To some (e.g. Karl Popper) causality is superfluous. Bertrand Russell said ``In advanced science the word cause never occurs. Causality is a relic of a bygone age.'' At the other extreme Rafael Sorkin and L. Bombelli suggest that space and time do not exist but are only an approximation to a reality that is simply a discrete ordered set, a ``causal set.'' For them causality IS reality. Others, like Judea Pearl and Nancy Cartwright are seaking to build a complex fundamental theory of causality (Causality, Cambridge Univ. Press, 2000) Or perhaps a theory of causality is simply the theory of functions. This is more or less my take on causality.
International Nuclear Information System (INIS)
Bombelli, L.; Lee, J.; Meyer, D.; Sorkin, R.D.
1987-01-01
We propose that space-time at the smallest scales is in reality a causal set: a locally finite set of elements endowed with a partial order corresponding to the macroscopic relation that defines past and future. We explore how a Lorentzian manifold can approximate a causal set, noting in particular that the thereby defined effective dimensionality of a given causal set can vary with length scale. Finally, we speculate briefly on the quantum dynamics of causal sets, indicating why an appropriate choice of action can reproduce general relativity in the classical limit
Directory of Open Access Journals (Sweden)
Idil Kokal
Full Text Available Studies investigating joint actions have suggested a central role for the putative mirror neuron system (pMNS because of the close link between perception and action provided by these brain regions [1], [2], [3]. In contrast, our previous functional magnetic resonance imaging (fMRI experiment demonstrated that the BOLD response of the pMNS does not suggest that it directly integrates observed and executed actions during joint actions [4]. To test whether the pMNS might contribute indirectly to the integration process by sending information to brain areas responsible for this integration (integration network, here we used Granger causality mapping (GCM [5]. We explored the directional information flow between the anterior sites of the pMNS and previously identified integrative brain regions. We found that the left BA44 sent more information than it received to both the integration network (left thalamus, right middle occipital gyrus and cerebellum and more posterior nodes of the pMNS (BA2. Thus, during joint actions, two anatomically separate networks therefore seem effectively connected and the information flow is predominantly from anterior to posterior areas of the brain. These findings suggest that the pMNS is involved indirectly in joint actions by transforming observed and executed actions into a common code and is part of a generative model that could predict the future somatosensory and visual consequences of observed and executed actions in order to overcome otherwise inevitable neural delays.
System Dynamics Modelling for a Balanced Scorecard
DEFF Research Database (Denmark)
Nielsen, Steen; Nielsen, Erland Hejn
2008-01-01
/methodology/approach - We use a case study model to develop time or dynamic dimensions by using a System Dynamics modelling (SDM) approach. The model includes five perspectives and a number of financial and non-financial measures. All indicators are defined and related to a coherent number of different cause...... have a major influence on other indicators and profit and may be impossible to predict without using a dynamic model. Practical implications - The model may be used as the first step in quantifying the cause-and-effect relationships of an integrated BSC model. Using the System Dynamics model provides......Purpose - To construct a dynamic model/framework inspired by a case study based on an international company. As described by the theory, one of the main difficulties of BSC is to foresee the time lag dimension of different types of indicators and their combined dynamic effects. Design...
Regression to Causality : Regression-style presentation influences causal attribution
DEFF Research Database (Denmark)
Bordacconi, Mats Joe; Larsen, Martin Vinæs
2014-01-01
of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... more likely. Our experiment drew on a sample of 235 university students from three different social science degree programs (political science, sociology and economics), all of whom had received substantial training in statistics. The subjects were asked to compare and evaluate the validity...
Directory of Open Access Journals (Sweden)
Cristina Puente Águeda
2011-10-01
Full Text Available Causality is a fundamental notion in every field of science. Since the times of Aristotle, causal relationships have been a matter of study as a way to generate knowledge and provide for explanations. In this paper I review the notion of causality through different scientific areas such as physics, biology, engineering, etc. In the scientific area, causality is usually seen as a precise relation: the same cause provokes always the same effect. But in the everyday world, the links between cause and effect are frequently imprecise or imperfect in nature. Fuzzy logic offers an adequate framework for dealing with imperfect causality, so a few notions of fuzzy causality are introduced.
The continuum limit of causal fermion systems from Planck scale structures to macroscopic physics
Finster, Felix
2016-01-01
This monograph introduces the basic concepts of the theory of causal fermion systems, a recent approach to the description of fundamental physics. The theory yields quantum mechanics, general relativity and quantum field theory as limiting cases and is therefore a candidate for a unified physical theory. From the mathematical perspective, causal fermion systems provide a general framework for describing and analyzing non-smooth geometries and "quantum geometries". The dynamics is described by a novel variational principle, called the causal action principle. In addition to the basics, the book provides all the necessary mathematical background and explains how the causal action principle gives rise to the interactions of the standard model plus gravity on the level of second-quantized fermionic fields coupled to classical bosonic fields. The focus is on getting a mathematically sound connection between causal fermion systems and physical systems in Minkowski space. The book is intended for graduate students e...
Inferring causal molecular networks: empirical assessment through a community-based effort.
Hill, Steven M; Heiser, Laura M; Cokelaer, Thomas; Unger, Michael; Nesser, Nicole K; Carlin, Daniel E; Zhang, Yang; Sokolov, Artem; Paull, Evan O; Wong, Chris K; Graim, Kiley; Bivol, Adrian; Wang, Haizhou; Zhu, Fan; Afsari, Bahman; Danilova, Ludmila V; Favorov, Alexander V; Lee, Wai Shing; Taylor, Dane; Hu, Chenyue W; Long, Byron L; Noren, David P; Bisberg, Alexander J; Mills, Gordon B; Gray, Joe W; Kellen, Michael; Norman, Thea; Friend, Stephen; Qutub, Amina A; Fertig, Elana J; Guan, Yuanfang; Song, Mingzhou; Stuart, Joshua M; Spellman, Paul T; Koeppl, Heinz; Stolovitzky, Gustavo; Saez-Rodriguez, Julio; Mukherjee, Sach
2016-04-01
It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.
International Nuclear Information System (INIS)
Shin, Seung Ki; Seong, Poong Hyun
2008-01-01
Conventional static reliability analysis methods are inadequate for modeling dynamic interactions between components of a system. Various techniques such as dynamic fault tree, dynamic Bayesian networks, and dynamic reliability block diagrams have been proposed for modeling dynamic systems based on improvement of the conventional modeling methods. In this paper, we review these methods briefly and introduce dynamic nodes to the existing Reliability Graph with General Gates (RGGG) as an intuitive modeling method to model dynamic systems. For a quantitative analysis, we use a discrete-time method to convert an RGGG to an equivalent Bayesian network and develop a software tool for generation of probability tables
Characterizing and modeling citation dynamics.
Directory of Open Access Journals (Sweden)
Young-Ho Eom
Full Text Available Citation distributions are crucial for the analysis and modeling of the activity of scientists. We investigated bibliometric data of papers published in journals of the American Physical Society, searching for the type of function which best describes the observed citation distributions. We used the goodness of fit with Kolmogorov-Smirnov statistics for three classes of functions: log-normal, simple power law and shifted power law. The shifted power law turns out to be the most reliable hypothesis for all citation networks we derived, which correspond to different time spans. We find that citation dynamics is characterized by bursts, usually occurring within a few years since publication of a paper, and the burst size spans several orders of magnitude. We also investigated the microscopic mechanisms for the evolution of citation networks, by proposing a linear preferential attachment with time dependent initial attractiveness. The model successfully reproduces the empirical citation distributions and accounts for the presence of citation bursts as well.
Supply based on demand dynamical model
Levi, Asaf; Sabuco, Juan; Sanjuán, Miguel A. F.
2018-04-01
We propose and numerically analyze a simple dynamical model that describes the firm behaviors under uncertainty of demand. Iterating this simple model and varying some parameter values, we observe a wide variety of market dynamics such as equilibria, periodic, and chaotic behaviors. Interestingly, the model is also able to reproduce market collapses.
Opinion dynamics model based on quantum formalism
Energy Technology Data Exchange (ETDEWEB)
Artawan, I. Nengah, E-mail: nengahartawan@gmail.com [Theoretical Physics Division, Department of Physics, Udayana University (Indonesia); Trisnawati, N. L. P., E-mail: nlptrisnawati@gmail.com [Biophysics, Department of Physics, Udayana University (Indonesia)
2016-03-11
Opinion dynamics model based on quantum formalism is proposed. The core of the quantum formalism is on the half spin dynamics system. In this research the implicit time evolution operators are derived. The analogy between the model with Deffuant dan Sznajd models is discussed.
Relating structure and dynamics in organisation models
Jonker, C.M.; Treur, J.
2003-01-01
To understand how an organisational structure relates to dynamics is an interesting fundamental challenge in the area of social modelling. Specifications of organisational structure usually have a diagrammatic form that abstracts from more detailed dynamics. Dynamic properties of agent systems, on
Derringer, Cory; Rottman, Benjamin Margolin
2018-05-01
Four experiments tested how people learn cause-effect relations when there are many possible causes of an effect. When there are many cues, even if all the cues together strongly predict the effect, the bivariate relation between each individual cue and the effect can be weak, which can make it difficult to detect the influence of each cue. We hypothesized that when detecting the influence of a cue, in addition to learning from the states of the cues and effect (e.g., a cue is present and the effect is present), which is hypothesized by multiple existing theories of learning, participants would also learn from transitions - how the cues and effect change over time (e.g., a cue turns on and the effect turns on). We found that participants were better able to identify positive and negative cues in an environment in which only one cue changed from one trial to the next, compared to multiple cues changing (Experiments 1A, 1B). Within a single learning sequence, participants were also more likely to update their beliefs about causal strength when one cue changed at a time ('one-change transitions') than when multiple cues changed simultaneously (Experiment 2). Furthermore, learning was impaired when the trials were grouped by the state of the effect (Experiment 3) or when the trials were grouped by the state of a cue (Experiment 4), both of which reduce the number of one-change transitions. We developed a modification of the Rescorla-Wagner algorithm to model this 'Informative Transitions' learning processes. Copyright © 2018 Elsevier Inc. All rights reserved.
Merrill, Jacqueline A; Deegan, Michael; Wilson, Rosalind V; Kaushal, Rainu; Fredericks, Kimberly
2013-06-01
To evaluate the complex dynamics involved in implementing electronic health information exchange (HIE) for public health reporting at a state health department, and to identify policy implications to inform similar implementations. Qualitative data were collected over 8 months from seven experts at New York State Department of Health who implemented web services and protocols for querying, receipt, and validation of electronic data supplied by regional health information organizations. Extensive project documentation was also collected. During group meetings experts described the implementation process and created reference modes and causal diagrams that the evaluation team used to build a preliminary model. System dynamics modeling techniques were applied iteratively to build causal loop diagrams representing the implementation. The diagrams were validated iteratively by individual experts followed by group review online, and through confirmatory review of documents and artifacts. Three casual loop diagrams captured well-recognized system dynamics: Sliding Goals, Project Rework, and Maturity of Resources. The findings were associated with specific policies that address funding, leadership, ensuring expertise, planning for rework, communication, and timeline management. This evaluation illustrates the value of a qualitative approach to system dynamics modeling. As a tool for strategic thinking on complicated and intense processes, qualitative models can be produced with fewer resources than a full simulation, yet still provide insights that are timely and relevant. System dynamics techniques clarified endogenous and exogenous factors at play in a highly complex technology implementation, which may inform other states engaged in implementing HIE supported by federal Health Information Technology for Economic and Clinical Health (HITECH) legislation.
Kim, Seehyung
2005-01-01
This research develops and tests a model of the service unit ownership and control patterns used by international service companies. The main purpose of this study is to investigate trivariate causal relationships among environmental factors, organizational structure, and perceived performance in the internationalization process of service firms. A service firm operating in foreign soil has a choice of three general entry mode strategies offering different degrees of ownership and control of ...
Directory of Open Access Journals (Sweden)
Tian-Syung Lan
2014-01-01
Full Text Available This study used system dynamics method to investigate the factors affecting elementary school students’ BMI values. The construction of the dynamic model is divided into the qualitative causal loop and the quantitative system dynamics modeling. According to the system dynamics modeling, this study consisted of research on the four dimensions: student’s personal life style, diet-relevant parenting behaviors, advocacy and implementation of school nutrition education, and students’ peer interaction. The results of this study showed that students with more adequate health concepts usually have better eating behaviors and consequently have less chance of becoming obese. In addition, this study also verified that educational attainment and socioeconomic status of parents have a positive correlation with students’ amounts of physical activity, and nutrition education has a prominent influence on changing students’ high-calorie diets.
Lan, Tian-Syung; Chen, Kai-Ling; Chen, Pin-Chang; Ku, Chao-Tai; Chiu, Pei-Hsuan; Wang, Meng-Hsiang
2014-01-01
This study used system dynamics method to investigate the factors affecting elementary school students' BMI values. The construction of the dynamic model is divided into the qualitative causal loop and the quantitative system dynamics modeling. According to the system dynamics modeling, this study consisted of research on the four dimensions: student's personal life style, diet-relevant parenting behaviors, advocacy and implementation of school nutrition education, and students' peer interaction. The results of this study showed that students with more adequate health concepts usually have better eating behaviors and consequently have less chance of becoming obese. In addition, this study also verified that educational attainment and socioeconomic status of parents have a positive correlation with students' amounts of physical activity, and nutrition education has a prominent influence on changing students' high-calorie diets.
Causal imprinting in causal structure learning.
Taylor, Eric G; Ahn, Woo-Kyoung
2012-11-01
Suppose one observes a correlation between two events, B and C, and infers that B causes C. Later one discovers that event A explains away the correlation between B and C. Normatively, one should now dismiss or weaken the belief that B causes C. Nonetheless, participants in the current study who observed a positive contingency between B and C followed by evidence that B and C were independent given A, persisted in believing that B causes C. The authors term this difficulty in revising initially learned causal structures "causal imprinting." Throughout four experiments, causal imprinting was obtained using multiple dependent measures and control conditions. A Bayesian analysis showed that causal imprinting may be normative under some conditions, but causal imprinting also occurred in the current study when it was clearly non-normative. It is suggested that causal imprinting occurs due to the influence of prior knowledge on how reasoners interpret later evidence. Consistent with this view, when participants first viewed the evidence showing that B and C are independent given A, later evidence with only B and C did not lead to the belief that B causes C. Copyright © 2012 Elsevier Inc. All rights reserved.
