Uncertainty and sensitivity analysis of flood risk management decisions based on stationary and nonstationary model choices

Directory of Open Access Journals (Sweden)

Rehan Balqis M.

2016-01-01

Full Text Available Current practice in flood frequency analysis assumes that the stochastic properties of extreme floods follow that of stationary conditions. As human intervention and anthropogenic climate change influences in hydrometeorological variables are becoming evident in some places, there have been suggestions that nonstationary statistics would be better to represent the stochastic properties of the extreme floods. The probabilistic estimation of non-stationary models, however, is surrounded with uncertainty related to scarcity of observations and modelling complexities hence the difficulty to project the future condition. In the face of uncertain future and the subjectivity of model choices, this study attempts to demonstrate the practical implications of applying a nonstationary model and compares it with a stationary model in flood risk assessment. A fully integrated framework to simulate decision makers’ behaviour in flood frequency analysis is thereby developed. The framework is applied to hypothetical flood risk management decisions and the outcomes are compared with those of known underlying future conditions. Uncertainty of the economic performance of the risk-based decisions is assessed through Monte Carlo simulations. Sensitivity of the results is also tested by varying the possible magnitude of future changes. The application provides quantitative and qualitative comparative results that satisfy a preliminary analysis of whether the nonstationary model complexity should be applied to improve the economic performance of decisions. Results obtained from the case study shows that the relative differences of competing models for all considered possible future changes are small, suggesting that stationary assumptions are preferred to a shift to nonstationary statistics for practical application of flood risk management. Nevertheless, nonstationary assumption should also be considered during a planning stage in addition to stationary assumption

How decision analysis can further nanoinformatics.

Science.gov (United States)

Bates, Matthew E; Larkin, Sabrina; Keisler, Jeffrey M; Linkov, Igor

2015-01-01

The increase in nanomaterial research has resulted in increased nanomaterial data. The next challenge is to meaningfully integrate and interpret these data for better and more efficient decisions. Due to the complex nature of nanomaterials, rapid changes in technology, and disunified testing and data publishing strategies, information regarding material properties is often illusive, uncertain, and/or of varying quality, which limits the ability of researchers and regulatory agencies to process and use the data. The vision of nanoinformatics is to address this problem by identifying the information necessary to support specific decisions (a top-down approach) and collecting and visualizing these relevant data (a bottom-up approach). Current nanoinformatics efforts, however, have yet to efficiently focus data acquisition efforts on the research most relevant for bridging specific nanomaterial data gaps. Collecting unnecessary data and visualizing irrelevant information are expensive activities that overwhelm decision makers. We propose that the decision analytic techniques of multicriteria decision analysis (MCDA), value of information (VOI), weight of evidence (WOE), and portfolio decision analysis (PDA) can bridge the gap from current data collection and visualization efforts to present information relevant to specific decision needs. Decision analytic and Bayesian models could be a natural extension of mechanistic and statistical models for nanoinformatics practitioners to master in solving complex nanotechnology challenges.

A model of pathways to artificial superintelligence catastrophe for risk and decision analysis

Science.gov (United States)

Barrett, Anthony M.; Baum, Seth D.

2017-03-01

An artificial superintelligence (ASI) is an artificial intelligence that is significantly more intelligent than humans in all respects. Whilst ASI does not currently exist, some scholars propose that it could be created sometime in the future, and furthermore that its creation could cause a severe global catastrophe, possibly even resulting in human extinction. Given the high stakes, it is important to analyze ASI risk and factor the risk into decisions related to ASI research and development. This paper presents a graphical model of major pathways to ASI catastrophe, focusing on ASI created via recursive self-improvement. The model uses the established risk and decision analysis modelling paradigms of fault trees and influence diagrams in order to depict combinations of events and conditions that could lead to AI catastrophe, as well as intervention options that could decrease risks. The events and conditions include select aspects of the ASI itself as well as the human process of ASI research, development and management. Model structure is derived from published literature on ASI risk. The model offers a foundation for rigorous quantitative evaluation and decision-making on the long-term risk of ASI catastrophe.

Applying Recursive Sensitivity Analysis to Multi-Criteria Decision Models to Reduce Bias in Defense Cyber Engineering Analysis

Science.gov (United States)

2015-10-28

techniques such as regression analysis, correlation, and multicollinearity assessment to identify the change and error on the input to the model...between many of the independent or predictor variables, the issue of multicollinearity may arise [18]. VII. SUMMARY Accurate decisions concerning

Decision analysis with cumulative prospect theory.