On the Temporal Causal Relationship Between Macroeconomic Variables
Directory of Open Access Journals (Sweden)
Srinivasan Palamalai
2014-02-01
Full Text Available The present study examines the dynamic interactions among macroeconomic variables such as real output, prices, money supply, interest rate (IR, and exchange rate (EXR in India during the pre-economic crisis and economic crisis periods, using the autoregressive distributed lag (ARDL bounds test for cointegration, Johansen and Juselius multivariate cointegration test, Granger causality/Block exogeneity Wald test based on Vector Error Correction Model, variance decomposition analysis and impulse response functions. The empirical results reveal a stronger long-run bilateral relationship between real output, price level, IR, and EXR during the pre-crisis sample period. Moreover, the empirical results confirm a unidirectional short-run causality running from price level to EXR, IR to price level, and real output to money supply during the pre-crisis period. Also, it is evident from the test results that there exist short-run bidirectional relationships running between real output and EXR, price level and IR, and IR and EXR in the pre-crisis era, respectively. Most importantly, long-run bidirectional causality is found between real output, EXR, and IR during the economic crisis period. And the study results indicate short-run bidirectional causality between money supply and EXR, IR and price level, and IR and output in India during the crisis era. Also, a short-run unidirectional causality runs from prices to real output in the crisis period.
Causality between public policies and exports of renewable energy technologies
International Nuclear Information System (INIS)
Sung, Bongsuk; Song, Woo-Yong
2013-01-01
This article investigates the causal relationship between public policies and exports of renewable energy technologies using panel data from 18 countries for the period 1991–2007. A number of panel unit root and cointegration tests are applied. Time series data on public policies and exports are integrated and cointegrated. The dynamic OLS results indicate that in the long run, a 1% increase in government R and D expenditures (RAD) increases exports (EX) by 0.819%. EX and RAD variables respond to deviations from the long-run equilibrium in the previous period. Additionally, the Blundell–Bond system generalized methods of moments (GMM) is employed to conduct a panel causality test in a vector error-correction mechanism (VECM) setting. Evidence of a bidirectional and short-run, and strong causal relationship between EX and the contribution of renewable energy to the total energy supply (CRES) is uncovered. CRES has a negative effect on EX, whereas EX has a positive effect on CRES. We suggest some policy implications based on the results of this study. - Highlights: ► We model VECM to test the Granger causality between the policies and the export. ► Technology-push policy has a positive impact on export in the long-run. ► There are the short-run causal relationships between market-pull policy and export
Hierarchical organisation of causal graphs
International Nuclear Information System (INIS)
Dziopa, P.
1993-01-01
This paper deals with the design of a supervision system using a hierarchy of models formed by graphs, in which the variables are the nodes and the causal relations between the variables of the arcs. To obtain a representation of the variables evolutions which contains only the relevant features of their real evolutions, the causal relations are completed with qualitative transfer functions (QTFs) which produce roughly the behaviour of the classical transfer functions. Major improvements have been made in the building of the hierarchical organization. First, the basic variables of the uppermost level and the causal relations between them are chosen. The next graph is built by adding intermediary variables to the upper graph. When the undermost graph has been built, the transfer functions parameters corresponding to its causal relations are identified. The second task consists in the upwelling of the information from the undermost graph to the uppermost one. A fusion procedure of the causal relations has been designed to compute the QFTs relevant for each level. This procedure aims to reduce the number of parameters needed to represent an evolution at a high level of abstraction. These techniques have been applied to the hierarchical modelling of nuclear process. (authors). 8 refs., 12 figs
Dynamic modelling of nuclear steam generators
International Nuclear Information System (INIS)
Kerlin, T.W.; Katz, E.M.; Freels, J.; Thakkar, J.
1980-01-01
Moving boundary, nodal models with dynamic energy balances, dynamic mass balances, quasi-static momentum balances, and an equivalent single channel approach have been developed for steam generators used in nuclear power plants. The model for the U-tube recirculation type steam generator is described and comparisons are made of responses from models of different complexity; non-linear versus linear, high-order versus low order, detailed modeling of the control system versus a simple control assumption. The results of dynamic tests on nuclear power systems show that when this steam generator model is included in a system simulation there is good agreement with actual plant performance. (author)
Dynamic Airspace Managment - Models and Algorithms
Cheng, Peng; Geng, Rui
2010-01-01
This chapter investigates the models and algorithms for implementing the concept of Dynamic Airspace Management. Three models are discussed. First two models are about how to use or adjust air route dynamically in order to speed up air trafï¬c ï¬‚ow and reduce delay. The third model gives a way to dynamically generate the optimal sector conï¬guration for an air trafï¬c control center to both balance the controllerâ€™s workload and save control resources. The ï¬rst model, called the Dynami...
A General Approach to Causal Mediation Analysis
Imai, Kosuke; Keele, Luke; Tingley, Dustin
2010-01-01
Traditionally in the social sciences, causal mediation analysis has been formulated, understood, and implemented within the framework of linear structural equation models. We argue and demonstrate that this is problematic for 3 reasons: the lack of a general definition of causal mediation effects independent of a particular statistical model, the…
Causal Indicators Can Help to Interpret Factors
Bentler, Peter M.
2016-01-01
The latent factor in a causal indicator model is no more than the latent factor of the factor part of the model. However, if the causal indicator variables are well-understood and help to improve the prediction of individuals' factor scores, they can help to interpret the meaning of the latent factor. Aguirre-Urreta, Rönkkö, and Marakas (2016)…
Wind Farm Decentralized Dynamic Modeling With Parameters
DEFF Research Database (Denmark)
Soltani, Mohsen; Shakeri, Sayyed Mojtaba; Grunnet, Jacob Deleuran
2010-01-01
Development of dynamic wind flow models for wind farms is part of the research in European research FP7 project AEOLUS. The objective of this report is to provide decentralized dynamic wind flow models with parameters. The report presents a structure for decentralized flow models with inputs from...... local models. The results of this report are especially useful, but not limited, to design a decentralized wind farm controller, since in centralized controller design one can also use the model and update it in a central computing node.......Development of dynamic wind flow models for wind farms is part of the research in European research FP7 project AEOLUS. The objective of this report is to provide decentralized dynamic wind flow models with parameters. The report presents a structure for decentralized flow models with inputs from...
System dynamics modelling of situation awareness
CSIR Research Space (South Africa)
Oosthuizen, R
2015-11-01
Full Text Available . The feedback loops and delays in the Command and Control system also contribute to the complex dynamic behavior. This paper will build on existing situation awareness models to develop a System Dynamics model to support a qualitative investigation through...
An Agent Model Integrating an Adaptive Model for Environmental Dynamics
Treur, J.; Umair, M.
2011-01-01
The environments in which agents are used often may be described by dynamical models, e.g., in the form of a set of differential equations. In this paper, an agent model is proposed that can perform model-based reasoning about the environment, based on a numerical (dynamical system) model of the
Hydration dynamics near a model protein surface
International Nuclear Information System (INIS)
Russo, Daniela; Hura, Greg; Head-Gordon, Teresa
2003-01-01
The evolution of water dynamics from dilute to very high concentration solutions of a prototypical hydrophobic amino acid with its polar backbone, N-acetyl-leucine-methylamide (NALMA), is studied by quasi-elastic neutron scattering and molecular dynamics simulation for both the completely deuterated and completely hydrogenated leucine monomer. We observe several unexpected features in the dynamics of these biological solutions under ambient conditions. The NALMA dynamics shows evidence of de Gennes narrowing, an indication of coherent long timescale structural relaxation dynamics. The translational water dynamics are analyzed in a first approximation with a jump diffusion model. At the highest solute concentrations, the hydration water dynamics is significantly suppressed and characterized by a long residential time and a slow diffusion coefficient. The analysis of the more dilute concentration solutions takes into account the results of the 2.0M solution as a model of the first hydration shell. Subtracting the first hydration layer based on the 2.0M spectra, the translational diffusion dynamics is still suppressed, although the rotational relaxation time and residential time are converged to bulk-water values. Molecular dynamics analysis shows spatially heterogeneous dynamics at high concentration that becomes homogeneous at more dilute concentrations. We discuss the hydration dynamics results of this model protein system in the context of glassy systems, protein function, and protein-protein interfaces
Causal Dynamics of Discrete Surfaces
Directory of Open Access Journals (Sweden)
Pablo Arrighi
2014-03-01
Full Text Available We formalize the intuitive idea of a labelled discrete surface which evolves in time, subject to two natural constraints: the evolution does not propagate information too fast; and it acts everywhere the same.
IT-enabled dynamic capability on performance: An empirical study of BSC model
Directory of Open Access Journals (Sweden)
Adilson Carlos Yoshikuni
2017-05-01
Full Text Available ew studies have investigated the influence of “information capital,” through IT-enabled dynamic capability, on corporate performance, particularly in economic turbulence. Our study investigates the causal relationship between performance perspectives of the balanced scorecard using partial least squares path modeling. Using data on 845 Brazilian companies, we conduct a quantitative empirical study of firms during an economic crisis and observe the following interesting results. Operational and analytical IT-enabled dynamic capability had positive effects on business process improvement and corporate performance. Results pertaining to mediation (endogenous variables and moderation (control variables clarify IT’s role in and benefits for corporate performance.
Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection
DEFF Research Database (Denmark)
Bork, Lasse; Møller, Stig Vinther
2015-01-01
We examine house price forecastability across the 50 states using Dynamic Model Averaging and Dynamic Model Selection, which allow for model change and parameter shifts. By allowing the entire forecasting model to change over time and across locations, the forecasting accuracy improves substantia......We examine house price forecastability across the 50 states using Dynamic Model Averaging and Dynamic Model Selection, which allow for model change and parameter shifts. By allowing the entire forecasting model to change over time and across locations, the forecasting accuracy improves...
Directory of Open Access Journals (Sweden)
Younis Jabarzadeh
2016-03-01
Full Text Available Identifying influential factors which contribute to the knowledge management maturity and studying their interaction over time helps managers to understand the complex behavior of knowledge management system. It also leads them to make right decisions for utilizing these factors in promoting knowledge management and achieve strategic goals of the organization by providing a sound insight and an appropriate mechanism to reach to the optimal maturity level. In this study, all aspects and components of knowledge management with an emphasis on human factors and organizational culture, and relations between them have been identified by using a systematic literature review and meta-synthesis qualitative research approach. Then by using consultation and consensus of experts, all results verified. The results include 64 codes which are classified in 9 dimensions and two categories. Finally, due to the obtained classification and their relations, the dynamic model of knowledge management maturity is presented. The results of this study could be a suitable framework for improving mental models of knowledge management executives and experts. It makes possible Developing dynamic analysis models and appropriate policies in order to improve the knowledge management maturity in organizations.
Bayesian networks improve causal environmental ...
Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the problem, using this knowledge with probabilistic calculus to combine multiple lines of evidence, and minimizing biases in predicting or diagnosing causal relationships. Too often, sources of uncertainty in conventional weight of evidence approaches are ignored that can be accounted for with Bayesian networks. Specifying and propagating uncertainties improve the ability of models to incorporate strength of the evidence in the risk management phase of an assessment. Probabilistic inference from a Bayesian network allows evaluation of changes in uncertainty for variables from the evidence. The network structure and probabilistic framework of a Bayesian approach provide advantages over qualitative approaches in weight of evidence for capturing the impacts of multiple sources of quantifiable uncertainty on predictions of ecological risk. Bayesian networks can facilitate the development of evidence-based policy under conditions of uncertainty by incorporating analytical inaccuracies or the implications of imperfect information, structuring and communicating causal issues through qualitative directed graph formulations, and quantitatively comparing the causal power of multiple stressors on value
Adaptive numerical modeling of dynamic crack propagation
International Nuclear Information System (INIS)
Adouani, H.; Tie, B.; Berdin, C.; Aubry, D.
2006-01-01
We propose an adaptive numerical strategy that aims at developing reliable and efficient numerical tools to model dynamic crack propagation and crack arrest. We use the cohesive zone theory as behavior of interface-type elements to model crack. Since the crack path is generally unknown beforehand, adaptive meshing is proposed to model the dynamic crack propagation. The dynamic study requires the development of specific solvers for time integration. As both geometry and finite element mesh of the studied structure evolve in time during transient analysis, the stability behavior of dynamic solver becomes a major concern. For this purpose, we use the space-time discontinuous Galerkin finite element method, well-known to provide a natural framework to manage meshes that evolve in time. As an important result, we prove that the space-time discontinuous Galerkin solver is unconditionally stable, when the dynamic crack propagation is modeled by the cohesive zone theory, which is highly non-linear. (authors)
McGeown, Laura; Davis, Ron
2018-02-15
The social modeling of eating effect refers to the consistently demonstrated phenomenon that individuals tend to match their quantity of food intake to their eating companion. The current study sought to explore whether activity within the mirror neuron system (MNS) mediates the social modeling of eating effect as a function of EEG frontal asymmetry and body mass index (BMI). Under the guise of rating empathy, 93 female undergraduates viewed a female video confederate "incidentally" consume either a low or high intake of chips while electroencephalogram (EEG) activity was recorded. Subsequent ad libitum chip consumption was quantified. A first- and second-stage dual moderation model revealed that frontal asymmetry and BMI moderated an indirect effect of model consumption on participants' food consumption as mediated by MNS activity at electrode site C3, a 3 b 3 =-0.718, SE=0.365, 95% CI [-1.632, -0.161]. Left frontal asymmetry was associated with greater mu activity and a positive association between model and participant chip consumption, while right frontal asymmetry was associated with less mu activity and a negative association between model and participant consumption. Across all levels of frontal asymmetry, the effect was only significant among those with a BMI at the 50th percentile or lower. Thus, among leaner individuals, the MNS was demonstrated to mediate social modeling of eating, as moderated by frontal asymmetry. These findings are integrated within the normative account of social modeling of eating. It is proposed that the normative framework may benefit from consideration of both conscious and unconscious operation of intake norms. Copyright © 2017 Elsevier B.V. All rights reserved.