Science.gov (United States)

Bayoumi, A M; Redelmeier, D A

2000-01-01

Individuals sometimes express preferences that do not follow expected utility theory. Cumulative prospect theory adjusts for some phenomena by using decision weights rather than probabilities when analyzing a decision tree. The authors examined how probability transformations from cumulative prospect theory might alter a decision analysis of a prophylactic therapy in AIDS, eliciting utilities from patients with HIV infection (n = 75) and calculating expected outcomes using an established Markov model. They next focused on transformations of three sets of probabilities: 1) the probabilities used in calculating standard-gamble utility scores; 2) the probabilities of being in discrete Markov states; 3) the probabilities of transitioning between Markov states. The same prophylaxis strategy yielded the highest quality-adjusted survival under all transformations. For the average patient, prophylaxis appeared relatively less advantageous when standard-gamble utilities were transformed. Prophylaxis appeared relatively more advantageous when state probabilities were transformed and relatively less advantageous when transition probabilities were transformed. Transforming standard-gamble and transition probabilities simultaneously decreased the gain from prophylaxis by almost half. Sensitivity analysis indicated that even near-linear probability weighting transformations could substantially alter quality-adjusted survival estimates. The magnitude of benefit estimated in a decision-analytic model can change significantly after using cumulative prospect theory. Incorporating cumulative prospect theory into decision analysis can provide a form of sensitivity analysis and may help describe when people deviate from expected utility theory.

Extended warranties, maintenance service and lease contracts modeling and analysis for decision-making

CERN Document Server

Murthy, D N Prabhakar

2014-01-01

Serving to unify the existing literature on extended warranties, maintenance service contracts and lease contracts, this book also presents a unique perspective on the topic focussed on cost analysis and decision-making from the perspectives of the parties involved. Using a game theoretic approach together with mathematical modelling, results are presented in an integrated manner with key topics that require further research highlighted in order to serve as a starting point for researchers (engineers and statisticians) who are interested in doing further work in these areas. Designed to assist practitioners (managers, engineers, applied statisticians) who are involved with extended warranties, maintenance service contracts and lease contracts, the book provides them with the models and techniques needed for proper cost analysis and effective decision-making. The book is also suitable for use as a reference text in industrial engineering, applied statistics, operations research and management.

A stochastic multicriteria model for evidence-based decision making in drug benefit-risk analysis.

Science.gov (United States)

Tervonen, Tommi; van Valkenhoef, Gert; Buskens, Erik; Hillege, Hans L; Postmus, Douwe

2011-05-30

Drug benefit-risk (BR) analysis is based on firm clinical evidence regarding various safety and efficacy outcomes. In this paper, we propose a new and more formal approach for constructing a supporting multi-criteria model that fully takes into account the evidence on efficacy and adverse drug reactions. Our approach is based on the stochastic multi-criteria acceptability analysis methodology, which allows us to compute the typical value judgments that support a decision, to quantify decision uncertainty, and to compute a comprehensive BR profile. We construct a multi-criteria model for the therapeutic group of second-generation antidepressants. We assess fluoxetine and venlafaxine together with placebo according to incidence of treatment response and three common adverse drug reactions by using data from a published study. Our model shows that there are clear trade-offs among the treatment alternatives. Copyright © 2011 John Wiley & Sons, Ltd.

Introduction to Modeling of Buying Decisions

Directory of Open Access Journals (Sweden)

O. Gruenwald

2011-01-01

Full Text Available Buying decision models of customers to adjust the competitiveness of organizations have been a challenge for marketing disciplines for several generations. This topic has been explored by researchers and academics in past years, and quite an extensive theoretical base exists with a number of approaches for dealing with this challenge.This paper presents some approaches for creating a customer decision model, and provides experimental results from an electronic investigation intended to build the Kano Model; to prove an ability to understand the modeling principle; and to find out the interpretation of the examined demand in a specific market segment involving students of a technical university. The last section of the paper contains a brief introduction to Choice-Based Modeling with Choice-Based Conjoint Analysis (CBC, which was tailored for modeling purchasing decisions.

Environmental Decision Analysis: Meeting the Challenges of Making Good Decisions at CALFED

Directory of Open Access Journals (Sweden)

Claire D Tomkins

2006-09-01

Full Text Available We present a methodology to support decision making at CALFED based on the principles of decision analysis, an analytical approach to decision making designed to handle complex decisions involving both uncertainty and multiple dimensions of value. The impetus for such an approach is a recognized need to enhance communication between scientists and management and between program elements within CALFED. In addition, the environmental decision analysis framework supports both the explicit representation of uncertainty in the decision problem and communication about risk, important elements of most environmental management decisions. The decision analysis cycle consists of four phases: 1 formulate, 2 evaluate, 3 appraise, and 4 decide. In phase one, we identify the objectives and also the alternatives, or possible actions. To facilitate inter-comparison between proposed actions, we recommend formulation of a set of common metrics for CALFED. In our pilot study, we introduced common metrics for salinity, winter-run Chinook salmon survival, and habitat health. The second phase focuses on quantifying possible impacts on the set of metrics, drawing on existing data, model runs, and expert opinions. For the evaluation phase, we employ tools such as decision trees to assess the system-wide impacts of a given action. In the final phase, tools such as expected cost-benefit analysis, value contribution diagrams, and 3-D tradeoff plots aid communication between various stakeholders, scientists, and managers. While decision analysis provides a spectrum of decision support tools, we emphasize that it does not dictate a solution but rather enhances communication about tradeoffs associated with different actions.