Introductive remarks on causal inference
Directory of Open Access Journals (Sweden)
Silvana A. Romio
2013-05-01
Full Text Available One of the more challenging issues in epidemiological research is being able to provide an unbiased estimate of the causal exposure-disease effect, to assess the possible etiological mechanisms and the implication for public health. A major source of bias is confounding, which can spuriously create or mask the causal relationship. In the last ten years, methodological research has been developed to better de_ne the concept of causation in epidemiology and some important achievements have resulted in new statistical models. In this review, we aim to show how a technique the well known by statisticians, i.e. standardization, can be seen as a method to estimate causal e_ects, equivalent under certain conditions to the inverse probability treatment weight procedure.
Modeling Gas Dynamics in California Sea Lions
2015-09-30
W. and Fahlman, A. (2009). Could beaked whales get the bends?. Effect of diving behaviour and physiology on modelled gas exchange for three species...1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Modeling Gas Dynamics in California Sea Lions Andreas...to update a current gas dynamics model with recently acquired data for respiratory compliance (P-V), and body compartment size estimates in
A Dynamic Model of Sustainment Investment
2015-02-01
Sustainment System Dynamics Model 11 Figure 7: Core Structure of Sustainment Work 12 Figure 8: Bandwagon Effect Loop 13 Figure 9: Limits to Growth Loop 14...Dynamics Model sustainment capacity sustainment performance gap Bandwagon Effect R1 Limits to Growth B1 S Work Smarter B3 Work Bigger B2 desired...which is of concern primarily when using the model as a vehicle for research. Figure 8 depicts a reinforcing loop called the “ Bandwagon Effect
Modeling the Dynamic Digestive System Microbiome†
Estes, Anne M.
2015-01-01
“Modeling the Dynamic Digestive System Microbiome” is a hands-on activity designed to demonstrate the dynamics of microbiome ecology using dried pasta and beans to model disturbance events in the human digestive system microbiome. This exercise demonstrates how microbiome diversity is influenced by: 1) niche availability and habitat space and 2) a major disturbance event, such as antibiotic use. Students use a pictorial key to examine prepared models of digestive system microbiomes to determi...
Differential equation models for sharp threshold dynamics.
Schramm, Harrison C; Dimitrov, Nedialko B
2014-01-01
We develop an extension to differential equation models of dynamical systems to allow us to analyze probabilistic threshold dynamics that fundamentally and globally change system behavior. We apply our novel modeling approach to two cases of interest: a model of infectious disease modified for malware where a detection event drastically changes dynamics by introducing a new class in competition with the original infection; and the Lanchester model of armed conflict, where the loss of a key capability drastically changes the effectiveness of one of the sides. We derive and demonstrate a step-by-step, repeatable method for applying our novel modeling approach to an arbitrary system, and we compare the resulting differential equations to simulations of the system's random progression. Our work leads to a simple and easily implemented method for analyzing probabilistic threshold dynamics using differential equations. Published by Elsevier Inc.
Campbell's and Rubin's Perspectives on Causal Inference
West, Stephen G.; Thoemmes, Felix
2010-01-01
Donald Campbell's approach to causal inference (D. T. Campbell, 1957; W. R. Shadish, T. D. Cook, & D. T. Campbell, 2002) is widely used in psychology and education, whereas Donald Rubin's causal model (P. W. Holland, 1986; D. B. Rubin, 1974, 2005) is widely used in economics, statistics, medicine, and public health. Campbell's approach focuses on…
Sinha, Shriprakash
2016-01-01
Simulation study in systems biology involving computational experiments dealing with Wnt signaling pathways abound in literature but often lack a pedagogical perspective that might ease the understanding of beginner students and researchers in transition, who intend to work on the modeling of the pathway. This paucity might happen due to restrictive business policies which enforce an unwanted embargo on the sharing of important scientific knowledge. A tutorial introduction to computational mo...
Nonlinear dynamic phenomena in the beer model
DEFF Research Database (Denmark)
Mosekilde, Erik; Laugesen, Jakob Lund
2007-01-01
The production-distribution system or "beer game" is one of the most well-known system dynamics models. Notorious for the complex dynamics it produces, the beer game has been used for nearly five decades to illustrate how structure generates behavior and to explore human decision making. Here we...
Phone Routing using the Dynamic Memory Model
DEFF Research Database (Denmark)
Bendtsen, Claus Nicolaj; Krink, Thiemo
2002-01-01
In earlier studies a genetic algorithm (GA) extended with the dynamic memory model has shown remarkable performance on real-world-like problems. In this paper we experiment with routing in communication networks and show that the dynamic memory GA performs remarkable well compared to ant colony...
Dynamic queuing transmission model for dynamic network loading
DEFF Research Database (Denmark)
Raovic, Nevena; Nielsen, Otto Anker; Prato, Carlo Giacomo
2017-01-01
and allowing for the representation of multiple vehicle classes, queue spillbacks and shock waves. The model assumes that a link is split into a moving part plus a queuing part, and p that traffic dynamics are given by a triangular fundamental diagram. A case-study is investigated and the DQTM is compared...
The Continuum Limit of Causal Fermion Systems
Finster, Felix
2016-01-01
This monograph introduces the basic concepts of the theory of causal fermion systems, a recent approach to the description of fundamental physics. The theory yields quantum mechanics, general relativity and quantum field theory as limiting cases and is therefore a candidate for a unified physical theory. From the mathematical perspective, causal fermion systems provide a general framework for describing and analyzing non-smooth geometries and "quantum geometries." The dynamics is described by...
Entanglement, holography and causal diamonds
de Boer, Jan; Haehl, Felix M.; Heller, Michal P.; Myers, Robert C.
2016-08-01
We argue that the degrees of freedom in a d-dimensional CFT can be reorganized in an insightful way by studying observables on the moduli space of causal diamonds (or equivalently, the space of pairs of timelike separated points). This 2 d-dimensional space naturally captures some of the fundamental nonlocality and causal structure inherent in the entanglement of CFT states. For any primary CFT operator, we construct an observable on this space, which is defined by smearing the associated one-point function over causal diamonds. Known examples of such quantities are the entanglement entropy of vacuum excitations and its higher spin generalizations. We show that in holographic CFTs, these observables are given by suitably defined integrals of dual bulk fields over the corresponding Ryu-Takayanagi minimal surfaces. Furthermore, we explain connections to the operator product expansion and the first law of entanglemententropy from this unifying point of view. We demonstrate that for small perturbations of the vacuum, our observables obey linear two-derivative equations of motion on the space of causal diamonds. In two dimensions, the latter is given by a product of two copies of a two-dimensional de Sitter space. For a class of universal states, we show that the entanglement entropy and its spin-three generalization obey nonlinear equations of motion with local interactions on this moduli space, which can be identified with Liouville and Toda equations, respectively. This suggests the possibility of extending the definition of our new observables beyond the linear level more generally and in such a way that they give rise to new dynamically interacting theories on the moduli space of causal diamonds. Various challenges one has to face in order to implement this idea are discussed.
Entanglement, holography and causal diamonds
Energy Technology Data Exchange (ETDEWEB)
Boer, Jan de [Institute of Physics, Universiteit van Amsterdam,Science Park 904, 1090 GL Amsterdam (Netherlands); Haehl, Felix M. [Centre for Particle Theory & Department of Mathematical Sciences, Durham University,South Road, Durham DH1 3LE (United Kingdom); Heller, Michal P.; Myers, Robert C. [Perimeter Institute for Theoretical Physics,31 Caroline Street North, Waterloo, Ontario N2L 2Y5 (Canada)
2016-08-29
We argue that the degrees of freedom in a d-dimensional CFT can be re-organized in an insightful way by studying observables on the moduli space of causal diamonds (or equivalently, the space of pairs of timelike separated points). This 2d-dimensional space naturally captures some of the fundamental nonlocality and causal structure inherent in the entanglement of CFT states. For any primary CFT operator, we construct an observable on this space, which is defined by smearing the associated one-point function over causal diamonds. Known examples of such quantities are the entanglement entropy of vacuum excitations and its higher spin generalizations. We show that in holographic CFTs, these observables are given by suitably defined integrals of dual bulk fields over the corresponding Ryu-Takayanagi minimal surfaces. Furthermore, we explain connections to the operator product expansion and the first law of entanglement entropy from this unifying point of view. We demonstrate that for small perturbations of the vacuum, our observables obey linear two-derivative equations of motion on the space of causal diamonds. In two dimensions, the latter is given by a product of two copies of a two-dimensional de Sitter space. For a class of universal states, we show that the entanglement entropy and its spin-three generalization obey nonlinear equations of motion with local interactions on this moduli space, which can be identified with Liouville and Toda equations, respectively. This suggests the possibility of extending the definition of our new observables beyond the linear level more generally and in such a way that they give rise to new dynamically interacting theories on the moduli space of causal diamonds. Various challenges one has to face in order to implement this idea are discussed.
Dynamic analysis of savings and economic growth in Nigeria ...
African Journals Online (AJOL)
Dynamic analysis of savings and economic growth in Nigeria. ... a trivariate dynamic Granger causality model with savings, economic growth and foreign ... It is recommended that in the short run, policies in Nigeria should be geared towards ...
Some dynamical aspects of interacting quintessence model
Indian Academy of Sciences (India)
Binayak S Choudhury
2018-03-16
Mar 16, 2018 ... Accelerated expansion of the Universe; quintessence; dynamical system; Friedmann–Lemaitre–. Robertson–Walker ... accepted theoretical model. One of the .... Thus, quintessence loses its self-strength and gives dark matter.
Modeling of truck's braking dynamics with ABS
Directory of Open Access Journals (Sweden)
Maxym DYACHUK
2014-09-01
Full Text Available In the article some questions of ABS simulation on the basis of plane vehicle's dynamics and automatic modeling are considered. The author's algorithm of ABS modulators control is presented.
Dynamic Models of Insurgent Activity
2014-05-19
one dimension that has recently been studied in the computer science community. The model involves movement with a speed proportional to a “fear...more realistic model of human locomotion. The movement of the criminal agents follows a biased Levy flight with step sizes distributed according to a...power-law distribution. The biased Brownian motion of the original model is then derived as a special case. Starting with an agent-based model, we
Stochastic population dynamic models as probability networks
M.E. and D.C. Lee. Borsuk
2009-01-01
The dynamics of a population and its response to environmental change depend on the balance of birth, death and age-at-maturity, and there have been many attempts to mathematically model populations based on these characteristics. Historically, most of these models were deterministic, meaning that the results were strictly determined by the equations of the model and...
International Nuclear Information System (INIS)
Ghirardi, G.C.; Pearle, P.
1991-02-01
The problem of getting a relativistic generalization of the CSL dynamical reduction model, which has been presented in part I, is discussed. In so doing we have the opportunity to introduce the idea of a stochastically invariant theory. The theoretical model we present, that satisfies this kind of invariance requirement, offers us the possibility to reconsider, from a new point of view, some conceptually relevant issues such as nonlocality, the legitimacy of attributing elements of physical reality to physical systems and the problem of establishing causal relations between physical events. (author). Refs, 3 figs
Repeated Causal Decision Making
Hagmayer, York; Meder, Bjorn
2013-01-01
Many of our decisions refer to actions that have a causal impact on the external environment. Such actions may not only allow for the mere learning of expected values or utilities but also for acquiring knowledge about the causal structure of our world. We used a repeated decision-making paradigm to examine what kind of knowledge people acquire in…
International Nuclear Information System (INIS)
Novello, M.; Salim, J.M.; Torres, J.; Oliveira, H.P. de
1989-01-01
A set of spatially homogeneous and isotropic cosmological geometries generated by a class of non-perfect is investigated fluids. The irreversibility if this system is studied in the context of causal thermodynamics which provides a useful mechanism to conform to the non-violation of the causal principle. (author) [pt
Causality in Classical Electrodynamics
Savage, Craig
2012-01-01
Causality in electrodynamics is a subject of some confusion, especially regarding the application of Faraday's law and the Ampere-Maxwell law. This has led to the suggestion that we should not teach students that electric and magnetic fields can cause each other, but rather focus on charges and currents as the causal agents. In this paper I argue…
Bond graph modeling of nuclear reactor dynamics
International Nuclear Information System (INIS)
Tylee, J.L.
1981-01-01
A tenth-order linear model of a pressurized water reactor (PWR) is developed using bond graph techniques. The model describes the nuclear heat generation process and the transfer of this heat to the reactor coolant. Comparisons between the calculated model response and test data from a small-scale PWR show the model to be an adequate representation of the actual plant dynamics. Possible application of the model in an advanced plant diagnostic system is discussed
Swarm Intelligence for Urban Dynamics Modelling
International Nuclear Information System (INIS)
Ghnemat, Rawan; Bertelle, Cyrille; Duchamp, Gerard H. E.
2009-01-01
In this paper, we propose swarm intelligence algorithms to deal with dynamical and spatial organization emergence. The goal is to model and simulate the developement of spatial centers using multi-criteria. We combine a decentralized approach based on emergent clustering mixed with spatial constraints or attractions. We propose an extension of the ant nest building algorithm with multi-center and adaptive process. Typically, this model is suitable to analyse and simulate urban dynamics like gentrification or the dynamics of the cultural equipment in urban area.
Swarm Intelligence for Urban Dynamics Modelling
Ghnemat, Rawan; Bertelle, Cyrille; Duchamp, Gérard H. E.
2009-04-01
In this paper, we propose swarm intelligence algorithms to deal with dynamical and spatial organization emergence. The goal is to model and simulate the developement of spatial centers using multi-criteria. We combine a decentralized approach based on emergent clustering mixed with spatial constraints or attractions. We propose an extension of the ant nest building algorithm with multi-center and adaptive process. Typically, this model is suitable to analyse and simulate urban dynamics like gentrification or the dynamics of the cultural equipment in urban area.
Causality in Europeanization Research
DEFF Research Database (Denmark)
Lynggaard, Kennet
2012-01-01
to develop discursive institutional analytical frameworks and something that comes close to the formulation of hypothesis on the effects of European Union (EU) policies and institutions on domestic change. Even if these efforts so far do not necessarily amount to substantive theories or claims of causality......Discourse analysis as a methodology is perhaps not readily associated with substantive causality claims. At the same time the study of discourses is very much the study of conceptions of causal relations among a set, or sets, of agents. Within Europeanization research we have seen endeavours......, it suggests that discourse analysis and the study of causality are by no means opposites. The study of Europeanization discourses may even be seen as an essential step in the move towards claims of causality in Europeanization research. This chapter deals with the question of how we may move from the study...