​An Overview on the Principles of Decision Analysis and Economic Modeling in Healthcare and an Introduction to Advanced Software Treeage Pro 2011

Directory of Open Access Journals (Sweden)

Ali Imani

2015-08-01

Full Text Available Background and Objectives : Decision analysis models are conceptual framework for most of the cost - effectiveness (CEA and cost-utility (CUA analyses and this model increasingly plays an important role in decision making. The aim of this study was to improve the understanding and use of decision analysis and economic modeling techniques with a particular emphasis on decision trees and Markov modeling. Material and Methods : A review of the published literature was performed using the seven search engines and databases which include Web of Science, PubMed, Cochrane, Embase, EconLit, EBSCO and HEED with key words including: Decision Analysis, Health Economic Modeling and TreeAge and their combination to describe the structure, application, and limitations of the more popular decision analytic methods including decision trees, Markov models, and sensitivity analysis in healthcare. Results : We identified 19 relevant published articles. The results indicated that decision analytical models are widely used in economic evaluation of health care interventions with the objective of generating valuable information to assist health policy decision-makers to allocate scarce health care resources efficiently. Conclusion : Decision analytic modeling allows a rational, feasible, scientific, and timely approach to measure the efficiency of new medical technologies in health care by using the best available evidence of different sources to produce detailed estimates of the clinical and economic indicators. Despite TreeAge Pro software increasing use in developing countries as economic modeling studies of various health interventions, unfortunately its role and impact are not known in Iran yet. ​

Including model uncertainty in risk-informed decision making

International Nuclear Information System (INIS)

Reinert, Joshua M.; Apostolakis, George E.

2006-01-01

Model uncertainties can have a significant impact on decisions regarding licensing basis changes. We present a methodology to identify basic events in the risk assessment that have the potential to change the decision and are known to have significant model uncertainties. Because we work with basic event probabilities, this methodology is not appropriate for analyzing uncertainties that cause a structural change to the model, such as success criteria. We use the risk achievement worth (RAW) importance measure with respect to both the core damage frequency (CDF) and the change in core damage frequency (ΔCDF) to identify potentially important basic events. We cross-check these with generically important model uncertainties. Then, sensitivity analysis is performed on the basic event probabilities, which are used as a proxy for the model parameters, to determine how much error in these probabilities would need to be present in order to impact the decision. A previously submitted licensing basis change is used as a case study. Analysis using the SAPHIRE program identifies 20 basic events as important, four of which have model uncertainties that have been identified in the literature as generally important. The decision is fairly insensitive to uncertainties in these basic events. In three of these cases, one would need to show that model uncertainties would lead to basic event probabilities that would be between two and four orders of magnitude larger than modeled in the risk assessment before they would become important to the decision. More detailed analysis would be required to determine whether these higher probabilities are reasonable. Methods to perform this analysis from the literature are reviewed and an example is demonstrated using the case study

A regret theory approach to decision curve analysis: A novel method for eliciting decision makers' preferences and decision-making

OpenAIRE

Vickers Andrew; Hozo Iztok; Tsalatsanis Athanasios; Djulbegovic Benjamin

2010-01-01

Abstract Background Decision curve analysis (DCA) has been proposed as an alternative method for evaluation of diagnostic tests, prediction models, and molecular markers. However, DCA is based on expected utility theory, which has been routinely violated by decision makers. Decision-making is governed by intuition (system 1), and analytical, deliberative process (system 2), thus, rational decision-making should reflect both formal principles of rationality and intuition about good decisions. ...

Modeling Adversaries in Counterterrorism Decisions Using Prospect Theory.

Science.gov (United States)

Merrick, Jason R W; Leclerc, Philip

2016-04-01

Counterterrorism decisions have been an intense area of research in recent years. Both decision analysis and game theory have been used to model such decisions, and more recently approaches have been developed that combine the techniques of the two disciplines. However, each of these approaches assumes that the attacker is maximizing its utility. Experimental research shows that human beings do not make decisions by maximizing expected utility without aid, but instead deviate in specific ways such as loss aversion or likelihood insensitivity. In this article, we modify existing methods for counterterrorism decisions. We keep expected utility as the defender's paradigm to seek for the rational decision, but we use prospect theory to solve for the attacker's decision to descriptively model the attacker's loss aversion and likelihood insensitivity. We study the effects of this approach in a critical decision, whether to screen containers entering the United States for radioactive materials. We find that the defender's optimal decision is sensitive to the attacker's levels of loss aversion and likelihood insensitivity, meaning that understanding such descriptive decision effects is important in making such decisions. © 2014 Society for Risk Analysis.

Markov Modeling with Soft Aggregation for Safety and Decision Analysis; TOPICAL

International Nuclear Information System (INIS)

COOPER, J. ARLIN

1999-01-01

The methodology in this report improves on some of the limitations of many conventional safety assessment and decision analysis methods. A top-down mathematical approach is developed for decomposing systems and for expressing imprecise individual metrics as possibilistic or fuzzy numbers. A ''Markov-like'' model is developed that facilitates combining (aggregating) inputs into overall metrics and decision aids, also portraying the inherent uncertainty. A major goal of Markov modeling is to help convey the top-down system perspective. One of the constituent methodologies allows metrics to be weighted according to significance of the attribute and aggregated nonlinearly as to contribution. This aggregation is performed using exponential combination of the metrics, since the accumulating effect of such factors responds less and less to additional factors. This is termed ''soft'' mathematical aggregation. Dependence among the contributing factors is accounted for by incorporating subjective metrics on ''overlap'' of the factors as well as by correspondingly reducing the overall contribution of these combinations to the overall aggregation. Decisions corresponding to the meaningfulness of the results are facilitated in several ways. First, the results are compared to a soft threshold provided by a sigmoid function. Second, information is provided on input ''Importance'' and ''Sensitivity,'' in order to know where to place emphasis on considering new controls that may be necessary. Third, trends in inputs and outputs are tracked in order to obtain significant information% including cyclic information for the decision process. A practical example from the air transportation industry is used to demonstrate application of the methodology. Illustrations are given for developing a structure (along with recommended inputs and weights) for air transportation oversight at three different levels, for developing and using cycle information, for developing Importance and