Directory of Open Access Journals (Sweden)
Thomas eWidlok
2014-11-01
Full Text Available Cognitive Scientists interested in causal cognition increasingly search for evidence from non-WEIRD people but find only very few cross-cultural studies that specifically target causal cognition. This article suggests how information about causality can be retrieved from ethnographic monographs, specifically from ethnographies that discuss agency and concepts of time. Many apparent cultural differences with regard to causal cognition dissolve when cultural extensions of agency and personhood to non-humans are taken into account. At the same time considerable variability remains when we include notions of time, linearity and sequence. The article focuses on ethnographic case studies from Africa but provides a more general perspective on the role of ethnography in research on the diversity and universality of causal cognition.
Understanding and Modeling Teams As Dynamical Systems
Gorman, Jamie C.; Dunbar, Terri A.; Grimm, David; Gipson, Christina L.
2017-01-01
By its very nature, much of teamwork is distributed across, and not stored within, interdependent people working toward a common goal. In this light, we advocate a systems perspective on teamwork that is based on general coordination principles that are not limited to cognitive, motor, and physiological levels of explanation within the individual. In this article, we present a framework for understanding and modeling teams as dynamical systems and review our empirical findings on teams as dynamical systems. We proceed by (a) considering the question of why study teams as dynamical systems, (b) considering the meaning of dynamical systems concepts (attractors; perturbation; synchronization; fractals) in the context of teams, (c) describe empirical studies of team coordination dynamics at the perceptual-motor, cognitive-behavioral, and cognitive-neurophysiological levels of analysis, and (d) consider the theoretical and practical implications of this approach, including new kinds of explanations of human performance and real-time analysis and performance modeling. Throughout our discussion of the topics we consider how to describe teamwork using equations and/or modeling techniques that describe the dynamics. Finally, we consider what dynamical equations and models do and do not tell us about human performance in teams and suggest future research directions in this area. PMID:28744231
An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems
Zenil, Hector
2017-09-08
We introduce a conceptual framework and an interventional calculus to steer and manipulate systems based on their intrinsic algorithmic probability using the universal principles of the theory of computability and algorithmic information. By applying sequences of controlled interventions to systems and networks, we estimate how changes in their algorithmic information content are reflected in positive/negative shifts towards and away from randomness. The strong connection between approximations to algorithmic complexity (the size of the shortest generating mechanism) and causality induces a sequence of perturbations ranking the network elements by the steering capabilities that each of them is capable of. This new dimension unmasks a separation between causal and non-causal components providing a suite of powerful parameter-free algorithms of wide applicability ranging from optimal dimension reduction, maximal randomness analysis and system control. We introduce methods for reprogramming systems that do not require the full knowledge or access to the system\\'s actual kinetic equations or any probability distributions. A causal interventional analysis of synthetic and regulatory biological networks reveals how the algorithmic reprogramming qualitatively reshapes the system\\'s dynamic landscape. For example, during cellular differentiation we find a decrease in the number of elements corresponding to a transition away from randomness and a combination of the system\\'s intrinsic properties and its intrinsic capabilities to be algorithmically reprogrammed can reconstruct an epigenetic landscape. The interventional calculus is broadly applicable to predictive causal inference of systems such as networks and of relevance to a variety of machine and causal learning techniques driving model-based approaches to better understanding and manipulate complex systems.
Dynamical models of spiral galaxies
International Nuclear Information System (INIS)
Grosbol, P.
1990-01-01
The effects of changing the basic parameters of rotation curve steepness, amount of bulge, and pitch angle of the imposed spiral pattern in the galactic model of Contoupolos and Grosbel (1986) are investigated. The general conclusions of the model are confirmed and shown to be insensitive to the specific choice of parameters for the galactic potential. The exact amplitude at which the nonlinear effects at the 4:1 resonance become important do, however, depend on the model
Energy Balance Models and Planetary Dynamics
Domagal-Goldman, Shawn
2012-01-01
We know that planetary dynamics can have a significant affect on the climate of planets. Planetary dynamics dominate the glacial-interglacial periods on Earth, leaving a significant imprint on the geological record. They have also been demonstrated to have a driving influence on the climates of other planets in our solar system. We should therefore expect th.ere to be similar relationships on extrasolar planets. Here we describe a simple energy balance model that can predict the growth and thickness of glaciers, and their feedbacks on climate. We will also describe model changes that we have made to include planetary dynamics effects. This is the model we will use at the start of our collaboration to handle the influence of dynamics on climate.
Stirling Engine Dynamic System Modeling
Nakis, Christopher G.
2004-01-01
The Thermo-Mechanical systems branch at the Glenn Research Center focuses a large amount time on Stirling engines. These engines will be used on missions where solar power is inefficient, especially in deep space. I work with Tim Regan and Ed Lewandowski who are currently developing and validating a mathematical model for the Stirling engines. This model incorporates all aspects of the system including, mechanical, electrical and thermodynamic components. Modeling is done through Simplorer, a program capable of running simulations of the model. Once created and then proven to be accurate, a model is used for developing new ideas for engine design. My largest specific project involves varying key parameters in the model and quantifying the results. This can all be done relatively trouble-free with the help of Simplorer. Once the model is complete, Simplorer will do all the necessary calculations. The more complicated part of this project is determining which parameters to vary. Finding key parameters depends on the potential for a value to be independently altered in the design. For example, a change in one dimension may lead to a proportional change to the rest of the model, and no real progress is made. Also, the ability for a changed value to have a substantial impact on the outputs of the system is important. Results will be condensed into graphs and tables with the purpose of better communication and understanding of the data. With the changing of these parameters, a more optimal design can be created without having to purchase or build any models. Also, hours and hours of results can be simulated in minutes. In the long run, using mathematical models can save time and money. Along with this project, I have many other smaller assignments throughout the summer. My main goal is to assist in the processes of model development, validation and testing.
Characterizing time series: when Granger causality triggers complex networks
International Nuclear Information System (INIS)
Ge Tian; Cui Yindong; Lin Wei; Liu Chong; Kurths, Jürgen
2012-01-01
In this paper, we propose a new approach to characterize time series with noise perturbations in both the time and frequency domains by combining Granger causality and complex networks. We construct directed and weighted complex networks from time series and use representative network measures to describe their physical and topological properties. Through analyzing the typical dynamical behaviors of some physical models and the MIT-BIH human electrocardiogram data sets, we show that the proposed approach is able to capture and characterize various dynamics and has much potential for analyzing real-world time series of rather short length. (paper)
Characterizing time series: when Granger causality triggers complex networks
Ge, Tian; Cui, Yindong; Lin, Wei; Kurths, Jürgen; Liu, Chong
2012-08-01
In this paper, we propose a new approach to characterize time series with noise perturbations in both the time and frequency domains by combining Granger causality and complex networks. We construct directed and weighted complex networks from time series and use representative network measures to describe their physical and topological properties. Through analyzing the typical dynamical behaviors of some physical models and the MIT-BIHMassachusetts Institute of Technology-Beth Israel Hospital. human electrocardiogram data sets, we show that the proposed approach is able to capture and characterize various dynamics and has much potential for analyzing real-world time series of rather short length.
mediation: R Package for Causal Mediation Analysis
Directory of Open Access Journals (Sweden)
Dustin Tingley
2014-09-01
Full Text Available In this paper, we describe the R package mediation for conducting causal mediation analysis in applied empirical research. In many scientific disciplines, the goal of researchers is not only estimating causal effects of a treatment but also understanding the process in which the treatment causally affects the outcome. Causal mediation analysis is frequently used to assess potential causal mechanisms. The mediation package implements a comprehensive suite of statistical tools for conducting such an analysis. The package is organized into two distinct approaches. Using the model-based approach, researchers can estimate causal mediation effects and conduct sensitivity analysis under the standard research design. Furthermore, the design-based approach provides several analysis tools that are applicable under different experimental designs. This approach requires weaker assumptions than the model-based approach. We also implement a statistical method for dealing with multiple (causally dependent mediators, which are often encountered in practice. Finally, the package also offers a methodology for assessing causal mediation in the presence of treatment noncompliance, a common problem in randomized trials.
Brand Equity Evolution: a System Dynamics Model
Directory of Open Access Journals (Sweden)
Edson Crescitelli
2009-04-01
Full Text Available One of the greatest challenges in brand management lies in monitoring brand equity over time. This paper aimsto present a simulation model able to represent this evolution. The model was drawn on brand equity concepts developed by Aaker and Joachimsthaler (2000, using the system dynamics methodology. The use ofcomputational dynamic models aims to create new sources of information able to sensitize academics and managers alike to the dynamic implications of their brand management. As a result, an easily implementable model was generated, capable of executing continuous scenario simulations by surveying casual relations among the variables that explain brand equity. Moreover, the existence of a number of system modeling tools will allow extensive application of the concepts used in this study in practical situations, both in professional and educational settings
Dynamic Modelling with "MLE-Energy Dynamic" for Primary School
Giliberti, Enrico; Corni, Federico
During the recent years simulation and modelling are growing instances in science education. In primary school, however, the main use of software is the simulation, due to the lack of modelling software tools specially designed to fit/accomplish the needs of primary education. In particular primary school teachers need to use simulation in a framework that is both consistent and simple enough to be understandable by children [2]. One of the possible area to approach modelling is about the construction of the concept of energy, in particular for what concerns the relations among substance, potential, power [3]. Following the previous initial research results with this approach [2], and with the static version of the software MLE Energy [1], we suggest the design and the experimentation of a dynamic modelling software—MLE dynamic-capable to represent dynamically the relations occurring when two substance-like quantities exchange energy, modifying their potential. By means of this software the user can graphically choose the dependent and independent variables and leave the other parameters fixed. The software has been initially evaluated, during a course of science education with a group of primary school teachers-to-be, to test the ability of the software to improve teachers' way of thinking in terms of substance-like quantities and their effects (graphical representation of the extensive, intensive variables and their mutual relations); moreover, the software has been tested with a group of primary school teachers, asking their opinion about the software didactical relevance in the class work.
Dynamics of the standard model
Donoghue, John F; Holstein, Barry R
2014-01-01
Describing the fundamental theory of particle physics and its applications, this book provides a detailed account of the Standard Model, focusing on techniques that can produce information about real observed phenomena. The book begins with a pedagogic account of the Standard Model, introducing essential techniques such as effective field theory and path integral methods. It then focuses on the use of the Standard Model in the calculation of physical properties of particles. Rigorous methods are emphasized, but other useful models are also described. This second edition has been updated to include recent theoretical and experimental advances, such as the discovery of the Higgs boson. A new chapter is devoted to the theoretical and experimental understanding of neutrinos, and major advances in CP violation and electroweak physics have been given a modern treatment. This book is valuable to graduate students and researchers in particle physics, nuclear physics and related fields.
Forecasting with Dynamic Regression Models
Pankratz, Alan
2012-01-01
One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.
Identity, causality, and pronoun ambiguity.
Sagi, Eyal; Rips, Lance J
2014-10-01
This article looks at the way people determine the antecedent of a pronoun in sentence pairs, such as: Albert invited Ron to dinner. He spent hours cleaning the house. The experiment reported here is motivated by the idea that such judgments depend on reasoning about identity (e.g., the identity of the he who cleaned the house). Because the identity of an individual over time depends on the causal-historical path connecting the stages of the individual, the correct antecedent will also depend on causal connections. The experiment varied how likely it is that the event of the first sentence (e.g., the invitation) would cause the event of the second (the house cleaning) for each of the two individuals (the likelihood that if Albert invited Ron to dinner, this would cause Albert to clean the house, versus cause Ron to clean the house). Decisions about the antecedent followed causal likelihood. A mathematical model of causal identity accounted for most of the key aspects of the data from the individual sentence pairs. Copyright © 2014 Cognitive Science Society, Inc.
Dynamic Modelling Of A SCARA Robot
Turiel, J. Perez; Calleja, R. Grossi; Diez, V. Gutierrez
1987-10-01
This paper describes a method for modelling industrial robots that considers dynamic approach to manipulation systems motion generation, obtaining the complete dynamic model for the mechanic part of the robot and taking into account the dynamic effect of actuators acting at the joints. For a four degree of freedom SCARA robot we obtain the dynamic model for the basic (minimal) configuration, that is, the three degrees of freedom that allow us to place the robot end effector in a desired point, using the Lagrange Method to obtain the dynamic equations in matrix form. The manipulator is considered to be a set of rigid bodies inter-connected by joints in the form of simple kinematic pairs. Then, the state space model is obtained for the actuators that move the robot joints, uniting the models of the single actuators, that is, two DC permanent magnet servomotors and an electrohydraulic actuator. Finally, using a computer simulation program written in FORTRAN language, we can compute the matrices of the complete model.
Dynamic modeling of IGCC power plants
International Nuclear Information System (INIS)
Casella, F.; Colonna, P.
2012-01-01
Integrated Gasification Combined Cycle (IGCC) power plants are an effective option to reduce emissions and implement carbon-dioxide sequestration. The combination of a very complex fuel-processing plant and a combined cycle power station leads to challenging problems as far as dynamic operation is concerned. Dynamic performance is extremely relevant because recent developments in the electricity market push toward an ever more flexible and varying operation of power plants. A dynamic model of the entire system and models of its sub-systems are indispensable tools in order to perform computer simulations aimed at process and control design. This paper presents the development of the lumped-parameters dynamic model of an entrained-flow gasifier, with special emphasis on the modeling approach. The model is implemented into software by means of the Modelica language and validated by comparison with one set of data related to the steady operation of the gasifier of the Buggenum power station in the Netherlands. Furthermore, in order to demonstrate the potential of the proposed modeling approach and the use of simulation for control design purposes, a complete model of an exemplary IGCC power plant, including its control system, has been developed, by re-using existing models of combined cycle plant components; the results of a load dispatch ramp simulation are presented and shortly discussed. - Highlights: ► The acausal dynamic model of an entrained gasifier has been developed. ► The model can be used to perform system optimization and control studies. ► The model has been validated using field data. ► Model use is illustrated with an example showing the transient of an IGCC plant.
Automated adaptive inference of phenomenological dynamical models
Daniels, Bryan
Understanding the dynamics of biochemical systems can seem impossibly complicated at the microscopic level: detailed properties of every molecular species, including those that have not yet been discovered, could be important for producing macroscopic behavior. The profusion of data in this area has raised the hope that microscopic dynamics might be recovered in an automated search over possible models, yet the combinatorial growth of this space has limited these techniques to systems that contain only a few interacting species. We take a different approach inspired by coarse-grained, phenomenological models in physics. Akin to a Taylor series producing Hooke's Law, forgoing microscopic accuracy allows us to constrain the search over dynamical models to a single dimension. This makes it feasible to infer dynamics with very limited data, including cases in which important dynamical variables are unobserved. We name our method Sir Isaac after its ability to infer the dynamical structure of the law of gravitation given simulated planetary motion data. Applying the method to output from a microscopically complicated but macroscopically simple biological signaling model, it is able to adapt the level of detail to the amount of available data. Finally, using nematode behavioral time series data, the method discovers an effective switch between behavioral attractors after the application of a painful stimulus.
D'Ariano, Giacomo Mauro
2018-07-13
Causality has never gained the status of a 'law' or 'principle' in physics. Some recent literature has even popularized the false idea that causality is a notion that should be banned from theory. Such misconception relies on an alleged universality of the reversibility of the laws of physics, based either on the determinism of classical theory, or on the multiverse interpretation of quantum theory, in both cases motivated by mere interpretational requirements for realism of the theory. Here, I will show that a properly defined unambiguous notion of causality is a theorem of quantum theory, which is also a falsifiable proposition of the theory. Such a notion of causality appeared in the literature within the framework of operational probabilistic theories. It is a genuinely theoretical notion, corresponding to establishing a definite partial order among events, in the same way as we do by using the future causal cone on Minkowski space. The notion of causality is logically completely independent of the misidentified concept of 'determinism', and, being a consequence of quantum theory, is ubiquitous in physics. In addition, as classical theory can be regarded as a restriction of quantum theory, causality holds also in the classical case, although the determinism of the theory trivializes it. I then conclude by arguing that causality naturally establishes an arrow of time. This implies that the scenario of the 'block Universe' and the connected 'past hypothesis' are incompatible with causality, and thus with quantum theory: they are both doomed to remain mere interpretations and, as such, are not falsifiable, similar to the hypothesis of 'super-determinism'.This article is part of a discussion meeting issue 'Foundations of quantum mechanics and their impact on contemporary society'. © 2018 The Author(s).
Online Learning of Industrial Manipulators' Dynamics Models
DEFF Research Database (Denmark)
Polydoros, Athanasios
2017-01-01
, it was compared with multiple other state-of-the-art machine learning algorithms. Moreover, the thesis presents the application of the proposed learning method on robot control for achieving trajectory execution while learning the inverse dynamics models on-the-fly . Also it is presented the application...... of the dynamics models. Those mainly derive from physics-based methods and thus they are based on physical properties which are hard to be calculated. In this thesis, is presented, a novel online machine learning approach which is able to model both inverse and forward dynamics models of industrial manipulators....... The proposed method belongs to the class of deep learning and exploits the concepts of self-organization, recurrent neural networks and iterative multivariate Bayesian regression. It has been evaluated on multiple datasets captured from industrial robots while they were performing various tasks. Also...
Dynamic optimization deterministic and stochastic models
Hinderer, Karl; Stieglitz, Michael
2016-01-01
This book explores discrete-time dynamic optimization and provides a detailed introduction to both deterministic and stochastic models. Covering problems with finite and infinite horizon, as well as Markov renewal programs, Bayesian control models and partially observable processes, the book focuses on the precise modelling of applications in a variety of areas, including operations research, computer science, mathematics, statistics, engineering, economics and finance. Dynamic Optimization is a carefully presented textbook which starts with discrete-time deterministic dynamic optimization problems, providing readers with the tools for sequential decision-making, before proceeding to the more complicated stochastic models. The authors present complete and simple proofs and illustrate the main results with numerous examples and exercises (without solutions). With relevant material covered in four appendices, this book is completely self-contained.
Dynamic modeling of the INAPRO aquaponic system
Karimanzira, Divas; Keesman, Karel J.; Kloas, Werner; Baganz, Daniela; Rauschenbach, Thomas
2016-01-01
The use of modeling techniques to analyze aquaponics systems is demonstrated with an example of dynamic modeling for the production of Nile tilapia (Oreochromis niloticus) and tomatoes (Solanum lycopersicon) using the innovative double recirculating aquaponic system ASTAF-PRO. For the management
Dynamic spatial panels : models, methods, and inferences
Elhorst, J. Paul
This paper provides a survey of the existing literature on the specification and estimation of dynamic spatial panel data models, a collection of models for spatial panels extended to include one or more of the following variables and/or error terms: a dependent variable lagged in time, a dependent
A Discrete Dynamical Model of Signed Partitions
Directory of Open Access Journals (Sweden)
G. Chiaselotti
2013-01-01
Full Text Available We use a discrete dynamical model with three evolution rules in order to analyze the structure of a partially ordered set of signed integer partitions whose main properties are actually not known. This model is related to the study of some extremal combinatorial sum problems.
Dynamic Factor Models for the Volatility Surface
DEFF Research Database (Denmark)
van der Wel, Michel; Ozturk, Sait R.; Dijk, Dick van
The implied volatility surface is the collection of volatilities implied by option contracts for different strike prices and time-to-maturity. We study factor models to capture the dynamics of this three-dimensional implied volatility surface. Three model types are considered to examine desirable...
Dynamical modeling of surface tension
International Nuclear Information System (INIS)
Brackbill, J.U.; Kothe, D.B.
1996-01-01
In a recent review it is said that free-surface flows ''represent some of the difficult remaining challenges in computational fluid dynamics''. There has been progress with the development of new approaches to treating interfaces, such as the level-set method and the improvement of older methods such as the VOF method. A common theme of many of the new developments has been the regularization of discontinuities at the interface. One example of this approach is the continuum surface force (CSF) formulation for surface tension, which replaces the surface stress given by Laplace's equation by an equivalent volume force. Here, we describe how CSF might be made more useful. Specifically, we consider a derivation of the CSF equations from a minimization of surface energy as outlined by Jacqmin. This reformulation suggests that if one eliminates the computation of curvature in terms of a unit normal vector, parasitic currents may be eliminated For this reformulation to work, it is necessary that transition region thickness be controlled. Various means for this, in addition to the one discussed by Jacqmin are discussed
Session 6: Dynamic Modeling and Systems Analysis
Csank, Jeffrey; Chapman, Jeffryes; May, Ryan
2013-01-01
These presentations cover some of the ongoing work in dynamic modeling and dynamic systems analysis. The first presentation discusses dynamic systems analysis and how to integrate dynamic performance information into the systems analysis. The ability to evaluate the dynamic performance of an engine design may allow tradeoffs between the dynamic performance and operability of a design resulting in a more efficient engine design. The second presentation discusses the Toolbox for Modeling and Analysis of Thermodynamic Systems (T-MATS). T-MATS is a Simulation system with a library containing the basic building blocks that can be used to create dynamic Thermodynamic Systems. Some of the key features include Turbo machinery components, such as turbines, compressors, etc., and basic control system blocks. T-MAT is written in the Matlab-Simulink environment and is open source software. The third presentation focuses on getting additional performance from the engine by allowing the limit regulators only to be active when a limit is danger of being violated. Typical aircraft engine control architecture is based on MINMAX scheme, which is designed to keep engine operating within prescribed mechanical/operational safety limits. Using a conditionally active min-max limit regulator scheme, additional performance can be gained by disabling non-relevant limit regulators
Mesoscale Models of Fluid Dynamics
Boghosian, Bruce M.; Hadjiconstantinou, Nicolas G.
During the last half century, enormous progress has been made in the field of computational materials modeling, to the extent that in many cases computational approaches are used in a predictive fashion. Despite this progress, modeling of general hydrodynamic behavior remains a challenging task. One of the main challenges stems from the fact that hydrodynamics manifests itself over a very wide range of length and time scales. On one end of the spectrum, one finds the fluid's "internal" scale characteristic of its molecular structure (in the absence of quantum effects, which we omit in this chapter). On the other end, the "outer" scale is set by the characteristic sizes of the problem's domain. The resulting scale separation or lack thereof as well as the existence of intermediate scales are key to determining the optimal approach. Successful treatments require a judicious choice of the level of description which is a delicate balancing act between the conflicting requirements of fidelity and manageable computational cost: a coarse description typically requires models for underlying processes occuring at smaller length and time scales; on the other hand, a fine-scale model will incur a significantly larger computational cost.
Modelling biased human trust dynamics
Hoogendoorn, M.; Jaffry, S.W.; Maanen, P.P. van; Treur, J.
2013-01-01
Abstract. Within human trust related behaviour, according to the literature from the domains of Psychology and Social Sciences often non-rational behaviour can be observed. Current trust models that have been developed typically do not incorporate non-rational elements in the trust formation
Dynamic model for a boiling water reactor
International Nuclear Information System (INIS)
Muscettola, M.
1963-07-01
A theoretical formulation is derived for the dynamics of a boiling water reactor of the pressure tube and forced circulation type. Attention is concentrated on neutron kinetics, fuel element heat transfer dynamics, and the primary circuit - that is the boiling channel, riser, steam drum, downcomer and recirculating pump of a conventional La Mont loop. Models for the steam and feedwater plant are not derived. (author)
Ito, Sosuke
2016-01-01
The transfer entropy is a well-established measure of information flow, which quantifies directed influence between two stochastic time series and has been shown to be useful in a variety fields of science. Here we introduce the transfer entropy of the backward time series called the backward transfer entropy, and show that the backward transfer entropy quantifies how far it is from dynamics to a hidden Markov model. Furthermore, we discuss physical interpretations of the backward transfer entropy in completely different settings of thermodynamics for information processing and the gambling with side information. In both settings of thermodynamics and the gambling, the backward transfer entropy characterizes a possible loss of some benefit, where the conventional transfer entropy characterizes a possible benefit. Our result implies the deep connection between thermodynamics and the gambling in the presence of information flow, and that the backward transfer entropy would be useful as a novel measure of information flow in nonequilibrium thermodynamics, biochemical sciences, economics and statistics. PMID:27833120
Rate-Agnostic (Causal) Structure Learning.
Plis, Sergey; Danks, David; Freeman, Cynthia; Calhoun, Vince
2015-12-01
Causal structure learning from time series data is a major scientific challenge. Extant algorithms assume that measurements occur sufficiently quickly; more precisely, they assume approximately equal system and measurement timescales. In many domains, however, measurements occur at a significantly slower rate than the underlying system changes, but the size of the timescale mismatch is often unknown. This paper develops three causal structure learning algorithms, each of which discovers all dynamic causal graphs that explain the observed measurement data, perhaps given undersampling. That is, these algorithms all learn causal structure in a "rate-agnostic" manner: they do not assume any particular relation between the measurement and system timescales. We apply these algorithms to data from simulations to gain insight into the challenge of undersampling.
van Dijk, Marjolein J A M; Claassen, Tom; Suwartono, Christiany; van der Veld, William M; van der Heijden, Paul T; Hendriks, Marc P H
Since the publication of the WAIS-IV in the U.S. in 2008, efforts have been made to explore the structural validity by applying factor analysis to various samples. This study aims to achieve a more fine-grained understanding of the structure of the Dutch language version of the WAIS-IV (WAIS-IV-NL) by applying an alternative analysis based on causal modeling in addition to confirmatory factor analysis (CFA). The Bayesian Constraint-based Causal Discovery (BCCD) algorithm learns underlying network structures directly from data and assesses more complex structures than is possible with factor analysis. WAIS-IV-NL profiles of two clinical samples of 202 patients (i.e. patients with temporal lobe epilepsy and a mixed psychiatric outpatient group) were analyzed and contrasted with a matched control group (N = 202) selected from the Dutch standardization sample of the WAIS-IV-NL to investigate internal structure by means of CFA and BCCD. With CFA, the four-factor structure as proposed by Wechsler demonstrates acceptable fit in all three subsamples. However, BCCD revealed three consistent clusters (verbal comprehension, visual processing, and processing speed) in all three subsamples. The combination of Arithmetic and Digit Span as a coherent working memory factor could not be verified, and Matrix Reasoning appeared to be isolated. With BCCD, some discrepancies from the proposed four-factor structure are exemplified. Furthermore, these results fit CHC theory of intelligence more clearly. Consistent clustering patterns indicate these results are robust. The structural causal discovery approach may be helpful in better interpreting existing tests, the development of new tests, and aid in diagnostic instruments.
A dynamical model for multifragmentation
International Nuclear Information System (INIS)
Ngo, H.; Ighezou, F.Z.; Ngo, C.
1999-01-01
The surface multifragmentation of highly excited (compression and thermal excitation) 208 Pb is investigated with a finite temperature spherical TDHF approximation coupled to a restructured aggregation model. This approach is discussed in terms of the data available from ALADIN collaboration at GSI on gold ion induced reactions on C, Al and Cu targets at 600 MeV/u excitation energy. The calculation showed that the slowest fragments originate in the nuclear volume while the smaller, faster fragments are emitted from surface
Nonlinear Dynamic Models in Advanced Life Support
Jones, Harry
2002-01-01
To facilitate analysis, ALS systems are often assumed to be linear and time invariant, but they usually have important nonlinear and dynamic aspects. Nonlinear dynamic behavior can be caused by time varying inputs, changes in system parameters, nonlinear system functions, closed loop feedback delays, and limits on buffer storage or processing rates. Dynamic models are usually cataloged according to the number of state variables. The simplest dynamic models are linear, using only integration, multiplication, addition, and subtraction of the state variables. A general linear model with only two state variables can produce all the possible dynamic behavior of linear systems with many state variables, including stability, oscillation, or exponential growth and decay. Linear systems can be described using mathematical analysis. Nonlinear dynamics can be fully explored only by computer simulations of models. Unexpected behavior is produced by simple models having only two or three state variables with simple mathematical relations between them. Closed loop feedback delays are a major source of system instability. Exceeding limits on buffer storage or processing rates forces systems to change operating mode. Different equilibrium points may be reached from different initial conditions. Instead of one stable equilibrium point, the system may have several equilibrium points, oscillate at different frequencies, or even behave chaotically, depending on the system inputs and initial conditions. The frequency spectrum of an output oscillation may contain harmonics and the sums and differences of input frequencies, but it may also contain a stable limit cycle oscillation not related to input frequencies. We must investigate the nonlinear dynamic aspects of advanced life support systems to understand and counter undesirable behavior.