Harnessing Ecosystem Models and Multi-Criteria Decision Analysis for the Support of Forest Management

Science.gov (United States)

Wolfslehner, Bernhard; Seidl, Rupert

2010-12-01

The decision-making environment in forest management (FM) has changed drastically during the last decades. Forest management planning is facing increasing complexity due to a widening portfolio of forest goods and services, a societal demand for a rational, transparent decision process and rising uncertainties concerning future environmental conditions (e.g., climate change). Methodological responses to these challenges include an intensified use of ecosystem models to provide an enriched, quantitative information base for FM planning. Furthermore, multi-criteria methods are increasingly used to amalgamate information, preferences, expert judgments and value expressions, in support of the participatory and communicative dimensions of modern forestry. Although the potential of combining these two approaches has been demonstrated in a number of studies, methodological aspects in interfacing forest ecosystem models (FEM) and multi-criteria decision analysis (MCDA) are scarcely addressed explicitly. In this contribution we review the state of the art in FEM and MCDA in the context of FM planning and highlight some of the crucial issues when combining ecosystem and preference modeling. We discuss issues and requirements in selecting approaches suitable for supporting FM planning problems from the growing body of FEM and MCDA concepts. We furthermore identify two major challenges in a harmonized application of FEM-MCDA: (i) the design and implementation of an indicator-based analysis framework capturing ecological and social aspects and their interactions relevant for the decision process, and (ii) holistic information management that supports consistent use of different information sources, provides meta-information as well as information on uncertainties throughout the planning process.

Modeling as a Decision-Making Process

Science.gov (United States)

Bleiler-Baxter, Sarah K.; Stephens, D. Christopher; Baxter, Wesley A.; Barlow, Angela T.

2017-01-01

The goal in this article is to support teachers in better understanding what it means to model with mathematics by focusing on three key decision-making processes: Simplification, Relationship Mapping, and Situation Analysis. The authors use the Theme Park task to help teachers develop a vision of how students engage in these three decision-making…

Cloud Geospatial Analysis Tools for Global-Scale Comparisons of Population Models for Decision Making

Science.gov (United States)

Hancher, M.; Lieber, A.; Scott, L.

2017-12-01

The volume of satellite and other Earth data is growing rapidly. Combined with information about where people are, these data can inform decisions in a range of areas including food and water security, disease and disaster risk management, biodiversity, and climate adaptation. Google's platform for planetary-scale geospatial data analysis, Earth Engine, grants access to petabytes of continually updating Earth data, programming interfaces for analyzing the data without the need to download and manage it, and mechanisms for sharing the analyses and publishing results for data-driven decision making. In addition to data about the planet, data about the human planet - population, settlement and urban models - are now available for global scale analysis. The Earth Engine APIs enable these data to be joined, combined or visualized with economic or environmental indicators such as nighttime lights trends, global surface water, or climate projections, in the browser without the need to download anything. We will present our newly developed application intended to serve as a resource for government agencies, disaster response and public health programs, or other consumers of these data to quickly visualize the different population models, and compare them to ground truth tabular data to determine which model suits their immediate needs. Users can further tap into the power of Earth Engine and other Google technologies to perform a range of analysis from simple statistics in custom regions to more complex machine learning models. We will highlight case studies in which organizations around the world have used Earth Engine to combine population data with multiple other sources of data, such as water resources and roads data, over deep stacks of temporal imagery to model disease risk and accessibility to inform decisions.

Models for Rational Decision Making. Analysis of Literature and Selected Bibliography. Analysis and Bibliography Series, No. 6.

Science.gov (United States)

Hall, John S.

This review analyzes the trend in educational decision making to replace hierarchical authority structures with more rational models for decision making drawn from management science. Emphasis is also placed on alternatives to a hierarchical decision-making model, including governing models, union models, and influence models. A 54-item…

Decision analysis for dynamic accounting of nuclear material

International Nuclear Information System (INIS)

Shipley, J.P.