System Dynamics Modeling of Multipurpose Reservoir Operation
Directory of Open Access Journals (Sweden)
Ebrahim Momeni
2006-03-01
Full Text Available System dynamics, a feedback – based object – oriented simulation approach, not only represents complex dynamic systemic systems in a realistic way but also allows the involvement of end users in model development to increase their confidence in modeling process. The increased speed of model development, the possibility of group model development, the effective communication of model results, and the trust developed in the model due to user participation are the main strengths of this approach. The ease of model modification in response to changes in the system and the ability to perform sensitivity analysis make this approach more attractive compared with systems analysis techniques for modeling water management systems. In this study, a system dynamics model was developed for the Zayandehrud basin in central Iran. This model contains river basin, dam reservoir, plains, irrigation systems, and groundwater. Current operation rule is conjunctive use of ground and surface water. Allocation factor for each irrigation system is computed based on the feedback from groundwater storage in its zone. Deficit water is extracted from groundwater.The results show that applying better rules can not only satisfy all demands such as Gawkhuni swamp environmental demand, but it can also prevent groundwater level drawdown in future.
Energy Technology Data Exchange (ETDEWEB)
Hunke, Elizabeth Clare [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Urrego Blanco, Jorge Rolando [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Urban, Nathan Mark [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2018-02-12
Coupled climate models have a large number of input parameters that can affect output uncertainty. We conducted a sensitivity analysis of sea ice proper:es and Arc:c related climate variables to 5 parameters in the HiLAT climate model: air-ocean turbulent exchange parameter (C), conversion of water vapor to clouds (cldfrc_rhminl) and of ice crystals to snow (micro_mg_dcs), snow thermal conduc:vity (ksno), and maximum snow grain size (rsnw_mlt). We used an elementary effect (EE) approach to rank their importance for output uncertainty. EE is an extension of one-at-a-time sensitivity analyses, but it is more efficient in sampling multi-dimensional parameter spaces. We looked for emerging relationships among climate variables across the model ensemble, and used causal discovery algorithms to establish potential pathways for those relationships.
International Nuclear Information System (INIS)
Maund, J.B.
1979-01-01
Although the existence of tachyons is not ruled out by special relativity, it appears that causal paradoxes will arise if there are tachyons. The usual solutions to these paradoxes employ some form of the reinterpretation principle. In this paper it is argued first that, the principle is incoherent, second, that even if it is not, some causal paradoxes remain, and third, the most plausible ''solution,'' which appeals to boundary conditions of the universe, will conflict with special relativity
Modeling and identification in structural dynamics
Jayakumar, Paramsothy
1987-01-01
Analytical modeling of structures subjected to ground motions is an important aspect of fully dynamic earthquake-resistant design. In general, linear models are only sufficient to represent structural responses resulting from earthquake motions of small amplitudes. However, the response of structures during strong ground motions is highly nonlinear and hysteretic. System identification is an effective tool for developing analytical models from experimental data. Testing of full-scale prot...
Feature Extraction for Structural Dynamics Model Validation
Energy Technology Data Exchange (ETDEWEB)
Farrar, Charles [Los Alamos National Laboratory; Nishio, Mayuko [Yokohama University; Hemez, Francois [Los Alamos National Laboratory; Stull, Chris [Los Alamos National Laboratory; Park, Gyuhae [Chonnam Univesity; Cornwell, Phil [Rose-Hulman Institute of Technology; Figueiredo, Eloi [Universidade Lusófona; Luscher, D. J. [Los Alamos National Laboratory; Worden, Keith [University of Sheffield
2016-01-13
As structural dynamics becomes increasingly non-modal, stochastic and nonlinear, finite element model-updating technology must adopt the broader notions of model validation and uncertainty quantification. For example, particular re-sampling procedures must be implemented to propagate uncertainty through a forward calculation, and non-modal features must be defined to analyze nonlinear data sets. The latter topic is the focus of this report, but first, some more general comments regarding the concept of model validation will be discussed.
Record Dynamics and the Parking Lot Model for granular dynamics
Sibani, Paolo; Boettcher, Stefan
Also known for its application to granular compaction (E. Ben-Naim et al., Physica D, 1998), the Parking Lot Model (PLM) describes the random parking of identical cars in a strip with no marked bays. In the thermally activated version considered, cars can be removed at an energy cost and, in thermal equilibrium, their average density increases as temperature decreases. However, equilibration at high density becomes exceedingly slow and the system enters an aging regime induced by a kinematic constraint, the fact that parked cars may not overlap. As parking an extra car reduces the available free space,the next parking event is even harder to achieve. Records in the number of parked cars mark the salient features of the dynamics and are shown to be well described by the log-Poisson statistics known from other glassy systems with record dynamics. Clusters of cars whose positions must be rearranged to make the next insertion possible have a length scale which grows logarithmically with age, while their life-time grows exponentially with size. The implications for a recent cluster model of colloidal dynamics,(S. Boettcher and P. Sibani, J. Phys.: Cond. Matter, 2011 N. Becker et al., J. Phys.: Cond. Matter, 2014) are discussed. Support rom the Villum Foundation is gratefully acknowledged.
Rehan, R; Knight, M A; Unger, A J A; Haas, C T
2013-12-15
This paper develops causal loop diagrams and a system dynamics model for financially sustainable management of urban water distribution networks. The developed causal loop diagrams are a novel contribution in that it illustrates the unique characteristics and feedback loops for financially self-sustaining water distribution networks. The system dynamics model is a mathematical realization of the developed interactions among system variables over time and is comprised of three sectors namely watermains network, consumer, and finance. This is the first known development of a water distribution network system dynamics model. The watermains network sector accounts for the unique characteristics of watermain pipes such as service life, deterioration progression, pipe breaks, and water leakage. The finance sector allows for cash reserving by the utility in addition to the pay-as-you-go and borrowing strategies. The consumer sector includes controls to model water fee growth as a function of service performance and a household's financial burden due to water fees. A series of policy levers are provided that allow the impact of various financing strategies to be evaluated in terms of financial sustainability and household affordability. The model also allows for examination of the impact of different management strategies on the water fee in terms of consistency and stability over time. The paper concludes with a discussion on how the developed system dynamics water model can be used by water utilities to achieve a variety of utility short and long-term objectives and to establish realistic and defensible water utility policies. It also discusses how the model can be used by regulatory bodies, government agencies, the financial industry, and researchers. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.
Modeling the dynamics of dissent
Lee, Eun; Holme, Petter; Lee, Sang Hoon
2017-11-01
We investigate the formation of opinion against authority in an authoritarian society composed of agents with different levels of authority. We explore a ;dissenting; opinion, held by lower-ranking, obedient, or less authoritative people, spreading in an environment of an ;affirmative; opinion held by authoritative leaders. A real-world example would be a corrupt society where people revolt against such leaders, but it can be applied to more general situations. In our model, agents can change their opinion depending on their authority relative to their neighbors and their own confidence level. In addition, with a certain probability, agents can override the affirmative opinion to take the dissenting opinion of a neighbor. Based on analytic derivation and numerical simulations, we observe that both the network structure and heterogeneity in authority, and their correlation, significantly affect the possibility of the dissenting opinion to spread through the population. In particular, the dissenting opinion is suppressed when the authority distribution is very heterogeneous and there exists a positive correlation between the authority and the number of neighbors of people (degree). Except for such an extreme case, though, spreading of the dissenting opinion takes place when people have the tendency to override the authority to hold the dissenting opinion, but the dissenting opinion can take a long time to spread to the entire society, depending on the model parameters. We argue that the internal social structure of agents sets the scale of the time to reach consensus, based on the analysis of the underlying structural properties of opinion spreading.
Modeling Dynamic Regulatory Processes in Stroke
McDermott, Jason E.; Jarman, Kenneth; Taylor, Ronald; Lancaster, Mary; Shankaran, Harish; Vartanian, Keri B.; Stevens, Susan L.; Stenzel-Poore, Mary P.; Sanfilippo, Antonio
2012-01-01
The ability to examine the behavior of biological systems in silico has the potential to greatly accelerate the pace of discovery in diseases, such as stroke, where in vivo analysis is time intensive and costly. In this paper we describe an approach for in silico examination of responses of the blood transcriptome to neuroprotective agents and subsequent stroke through the development of dynamic models of the regulatory processes observed in the experimental gene expression data. First, we identified functional gene clusters from these data. Next, we derived ordinary differential equations (ODEs) from the data relating these functional clusters to each other in terms of their regulatory influence on one another. Dynamic models were developed by coupling these ODEs into a model that simulates the expression of regulated functional clusters. By changing the magnitude of gene expression in the initial input state it was possible to assess the behavior of the networks through time under varying conditions since the dynamic model only requires an initial starting state, and does not require measurement of regulatory influences at each time point in order to make accurate predictions. We discuss the implications of our models on neuroprotection in stroke, explore the limitations of the approach, and report that an optimized dynamic model can provide accurate predictions of overall system behavior under several different neuroprotective paradigms. PMID:23071432
Coupling population dynamics with earth system models: the POPEM model.
Navarro, Andrés; Moreno, Raúl; Jiménez-Alcázar, Alfonso; Tapiador, Francisco J
2017-09-16
Precise modeling of CO 2 emissions is important for environmental research. This paper presents a new model of human population dynamics that can be embedded into ESMs (Earth System Models) to improve climate modeling. Through a system dynamics approach, we develop a cohort-component model that successfully simulates historical population dynamics with fine spatial resolution (about 1°×1°). The population projections are used to improve the estimates of CO 2 emissions, thus transcending the bulk approach of existing models and allowing more realistic non-linear effects to feature in the simulations. The module, dubbed POPEM (from Population Parameterization for Earth Models), is compared with current emission inventories and validated against UN aggregated data. Finally, it is shown that the module can be used to advance toward fully coupling the social and natural components of the Earth system, an emerging research path for environmental science and pollution research.
Efficient nonparametric estimation of causal mediation effects
Chan, K. C. G.; Imai, K.; Yam, S. C. P.; Zhang, Z.
2016-01-01
An essential goal of program evaluation and scientific research is the investigation of causal mechanisms. Over the past several decades, causal mediation analysis has been used in medical and social sciences to decompose the treatment effect into the natural direct and indirect effects. However, all of the existing mediation analysis methods rely on parametric modeling assumptions in one way or another, typically requiring researchers to specify multiple regression models involving the treat...
Dynamical properties of the Rabi model
International Nuclear Information System (INIS)
Hu, Binglu; Zhou, Huili; Chen, Shujie; Xianlong, Gao; Wang, Kelin
2017-01-01
We study the dynamical properties of the quantum Rabi model using a systematic expansion method. Based on the observation that the parity symmetry of the Rabi model is kept during evolution of the states, we decompose the initial state and the time-dependent one into positive and negative parity parts expanded by superposition of the coherent states. The evolutions of the corresponding positive and the negative parities are obtained, in which the expansion coefficients in the dynamical equations are known from the derived recurrence relation. (paper)
Dynamic Modeling of ThermoFluid Systems
DEFF Research Database (Denmark)
Jensen, Jakob Munch
2003-01-01
The objective of the present study has been to developed dynamic models for two-phase flow in pipes (evaporation and condensation). Special attention has been given to modeling evaporators for refrigeration plant particular dry-expansion evaporators. Models of different complexity have been...... formulated. The different models deviate with respect to the detail¿s included and calculation time in connection with simulation. The models have been implemented in a new library named ThermoTwoPhase to the programming language Modelica. A test rig has been built with an evaporator instrumented in a way...
Research on nonlinear stochastic dynamical price model
International Nuclear Information System (INIS)
Li Jiaorui; Xu Wei; Xie Wenxian; Ren Zhengzheng
2008-01-01
In consideration of many uncertain factors existing in economic system, nonlinear stochastic dynamical price model which is subjected to Gaussian white noise excitation is proposed based on deterministic model. One-dimensional averaged Ito stochastic differential equation for the model is derived by using the stochastic averaging method, and applied to investigate the stability of the trivial solution and the first-passage failure of the stochastic price model. The stochastic price model and the methods presented in this paper are verified by numerical studies
Modelling environmental dynamics. Advances in goematic solutions
Energy Technology Data Exchange (ETDEWEB)
Paegelow, Martin [Toulouse-2 Univ., 31 (France). GEODE UMR 5602 CNRS; Camacho Olmedo, Maria Teresa (eds.) [Granada Univ (Spain). Dpto. de Analisis Geografico Regional y Geografia Fisica
2008-07-01
Modelling environmental dynamics is critical to understanding and predicting the evolution of the environment in response to the large number of influences including urbanisation, climate change and deforestation. Simulation and modelling provide support for decision making in environmental management. The first chapter introduces terminology and provides an overview of methodological modelling approaches which may be applied to environmental and complex dynamics. Based on this introduction this book illustrates various models applied to a large variety of themes: deforestation in tropical regions, fire risk, natural reforestation in European mountains, agriculture, biodiversity, urbanism, climate change and land management for decision support, etc. These case studies, provided by a large international spectrum of researchers and presented in a uniform structure, focus particularly on methods and model validation so that this book is not only aimed at researchers and graduates but also at professionals. (orig.)
Modeling emotional dynamics : currency versus field.
Energy Technology Data Exchange (ETDEWEB)
Sallach, D .L.; Decision and Information Sciences; Univ. of Chicago
2008-08-01
Randall Collins has introduced a simplified model of emotional dynamics in which emotional energy, heightened and focused by interaction rituals, serves as a common denominator for social exchange: a generic form of currency, except that it is active in a far broader range of social transactions. While the scope of this theory is attractive, the specifics of the model remain unconvincing. After a critical assessment of the currency theory of emotion, a field model of emotion is introduced that adds expressiveness by locating emotional valence within its cognitive context, thereby creating an integrated orientation field. The result is a model which claims less in the way of motivational specificity, but is more satisfactory in modeling the dynamic interaction between cognitive and emotional orientations at both individual and social levels.
Padula, Amy M.; Mortimer, Kathleen; Hubbard, Alan; Lurmann, Frederick; Jerrett, Michael; Tager, Ira B.