1978-01-01

Effective materials accounting for special nuclear material in modern fuel cycle facilities will depend heavily on sophisticated data analysis techniques. Decision analysis, which combines elements of estimation theory, decision theory, and systems analysis, is a framework well suited to the development and application of these techniques. Augmented by pattern-recognition tools such as the alarm-sequence chart, decision analysis can be used to reduce errors caused by subjective data evaluation and to condense large collections of data to a smaller set of more descriptive statistics. Application to data from a model plutonium nitrate-to-oxide conversion process illustrates the concepts

Rule-based decision making model

International Nuclear Information System (INIS)

Sirola, Miki

1998-01-01

A rule-based decision making model is designed in G2 environment. A theoretical and methodological frame for the model is composed and motivated. The rule-based decision making model is based on object-oriented modelling, knowledge engineering and decision theory. The idea of safety objective tree is utilized. Advanced rule-based methodologies are applied. A general decision making model 'decision element' is constructed. The strategy planning of the decision element is based on e.g. value theory and utility theory. A hypothetical process model is built to give input data for the decision element. The basic principle of the object model in decision making is division in tasks. Probability models are used in characterizing component availabilities. Bayes' theorem is used to recalculate the probability figures when new information is got. The model includes simple learning features to save the solution path. A decision analytic interpretation is given to the decision making process. (author)

Energy systems analysis modeling as a decision tool; Systemanalytisk energimodell som beslutsverktyg. Borlaenge och andra kommuner i Dalarna

Energy Technology Data Exchange (ETDEWEB)

Byman, Karin

1999-10-01

The hypothesis is that `a system optimisation model can be a powerful tool to produce basic data for strategic decision making in a local energy company`. Another question is: do the companies trust the results? The model used is MODEST, which is a model for energy-system optimisation, built on linear programming. The local energy system in the municipality of Borlaenge, has been analysed by means of MODEST and at the same time a traditional study has been carried out by an experienced energy-consultant. Both investigations were made under exactly the same conditions but totally independent of each other. Both studies came to same conclusions, and this was very important for the continued work. A more extensive investigation was made when five communities, including Borlaenge, joined to analyse the optimal energy supply in the region of Dalarna. The other communities are Hedemora, Saeter, Avesta and Falun. By following the decision-making process in Borlaenge and the other communities it has been possible to judge the usefulness of the MODEST model and the concept of energy system analyses that goes with it. The energy companies were interviewed about their experiences of the model. They all agree that they have confidence in the model, as the results correspond with their own calculations and knowledge of their energy systems. The process is easy to follow and the inputs to the model are data that always have to be processed in an investigation of a new investment or other changes of the energy supply. The result of the analysis is easy to comprehend. Regarding Borlaenge a decisive strategic decision has not yet been made. That depends on external insecurities on the energy market which are outside the modelling process. Not making a decision is also a strategic act. Based on the results of the analysis that have been made during the period of this work, 1997-1999, Borlaenge Energy has decided to postpone the final decision for two or three years. The president of

[Mathematical models of decision making and learning].

Science.gov (United States)

Ito, Makoto; Doya, Kenji

2008-07-01

Computational models of reinforcement learning have recently been applied to analysis of brain imaging and neural recording data to identity neural correlates of specific processes of decision making, such as valuation of action candidates and parameters of value learning. However, for such model-based analysis paradigms, selecting an appropriate model is crucial. In this study we analyze the process of choice learning in rats using stochastic rewards. We show that "Q-learning," which is a standard reinforcement learning algorithm, does not adequately reflect the features of choice behaviors. Thus, we propose a generalized reinforcement learning (GRL) algorithm that incorporates the negative reward effect of reward loss and forgetting of values of actions not chosen. Using the Bayesian estimation method for time-varying parameters, we demonstrated that the GRL algorithm can predict an animal's choice behaviors as efficiently as the best Markov model. The results suggest the usefulness of the GRL for the model-based analysis of neural processes involved in decision making.

Emergent collective decision-making: Control, model and behavior

Science.gov (United States)

Shen, Tian

In this dissertation we study emergent collective decision-making in social groups with time-varying interactions and heterogeneously informed individuals. First we analyze a nonlinear dynamical systems model motivated by animal collective motion with heterogeneously informed subpopulations, to examine the role of uninformed individuals. We find through formal analysis that adding uninformed individuals in a group increases the likelihood of a collective decision. Secondly, we propose a model for human shared decision-making with continuous-time feedback and where individuals have little information about the true preferences of other group members. We study model equilibria using bifurcation analysis to understand how the model predicts decisions based on the critical threshold parameters that represent an individual's tradeoff between social and environmental influences. Thirdly, we analyze continuous-time data of pairs of human subjects performing an experimental shared tracking task using our second proposed model in order to understand transient behavior and the decision-making process. We fit the model to data and show that it reproduces a wide range of human behaviors surprisingly well, suggesting that the model may have captured the mechanisms of observed behaviors. Finally, we study human behavior from a game-theoretic perspective by modeling the aforementioned tracking task as a repeated game with incomplete information. We show that the majority of the players are able to converge to playing Nash equilibrium strategies. We then suggest with simulations that the mean field evolution of strategies in the population resemble replicator dynamics, indicating that the individual strategies may be myopic. Decisions form the basis of control and problems involving deciding collectively between alternatives are ubiquitous in nature and in engineering. Understanding how multi-agent systems make decisions among alternatives also provides insight for designing

Model for nuclear proliferation resistance analysis using decision making tools

International Nuclear Information System (INIS)

Ko, Won Il; Kim, Ho Dong; Yang, Myung Seung

2003-06-01

The nuclear proliferation risks of nuclear fuel cycles is being considered as one of the most important factors in assessing advanced and innovative nuclear systems in GEN IV and INPRO program. They have been trying to find out an appropriate and reasonable method to evaluate quantitatively several nuclear energy system alternatives. Any reasonable methodology for integrated analysis of the proliferation resistance, however, has not yet been come out at this time. In this study, several decision making methods, which have been used in the situation of multiple objectives, are described in order to see if those can be appropriately used for proliferation resistance evaluation. Especially, the AHP model for quantitatively evaluating proliferation resistance is dealt with in more detail. The theoretical principle of the method and some examples for the proliferation resistance problem are described. For more efficient applications, a simple computer program for the AHP model is developed, and the usage of the program is introduced here in detail. We hope that the program developed in this study could be useful for quantitative analysis of the proliferation resistance involving multiple conflict criteria

Model for nuclear proliferation resistance analysis using decision making tools

Energy Technology Data Exchange (ETDEWEB)

Ko, Won Il; Kim, Ho Dong; Yang, Myung Seung

2003-06-01

The nuclear proliferation risks of nuclear fuel cycles is being considered as one of the most important factors in assessing advanced and innovative nuclear systems in GEN IV and INPRO program. They have been trying to find out an appropriate and reasonable method to evaluate quantitatively several nuclear energy system alternatives. Any reasonable methodology for integrated analysis of the proliferation resistance, however, has not yet been come out at this time. In this study, several decision making methods, which have been used in the situation of multiple objectives, are described in order to see if those can be appropriately used for proliferation resistance evaluation. Especially, the AHP model for quantitatively evaluating proliferation resistance is dealt with in more detail. The theoretical principle of the method and some examples for the proliferation resistance problem are described. For more efficient applications, a simple computer program for the AHP model is developed, and the usage of the program is introduced here in detail. We hope that the program developed in this study could be useful for quantitative analysis of the proliferation resistance involving multiple conflict criteria.

Decision forests for computer vision and medical image analysis

CERN Document Server

Criminisi, A

2013-01-01

This practical and easy-to-follow text explores the theoretical underpinnings of decision forests, organizing the vast existing literature on the field within a new, general-purpose forest model. Topics and features: with a foreword by Prof. Y. Amit and Prof. D. Geman, recounting their participation in the development of decision forests; introduces a flexible decision forest model, capable of addressing a large and diverse set of image and video analysis tasks; investigates both the theoretical foundations and the practical implementation of decision forests; discusses the use of decision for

Decision analysis and risk models for land development affecting infrastructure systems.

Science.gov (United States)

Thekdi, Shital A; Lambert, James H

2012-07-01

Coordination and layering of models to identify risks in complex systems such as large-scale infrastructure of energy, water, and transportation is of current interest across application domains. Such infrastructures are increasingly vulnerable to adjacent commercial and residential land development. Land development can compromise the performance of essential infrastructure systems and increase the costs of maintaining or increasing performance. A risk-informed approach to this topic would be useful to avoid surprise, regret, and the need for costly remedies. This article develops a layering and coordination of models for risk management of land development affecting infrastructure systems. The layers are: system identification, expert elicitation, predictive modeling, comparison of investment alternatives, and implications of current decisions for future options. The modeling layers share a focus on observable factors that most contribute to volatility of land development and land use. The relevant data and expert evidence include current and forecasted growth in population and employment, conservation and preservation rules, land topography and geometries, real estate assessments, market and economic conditions, and other factors. The approach integrates to a decision framework of strategic considerations based on assessing risk, cost, and opportunity in order to prioritize needs and potential remedies that mitigate impacts of land development to the infrastructure systems. The approach is demonstrated for a 5,700-mile multimodal transportation system adjacent to 60,000 tracts of potential land development. © 2011 Society for Risk Analysis.

A framework for sensitivity analysis of decision trees.

Science.gov (United States)

Kamiński, Bogumił; Jakubczyk, Michał; Szufel, Przemysław

2018-01-01

In the paper, we consider sequential decision problems with uncertainty, represented as decision trees. Sensitivity analysis is always a crucial element of decision making and in decision trees it often focuses on probabilities. In the stochastic model considered, the user often has only limited information about the true values of probabilities. We develop a framework for performing sensitivity analysis of optimal strategies accounting for this distributional uncertainty. We design this robust optimization approach in an intuitive and not overly technical way, to make it simple to apply in daily managerial practice. The proposed framework allows for (1) analysis of the stability of the expected-value-maximizing strategy and (2) identification of strategies which are robust with respect to pessimistic/optimistic/mode-favoring perturbations of probabilities. We verify the properties of our approach in two cases: (a) probabilities in a tree are the primitives of the model and can be modified independently; (b) probabilities in a tree reflect some underlying, structural probabilities, and are interrelated. We provide a free software tool implementing the methods described.

Selection of Representative Models for Decision Analysis Under Uncertainty

Science.gov (United States)

Meira, Luis A. A.; Coelho, Guilherme P.; Santos, Antonio Alberto S.; Schiozer, Denis J.