2012-01-01
Traffic-related air pollution is recognized as an important contributor to health problems. Epidemiologic analyses suggest that prenatal exposure to traffic-related air pollutants may be associated with adverse birth outcomes; however, there is insufficient evidence to conclude that the relation is causal. The Study of Air Pollution, Genetics and Early Life Events comprises all births to women living in 4 counties in California's San Joaquin Valley during the years 2000–2006. The probability of low birth weight among full-term infants in the population was estimated using machine learning and targeted maximum likelihood estimation for each quartile of traffic exposure during pregnancy. If everyone lived near high-volume freeways (approximated as the fourth quartile of traffic density), the estimated probability of term low birth weight would be 2.27% (95% confidence interval: 2.16, 2.38) as compared with 2.02% (95% confidence interval: 1.90, 2.12) if everyone lived near smaller local roads (first quartile of traffic density). Assessment of potentially causal associations, in the absence of arbitrary model assumptions applied to the data, should result in relatively unbiased estimates. The current results support findings from previous studies that prenatal exposure to traffic-related air pollution may adversely affect birth weight among full-term infants. PMID:23045474
Hagmayer, York; Engelmann, Neele
2014-01-01
Cognitive psychological research focuses on causal learning and reasoning while cognitive anthropological and social science research tend to focus on systems of beliefs. Our aim was to explore how these two types of research can inform each other. Cognitive psychological theories (causal model theory and causal Bayes nets) were used to derive predictions for systems of causal beliefs. These predictions were then applied to lay theories of depression as a specific test case. A systematic lite...
A frequency domain subspace algorithm for mixed causal, anti-causal LTI systems
Fraanje, Rufus; Verhaegen, Michel; Verdult, Vincent; Pintelon, Rik
2003-01-01
The paper extends the subspacc identification method to estimate state-space models from frequency response function (FRF) samples, proposed by McKelvey et al. (1996) for mixed causal/anti-causal systems, and shows that other frequency domain subspace algorithms can be extended similarly. The method
Entanglement, non-Markovianity, and causal non-separability
Milz, Simon; Pollock, Felix A.; Le, Thao P.; Chiribella, Giulio; Modi, Kavan
2018-03-01
Quantum mechanics, in principle, allows for processes with indefinite causal order. However, most of these causal anomalies have not yet been detected experimentally. We show that every such process can be simulated experimentally by means of non-Markovian dynamics with a measurement on additional degrees of freedom. In detail, we provide an explicit construction to implement arbitrary a causal processes. Furthermore, we give necessary and sufficient conditions for open system dynamics with measurement to yield processes that respect causality locally, and find that tripartite entanglement and nonlocal unitary transformations are crucial requirements for the simulation of causally indefinite processes. These results show a direct connection between three counter-intuitive concepts: entanglement, non-Markovianity, and causal non-separability.
Modeling initial contact dynamics during ambulation with dynamic simulation.
Meyer, Andrew R; Wang, Mei; Smith, Peter A; Harris, Gerald F
2007-04-01
Ankle-foot orthoses are frequently used interventions to correct pathological gait. Their effects on the kinematics and kinetics of the proximal joints are of great interest when prescribing ankle-foot orthoses to specific patient groups. Mathematical Dynamic Model (MADYMO) is developed to simulate motor vehicle crash situations and analyze tissue injuries of the occupants based multibody dynamic theories. Joint kinetics output from an inverse model were perturbed and input to the forward model to examine the effects of changes in the internal sagittal ankle moment on knee and hip kinematics following heel strike. Increasing the internal ankle moment (augmentation, equivalent to gastroc-soleus contraction) produced less pronounced changes in kinematic results at the hip, knee and ankle than decreasing the moment (attenuation, equivalent to gastroc-soleus relaxation). Altering the internal ankle moment produced two distinctly different kinematic curve morphologies at the hip. Decreased internal ankle moments increased hip flexion, peaking at roughly 8% of the gait cycle. Increasing internal ankle moments decreased hip flexion to a lesser degree, and approached normal at the same point in the gait cycle. Increasing the internal ankle moment produced relatively small, well-behaved extension-biased kinematic results at the knee. Decreasing the internal ankle moment produced more substantial changes in knee kinematics towards flexion that increased with perturbation magnitude. Curve morphologies were similar to those at the hip. Immediately following heel strike, kinematic results at the ankle showed movement in the direction of the internal moment perturbation. Increased internal moments resulted in kinematic patterns that rapidly approach normal after initial differences. When the internal ankle moment was decreased, differences from normal were much greater and did not rapidly decrease. This study shows that MADYMO can be successfully applied to accomplish forward
A Stochastic Model for Malaria Transmission Dynamics
Directory of Open Access Journals (Sweden)
Rachel Waema Mbogo
2018-01-01
Full Text Available Malaria is one of the three most dangerous infectious diseases worldwide (along with HIV/AIDS and tuberculosis. In this paper we compare the disease dynamics of the deterministic and stochastic models in order to determine the effect of randomness in malaria transmission dynamics. Relationships between the basic reproduction number for malaria transmission dynamics between humans and mosquitoes and the extinction thresholds of corresponding continuous-time Markov chain models are derived under certain assumptions. The stochastic model is formulated using the continuous-time discrete state Galton-Watson branching process (CTDSGWbp. The reproduction number of deterministic models is an essential quantity to predict whether an epidemic will spread or die out. Thresholds for disease extinction from stochastic models contribute crucial knowledge on disease control and elimination and mitigation of infectious diseases. Analytical and numerical results show some significant differences in model predictions between the stochastic and deterministic models. In particular, we find that malaria outbreak is more likely if the disease is introduced by infected mosquitoes as opposed to infected humans. These insights demonstrate the importance of a policy or intervention focusing on controlling the infected mosquito population if the control of malaria is to be realized.
Modeling biological pathway dynamics with timed automata.
Schivo, Stefano; Scholma, Jetse; Wanders, Brend; Urquidi Camacho, Ricardo A; van der Vet, Paul E; Karperien, Marcel; Langerak, Rom; van de Pol, Jaco; Post, Janine N
2014-05-01
Living cells are constantly subjected to a plethora of environmental stimuli that require integration into an appropriate cellular response. This integration takes place through signal transduction events that form tightly interconnected networks. The understanding of these networks requires capturing their dynamics through computational support and models. ANIMO (analysis of Networks with Interactive Modeling) is a tool that enables the construction and exploration of executable models of biological networks, helping to derive hypotheses and to plan wet-lab experiments. The tool is based on the formalism of Timed Automata, which can be analyzed via the UPPAAL model checker. Thanks to Timed Automata, we can provide a formal semantics for the domain-specific language used to represent signaling networks. This enforces precision and uniformity in the definition of signaling pathways, contributing to the integration of isolated signaling events into complex network models. We propose an approach to discretization of reaction kinetics that allows us to efficiently use UPPAAL as the computational engine to explore the dynamic behavior of the network of interest. A user-friendly interface hides the use of Timed Automata from the user, while keeping the expressive power intact. Abstraction to single-parameter kinetics speeds up construction of models that remain faithful enough to provide meaningful insight. The resulting dynamic behavior of the network components is displayed graphically, allowing for an intuitive and interactive modeling experience.
Dynamic Model Averaging in Large Model Spaces Using Dynamic Occam’s Window*
Onorante, Luca; Raftery, Adrian E.
2015-01-01
Bayesian model averaging has become a widely used approach to accounting for uncertainty about the structural form of the model generating the data. When data arrive sequentially and the generating model can change over time, Dynamic Model Averaging (DMA) extends model averaging to deal with this situation. Often in macroeconomics, however, many candidate explanatory variables are available and the number of possible models becomes too large for DMA to be applied in its original form. We propose a new method for this situation which allows us to perform DMA without considering the whole model space, but using a subset of models and dynamically optimizing the choice of models at each point in time. This yields a dynamic form of Occam’s window. We evaluate the method in the context of the problem of nowcasting GDP in the Euro area. We find that its forecasting performance compares well with that of other methods. PMID:26917859
Dynamic Model Averaging in Large Model Spaces Using Dynamic Occam's Window.
Onorante, Luca; Raftery, Adrian E
2016-01-01
Bayesian model averaging has become a widely used approach to accounting for uncertainty about the structural form of the model generating the data. When data arrive sequentially and the generating model can change over time, Dynamic Model Averaging (DMA) extends model averaging to deal with this situation. Often in macroeconomics, however, many candidate explanatory variables are available and the number of possible models becomes too large for DMA to be applied in its original form. We propose a new method for this situation which allows us to perform DMA without considering the whole model space, but using a subset of models and dynamically optimizing the choice of models at each point in time. This yields a dynamic form of Occam's window. We evaluate the method in the context of the problem of nowcasting GDP in the Euro area. We find that its forecasting performance compares well with that of other methods.
Source characterization and dynamic fault modeling of induced seismicity
Lui, S. K. Y.; Young, R. P.
2017-12-01
In recent years there are increasing concerns worldwide that industrial activities in the sub-surface can cause or trigger damaging earthquakes. In order to effectively mitigate the damaging effects of induced seismicity, the key is to better understand the source physics of induced earthquakes, which still remain elusive at present. Furthermore, an improved understanding of induced earthquake physics is pivotal to assess large-magnitude earthquake triggering. A better quantification of the possible causes of induced earthquakes can be achieved through numerical simulations. The fault model used in this study is governed by the empirically-derived rate-and-state friction laws, featuring a velocity-weakening (VW) patch embedded into a large velocity-strengthening (VS) region. Outside of that, the fault is slipping at the background loading rate. The model is fully dynamic, with all wave effects resolved, and is able to resolve spontaneous long-term slip history on a fault segment at all stages of seismic cycles. An earlier study using this model has established that aseismic slip plays a major role in the triggering of small repeating earthquakes. This study presents a series of cases with earthquakes occurring on faults with different fault frictional properties and fluid-induced stress perturbations. The effects to both the overall seismicity rate and fault slip behavior are investigated, and the causal relationship between the pre-slip pattern prior to the event and the induced source characteristics is discussed. Based on simulation results, the subsequent step is to select specific cases for laboratory experiments which allow well controlled variables and fault parameters. Ultimately, the aim is to provide better constraints on important parameters for induced earthquakes based on numerical modeling and laboratory data, and hence to contribute to a physics-based induced earthquake hazard assessment.
On the mathematical modeling of soccer dynamics
Machado, J. A. Tenreiro; Lopes, António M.
2017-12-01
This paper addresses the modeling and dynamical analysis of soccer teams. Two modeling perspectives based on the concepts of fractional calculus are adopted. In the first, the power law behavior and fractional-order integration are explored. In the second, a league season is interpreted in the light of a system where the teams are represented by objects (particles) that evolve in time and interact (collide) at successive rounds with dynamics driven by the outcomes of the matches. The two proposed models embed implicitly details of players and coaches, or strategical and tactical maneuvers during the matches. Therefore, the scale of observation focuses on the teams behavior in the scope of the observed variables. Data characterizing two European soccer leagues in the season 2015-2016 are adopted and processed. The model leads to the emergence of patterns that are analyzed and interpreted.
BWR stability using a reducing dynamical model
International Nuclear Information System (INIS)
Ballestrin Bolea, J. M.; Blazquez Martinez, J. B.
1990-01-01
BWR stability can be treated with reduced order dynamical models. When the parameters of the model came from dynamical models. When the parameters of the model came from experimental data, the predictions are accurate. In this work an alternative derivation for the void fraction equation is made, but remarking the physical structure of the parameters. As the poles of power/reactivity transfer function are related with the parameters, the measurement of the poles by other techniques such as noise analysis will lead to the parameters, but the system of equations is non-linear. Simple parametric calculation of decay ratio are performed, showing why BWRs become unstable when they are operated at low flow and high power. (Author)
THE CAUSAL ANALYSIS / DIAGNOSIS DECISION ...
CADDIS is an on-line decision support system that helps investigators in the regions, states and tribes find, access, organize, use and share information to produce causal evaluations in aquatic systems. It is based on the US EPA's Stressor Identification process which is a formal method for identifying causes of impairments in aquatic systems. CADDIS 2007 increases access to relevant information useful for causal analysis and provides methods and tools that practitioners can use to analyze their own data. The new Candidate Cause section provides overviews of commonly encountered causes of impairments to aquatic systems: metals, sediments, nutrients, flow alteration, temperature, ionic strength, and low dissolved oxygen. CADDIS includes new Conceptual Models that illustrate the relationships from sources to stressors to biological effects. An Interactive Conceptual Model for phosphorus links the diagram with supporting literature citations. The new Analyzing Data section helps practitioners analyze their data sets and interpret and use those results as evidence within the USEPA causal assessment process. Downloadable tools include a graphical user interface statistical package (CADStat), and programs for use with the freeware R statistical package, and a Microsoft Excel template. These tools can be used to quantify associations between causes and biological impairments using innovative methods such as species-sensitivity distributions, biological inferenc
Causal structure of analogue spacetimes
International Nuclear Information System (INIS)
Barcelo, Carlos; Liberati, Stefano; Sonego, Sebastiano; Visser, Matt
2004-01-01
The so-called 'analogue models of general relativity' provide a number of specific physical systems, well outside the traditional realm of general relativity, that nevertheless are well-described by the differential geometry of curved spacetime. Specifically, the propagation of perturbations in these condensed matter systems is described by 'effective metrics' that carry with them notions of 'causal structure' as determined by an exchange of quasi-particles. These quasi-particle-induced causal structures serve as specific examples of what can be done in the presence of a Lorentzian metric without having recourse to the Einstein equations of general relativity. (After all, the underlying analogue model is governed by its own specific physics, not necessarily by the Einstein equations.) In this paper we take a careful look at what can be said about the causal structure of analogue spacetimes, focusing on those containing quasi-particle horizons, both with a view to seeing what is different from standard general relativity, and what the similarities might be. For definiteness, and because the physics is particularly simple to understand, we will phrase much of the discussion in terms of acoustic disturbances in moving fluids, where the underlying physics is ordinary fluid mechanics, governed by the equations of traditional hydrodynamics, and the relevant quasi-particles are the phonons. It must however be emphasized that this choice of example is only for the sake of pedagogical simplicity and that our considerations apply generically to wide classes of analogue spacetimes
Modelling the Dynamics of Emotional Awareness
Thilakarathne, D.J.; Treur, J.; Schaub, T.