2016-03-01

The decision-making process in oil fields includes a step of risk analysis associated with the uncertainties present in the variables of the problem. Such uncertainties lead to hundreds, even thousands, of possible scenarios that are supposed to be analyzed so an effective production strategy can be selected. Given this high number of scenarios, a technique to reduce this set to a smaller, feasible subset of representative scenarios is imperative. The selected scenarios must be representative of the original set and also free of optimistic and pessimistic bias. This paper is devoted to propose an assisted methodology to identify representative models in oil fields. To do so, first a mathematical function was developed to model the representativeness of a subset of models with respect to the full set that characterizes the problem. Then, an optimization tool was implemented to identify the representative models of any problem, considering not only the cross-plots of the main output variables, but also the risk curves and the probability distribution of the attribute-levels of the problem. The proposed technique was applied to two benchmark cases and the results, evaluated by experts in the field, indicate that the obtained solutions are richer than those identified by previously adopted manual approaches. The program bytecode is available under request.

A Costing Analysis for Decision Making Grid Model in Failure-Based Maintenance

OpenAIRE

M. A., Burhanuddin; Halawani, Sami M.; Ahmad, A. R.

2011-01-01

Background. In current economic downturn, industries have to set good control on production cost, to maintain their profit margin. Maintenance department as an imperative unit in industries should attain all maintenance data, process information instantaneously, and subsequently transform it into a useful decision. Then act on the alternative to reduce production cost. Decision Making Grid model is used to identify strategies for maintenance decision. However, the model has limitation as it c...

A new approach to hazardous materials transportation risk analysis: decision modeling to identify critical variables.

Science.gov (United States)

Clark, Renee M; Besterfield-Sacre, Mary E

2009-03-01

We take a novel approach to analyzing hazardous materials transportation risk in this research. Previous studies analyzed this risk from an operations research (OR) or quantitative risk assessment (QRA) perspective by minimizing or calculating risk along a transport route. Further, even though the majority of incidents occur when containers are unloaded, the research has not focused on transportation-related activities, including container loading and unloading. In this work, we developed a decision model of a hazardous materials release during unloading using actual data and an exploratory data modeling approach. Previous studies have had a theoretical perspective in terms of identifying and advancing the key variables related to this risk, and there has not been a focus on probability and statistics-based approaches for doing this. Our decision model empirically identifies the critical variables using an exploratory methodology for a large, highly categorical database involving latent class analysis (LCA), loglinear modeling, and Bayesian networking. Our model identified the most influential variables and countermeasures for two consequences of a hazmat incident, dollar loss and release quantity, and is one of the first models to do this. The most influential variables were found to be related to the failure of the container. In addition to analyzing hazmat risk, our methodology can be used to develop data-driven models for strategic decision making in other domains involving risk.

IMPORTANCE OF DIFFERENT MODELS IN DECISION MAKING, EXPLAINING THE STRATEGIC BEHAVIOR IN ORGANIZATIONS

Directory of Open Access Journals (Sweden)

Cristiano de Oliveira Maciel

2006-11-01

Full Text Available This study is about the different models of decision process analyzing the organizational strategy. The article presents the strategy according to a cognitive approach. The discussion about that approach has three models of decision process: rational actor model, organizational behavior, and political model. These models, respectively, present some improvement in the decision making results, search for a good decision facing the cognitive restrictions of the administrator, and lots of talks for making a decision. According to the emphasis of each model, the possibilities for analyzing the strategy are presented. The article also shows that it is necessary to take into account the three different ways of analysis. That statement is justified once the analysis as well as the decision making become more complex, mainly those which are more important for the organizations.

Propagating Water Quality Analysis Uncertainty Into Resource Management Decisions Through Probabilistic Modeling

Science.gov (United States)

Gronewold, A. D.; Wolpert, R. L.; Reckhow, K. H.

2007-12-01

Most probable number (MPN) and colony-forming-unit (CFU) are two estimates of fecal coliform bacteria concentration commonly used as measures of water quality in United States shellfish harvesting waters. The MPN is the maximum likelihood estimate (or MLE) of the true fecal coliform concentration based on counts of non-sterile tubes in serial dilution of a sample aliquot, indicating bacterial metabolic activity. The CFU is the MLE of the true fecal coliform concentration based on the number of bacteria colonies emerging on a growth plate after inoculation from a sample aliquot. Each estimating procedure has intrinsic variability and is subject to additional uncertainty arising from minor variations in experimental protocol. Several versions of each procedure (using different sized aliquots or different numbers of tubes, for example) are in common use, each with its own levels of probabilistic and experimental error and uncertainty. It has been observed empirically that the MPN procedure is more variable than the CFU procedure, and that MPN estimates are somewhat higher on average than CFU estimates, on split samples from the same water bodies. We construct a probabilistic model that provides a clear theoretical explanation for the observed variability in, and discrepancy between, MPN and CFU measurements. We then explore how this variability and uncertainty might propagate into shellfish harvesting area management decisions through a two-phased modeling strategy. First, we apply our probabilistic model in a simulation-based analysis of future water quality standard violation frequencies under alternative land use scenarios, such as those evaluated under guidelines of the total maximum daily load (TMDL) program. Second, we apply our model to water quality data from shellfish harvesting areas which at present are closed (either conditionally or permanently) to shellfishing, to determine if alternative laboratory analysis procedures might have led to different

The use of decision analytic techniques in energy policy decisions

International Nuclear Information System (INIS)

Haemaelaeinen, R.P.; Seppaelaeinen, T.O.