2014-01-01
In this paper, based on literature from Cognitive and Affective Neuroscience, a computational agent model is introduced incorporating the role of emotional awareness states in the dynamics of action generation. More specifically, it covers both automatic, unconscious (bottom-up) and more cognitive
Object Oriented Modelling and Dynamical Simulation
DEFF Research Database (Denmark)
Wagner, Falko Jens; Poulsen, Mikael Zebbelin
1998-01-01
This report with appendix describes the work done in master project at DTU.The goal of the project was to develop a concept for simulation of dynamical systems based on object oriented methods.The result was a library of C++-classes, for use when both building componentbased models and when...
Modeling the population dynamics of Pacific yew.
Richard T. Busing; Thomas A. Spies
1995-01-01
A study of Pacific yew (Taxus brevifolia Nutt.) population dynamics in the mountains of western Oregon and Washington was based on a combination of long-term population data and computer modeling. Rates of growth and mortality were low in mature and old-growth forest stands. Diameter growth at breast height ranged from 0 to 3 centimeters per decade...
CFTSIM-ITER dynamic fuel cycle model
International Nuclear Information System (INIS)
Busigin, A.; Gierszewski, P.
1998-01-01
Dynamic system models have been developed for specific tritium systems with considerable detail and for integrated fuel cycles with lesser detail (e.g. D. Holland, B. Merrill, Analysis of tritium migration and deposition in fusion reactor systems, Proceedings of the Ninth Symposium Eng. Problems of Fusion Research (1981); M.A. Abdou, E. Vold, C. Gung, M. Youssef, K. Shin, DT fuel self-sufficiency in fusion reactors, Fusion Technol. (1986); G. Spannagel, P. Gierszewski, Dynamic tritium inventory of a NET/ITER fuel cycle with lithium salt solution blanket, Fusion Eng. Des. (1991); W. Kuan, M.A. Abdou, R.S. Willms, Dynamic simulation of a proposed ITER tritium processing system, Fusion Technol. (1995)). In order to provide a tool to understand and optimize the behavior of the ITER fuel cycle, a dynamic fuel cycle model called CFTSIM is under development. The CFTSIM code incorporates more detailed ITER models, specifically for the important isotope separation system, and also has an easier-to-use graphical interface. This paper provides an overview of CFTSIM Version 1.0. The models included are those with significant and varying tritium inventories over a test campaign: fueling, plasma and first wall, pumping, fuel cleanup, isotope separation and storage. An illustration of the results is shown. (orig.)
The quantum Rabi model: solution and dynamics
International Nuclear Information System (INIS)
Xie, Qiongtao; Zhong, Honghua; Lee, Chaohong; Batchelor, Murray T
2017-01-01
This article presents a review of recent developments on various aspects of the quantum Rabi model. Particular emphasis is given on the exact analytic solution obtained in terms of confluent Heun functions. The analytic solutions for various generalisations of the quantum Rabi model are also discussed. Results are also reviewed on the level statistics and the dynamics of the quantum Rabi model. The article concludes with an introductory overview of several experimental realisations of the quantum Rabi model. An outlook towards future developments is also given. (topical review)
A complete dynamic model of primary sedimentation.
Paraskevas, P; Kolokithas, G; Lekkas, T
1993-11-01
A dynamic mathematical model for the primary clarifier of a wastewater treatment plant is described, which is represented by a general tanks-in-series model, to simulate insufficient mixing. The model quantifies successfully the diurnal response of both the suspended and dissolved species. It is general enough, so that the values of the parameters can be replaced with those applicable to a specific case. The model was verified through data from the Biological Centre of Metamorfosi, in Athens, Greece, and can be used to assist in the design of new plants or in the analysis and output predictions of existing ones.
Dynamic Modeling of CDS Index Tranche Spreads
DEFF Research Database (Denmark)
Dorn, Jochen
This paper provides a Market Model which implies a dynamics for standardized CDS index tranche spreads, i.e. tranches which securitise CDS index series and dispose of predefined subordination. This model is useful for pricing options on tranches with future Issue Dates as well as for modeling...... options on structured credit derivatives. With the upcoming regulation of the CDS market in perspective, the model presented here is also an attempt to face the effects on pricing approaches provoked by an eventual Clearing Chamber . It becomes also possible to calibrate Index Tranche Options with bespoke...... tenors/tranche subordination to market data obtained by more liquid Index Tranche Options with standard characteristics....
Uncertainty and its propagation in dynamics models
International Nuclear Information System (INIS)
Devooght, J.
1994-01-01
The purpose of this paper is to bring together some characteristics due to uncertainty when we deal with dynamic models and therefore to propagation of uncertainty. The respective role of uncertainty and inaccuracy is examined. A mathematical formalism based on Chapman-Kolmogorov equation allows to define a open-quotes subdynamicsclose quotes where the evolution equation takes the uncertainty into account. The problem of choosing or combining models is examined through a loss function associated to a decision
Doest, ter L.; Jonge, de J.
2006-01-01
This study re-evaluated causal relationships between job characteristics (demands, autonomy, social support) and employee well-being (job satisfaction, emotional exhaustion) in a methodological replication of De Jonge et al.'s (2001) two-wave panel study. The principal difference was the 2-year time
Dynamic multibody modeling for tethered space elevators
Williams, Paul
2009-08-01
This paper presents a fundamental modeling strategy for dealing with powered and propelled bodies moving along space tethers. The tether is divided into a large number of discrete masses, which are connected by viscoelastic springs. The tether is subject to the full range of forces expected in Earth orbit in a relatively simple manner. Two different models of the elevator dynamics are presented. In order to capture the effect of the elevator moving along the tether, the elevator dynamics are included as a separate body in both models. One model treats the elevator's motion dynamically, where propulsive and friction forces are applied to the elevator body. The second model treats the elevator's motion kinematically, where the distance along the tether is determined by adjusting the lengths of tether on either side of the elevator. The tether model is used to determine optimal configurations for the space elevator. A modal analysis of two different configurations is presented which show that the fundamental mode of oscillation is a pendular one around the anchor point with a period on the order of 160 h for the in-plane motion, and 24 h for the out-of-plane motion. Numerical simulation results of the effects of the elevator moving along the cable are presented for different travel velocities and different elevator masses.
Sepsis progression and outcome: a dynamical model
Directory of Open Access Journals (Sweden)
Gessler Damian DG
2006-02-01
Full Text Available Abstract Background Sepsis (bloodstream infection is the leading cause of death in non-surgical intensive care units. It is diagnosed in 750,000 US patients per annum, and has high mortality. Current understanding of sepsis is predominately observational and correlational, with only a partial and incomplete understanding of the physiological dynamics underlying the syndrome. There exists a need for dynamical models of sepsis progression, based upon basic physiologic principles, which could eventually guide hourly treatment decisions. Results We present an initial mathematical model of sepsis, based on metabolic rate theory that links basic vascular and immunological dynamics. The model includes the rate of vascular circulation, a surrogate for the metabolic rate that is mechanistically associated with disease progression. We use the mass-specific rate of blood circulation (SRBC, a correlate of the body mass index, to build a differential equation model of circulation, infection, organ damage, and recovery. This introduces a vascular component into an infectious disease model that describes the interaction between a pathogen and the adaptive immune system. Conclusion The model predicts that deviations from normal SRBC correlate with disease progression and adverse outcome. We compare the predictions with population mortality data from cardiovascular disease and cancer and show that deviations from normal SRBC correlate with higher mortality rates.
Mineral vein dynamics modeling (FRACS). Phase 1
Energy Technology Data Exchange (ETDEWEB)
Urai, J.; Virgo, S.; Arndt, M. [RWTH Aachen (Germany). Geologie-Endogene Dynamik] [and others
2013-07-15
The Mineral Vein Dynamics Modeling group ''FRACS'' is a team of 7 research groups from the Universities of Mainz, Aachen, Tuebingen, Karlsruhe, Bayreuth, ETH Zuerich and Glasgow working on an understanding of the dynamic development of fracturing, fluid flow and fracture sealing. World-class field laboratories, especially carbonate sequences from the Oman Mountains are studied and classified. State of the art numerical programs are written, expanded and used to simulate the dynamic interaction of fracturing, flow and resealing and the results are compared with the natural examples. Newest analytical technologies including laser scanning, high resolution X-ray microtomography, fluid inclusion and isotope analysis are performed to understand and compare the results of simulations with natural examples. A new statistical program was developed to classify the natural fracture and vein systems and compare them with dynamic numerical simulations and analytical models. The results of the first project phase are extremely promising. Most of the numerical models have been developed up to the stage where they can be used to simulate the natural examples. The models allow a definition of the first proxies for high fluid pressure and tectonic stresses. It was found out that the Oman Mountains are a complex and very dynamic system that constantly fractures and reseals from the scale of small veins up to the scale of large normal and strike slip faults. The numerical simulations also indicate that the permeability of such systems is not a constant but that the system adjusts to the driving force, for ex-ample high fluid pressure. When the system reseals fast a fluctuating behavior can be observed in the models where the system constantly fractures and reseals, which is in accordance with the observation of the natural laboratory.
Evaluation of Sustainability of Brazilian Ethanol Production: A model in System Dynamics
Directory of Open Access Journals (Sweden)
Arnoldo Jose de Hoyos Guevara
2017-01-01
Full Text Available System dynamics is an approach to analyze the behavior of complex systems, such as the productive chains, strictly considering the inherent characteristics. This approach is based on mathematical concepts of nonlinear processes developed in mathematics and physics and consolidated in engineering. The concepts inherent in this approach assists in creating a mathematical model that represents a production chain by using computer simulation. Thus, the main objective of this paper is to present the formalization of the dynamic model of assessing the sustainability of Brazilian ethanol production. We analyzed the external environment and the scenarios needed for a deeper understanding of relation of cause and effect, causal loops and diagrams of flows and stocks because of the awareness stage, with regard to understanding the problems involved, according to methodology known as design science.
Uncertainty, causality and decision: The case of social risks and nuclear risk in particular
International Nuclear Information System (INIS)
Lahidji, R.
2012-01-01
Probability and causality are two indispensable tools for addressing situations of social risk. Causal relations are the foundation for building risk assessment models and identifying risk prevention, mitigation and compensation measures. Probability enables us to quantify risk assessments and to calibrate intervention measures. It therefore seems not only natural, but also necessary to make the role of causality and probability explicit in the definition of decision problems in situations of social risk. Such is the aim of this thesis.By reviewing the terminology of risk and the logic of public interventions in various fields of social risk, we gain a better understanding of the notion and of the issues that one faces when trying to model it. We further elaborate our analysis in the case of nuclear safety, examining in detail how methods and policies have been developed in this field and how they have evolved through time. This leads to a number of observations concerning risk and safety assessments.Generalising the concept of intervention in a Bayesian network allows us to develop a variety of causal Bayesian networks adapted to our needs. In this framework, we propose a definition of risk which seems to be relevant for a broad range of issues. We then offer simple applications of our model to specific aspects of the Fukushima accident and other nuclear safety problems. In addition to specific lessons, the analysis leads to the conclusion that a systematic approach for identifying uncertainties is needed in this area. When applied to decision theory, our tool evolves into a dynamic decision model in which acts cause consequences and are causally interconnected. The model provides a causal interpretation of Savage's conceptual framework, solves some of its paradoxes and clarifies certain aspects. It leads us to considering uncertainty with regard to a problem's causal structure as the source of ambiguity in decision-making, an interpretation which corresponds to a
Direct modeling for computational fluid dynamics
Xu, Kun
2015-06-01
All fluid dynamic equations are valid under their modeling scales, such as the particle mean free path and mean collision time scale of the Boltzmann equation and the hydrodynamic scale of the Navier-Stokes (NS) equations. The current computational fluid dynamics (CFD) focuses on the numerical solution of partial differential equations (PDEs), and its aim is to get the accurate solution of these governing equations. Under such a CFD practice, it is hard to develop a unified scheme that covers flow physics from kinetic to hydrodynamic scales continuously because there is no such governing equation which could make a smooth transition from the Boltzmann to the NS modeling. The study of fluid dynamics needs to go beyond the traditional numerical partial differential equations. The emerging engineering applications, such as air-vehicle design for near-space flight and flow and heat transfer in micro-devices, do require further expansion of the concept of gas dynamics to a larger domain of physical reality, rather than the traditional distinguishable governing equations. At the current stage, the non-equilibrium flow physics has not yet been well explored or clearly understood due to the lack of appropriate tools. Unfortunately, under the current numerical PDE approach, it is hard to develop such a meaningful tool due to the absence of valid PDEs. In order to construct multiscale and multiphysics simulation methods similar to the modeling process of constructing the Boltzmann or the NS governing equations, the development of a numerical algorithm should be based on the first principle of physical modeling. In this paper, instead of following the traditional numerical PDE path, we introduce direct modeling as a principle for CFD algorithm development. Since all computations are conducted in a discretized space with limited cell resolution, the flow physics to be modeled has to be done in the mesh size and time step scales. Here, the CFD is more or less a direct
New concepts for dynamic plant uptake models
DEFF Research Database (Denmark)
Rein, Arno; Legind, Charlotte Nielsen; Trapp, Stefan
2011-01-01
Models for the prediction of chemical uptake into plants are widely applied tools for human and wildlife exposure assessment, pesticide design and for environmental biotechnology such as phytoremediation. Steady-state considerations are often applied, because they are simple and have a small data...... need. However, often the emission pattern is non-steady. Examples are pesticide spraying, or the application of manure and sewage sludge on agricultural fields. In these scenarios, steady-state solutions are not valid, and dynamic simulation is required. We compared different approaches for dynamic...
Statistical models of petrol engines vehicles dynamics
Ilie, C. O.; Marinescu, M.; Alexa, O.; Vilău, R.; Grosu, D.
2017-10-01
This paper focuses on studying statistical models of vehicles dynamics. It was design and perform a one year testing program. There were used many same type cars with gasoline engines and different mileage. Experimental data were collected of onboard sensors and those on the engine test stand. A database containing data of 64th tests was created. Several mathematical modelling were developed using database and the system identification method. Each modelling is a SISO or a MISO linear predictive ARMAX (AutoRegressive-Moving-Average with eXogenous inputs) model. It represents a differential equation with constant coefficients. It were made 64th equations for each dependency like engine torque as output and engine’s load and intake manifold pressure, as inputs. There were obtained strings with 64 values for each type of model. The final models were obtained using average values of the coefficients. The accuracy of models was assessed.