1986-08-01

The report reviews decision analytic techniques and their applications to energy policy decision making. Decision analysis consists in techniques for structuring the essential elements of a decision problem and mathematical methods for ranking the alternatives from a set of simple judgments. Because modeling subjective judgments is characteristic of decision analysis, the models can incorporate qualitative factors and values, which escape traditional energy modeling. Decision analysis has been applied to choices among energy supply alternatives, siting energy facilities, selecting nuclear waste repositories, selecting research and development projects, risk analysis and prioritizing alternative energy futures. Many applications are done in universities and research institutions, but during the 70's the use of decision analysis has spread both to the public and the private sector. The settings where decision analysis has been applied range from aiding a single decision maker to clarifying opposing points of view. Decision analytic methods have also been linked with energy models. The most valuable result of decision analysis is the clarification of the problem at hand. Political decisions cannot be made solely on the basis of models, but models can be used to gain insight of the decision situation. Models inevitably simplify reality, so they must be regarded only as aids to judgment. So far there has been only one decision analysis of energy policy issues in Finland with actual political decision makers as participants. The experiences of this project and numerous foreign applications do however suggest that the decision analytic approach is useful in energy policy questions. The report presents a number of Finnish energy policy decisions where decision analysis might prove useful. However, the applicability of the methods depends crucially on the actual circumstances at hand

Feasibility Risk Assessment of Transport Infrastructure Projects: The CBA-DK Decision Support Model

DEFF Research Database (Denmark)

Salling, Kim Bang; Banister, David

2010-01-01

informed decision support towards decision-makers and stakeholders in terms of accumulated descending graphs. The decision support method developed in this paper aims to provide assistance in the analysis and ultimately the choice of action, while accounting for the uncertainties surrounding any transport......This paper presents the final version of the CBA-DK decision support model for assessment of transport projects. The model makes use of conventional cost-benefit analysis resulting in aggregated single point estimates and quantitative risk analysis using Monte Carlo simulation resulting in interval...... result, and the determination of suitable probability distributions. Use is made of the reference class forecasting information, such as that developed in Optimism Bias for adjustments to investment decisions that relate to all modes of transport. The CBA-DK decision support model results in more...

Adaptation and Evaluation of a Multi-Criteria Decision Analysis Model for Lyme Disease Prevention.

Directory of Open Access Journals (Sweden)

Cécile Aenishaenslin

Full Text Available Designing preventive programs relevant to vector-borne diseases such as Lyme disease (LD can be complex given the need to include multiple issues and perspectives into prioritizing public health actions. A multi-criteria decision aid (MCDA model was previously used to rank interventions for LD prevention in Quebec, Canada, where the disease is emerging. The aim of the current study was to adapt and evaluate the decision model constructed in Quebec under a different epidemiological context, in Switzerland, where LD has been endemic for the last thirty years. The model adaptation was undertaken with a group of Swiss stakeholders using a participatory approach. The PROMETHEE method was used for multi-criteria analysis. Key elements and results of the MCDA model are described and contrasted with the Quebec model. All criteria and most interventions of the MCDA model developed for LD prevention in Quebec were directly transferable to the Swiss context. Four new decision criteria were added, and the list of proposed interventions was modified. Based on the overall group ranking, interventions targeting human populations were prioritized in the Swiss model, with the top ranked action being the implementation of a large communication campaign. The addition of criteria did not significantly alter the intervention rankings, but increased the capacity of the model to discriminate between highest and lowest ranked interventions. The current study suggests that beyond the specificity of the MCDA models developed for Quebec and Switzerland, their general structure captures the fundamental and common issues that characterize the complexity of vector-borne disease prevention. These results should encourage public health organizations to adapt, use and share MCDA models as an effective and functional approach to enable the integration of multiple perspectives and considerations in the prevention and control of complex public health issues such as Lyme disease or

Adaptation and Evaluation of a Multi-Criteria Decision Analysis Model for Lyme Disease Prevention.

Science.gov (United States)

Aenishaenslin, Cécile; Gern, Lise; Michel, Pascal; Ravel, André; Hongoh, Valérie; Waaub, Jean-Philippe; Milord, François; Bélanger, Denise

2015-01-01

Designing preventive programs relevant to vector-borne diseases such as Lyme disease (LD) can be complex given the need to include multiple issues and perspectives into prioritizing public health actions. A multi-criteria decision aid (MCDA) model was previously used to rank interventions for LD prevention in Quebec, Canada, where the disease is emerging. The aim of the current study was to adapt and evaluate the decision model constructed in Quebec under a different epidemiological context, in Switzerland, where LD has been endemic for the last thirty years. The model adaptation was undertaken with a group of Swiss stakeholders using a participatory approach. The PROMETHEE method was used for multi-criteria analysis. Key elements and results of the MCDA model are described and contrasted with the Quebec model. All criteria and most interventions of the MCDA model developed for LD prevention in Quebec were directly transferable to the Swiss context. Four new decision criteria were added, and the list of proposed interventions was modified. Based on the overall group ranking, interventions targeting human populations were prioritized in the Swiss model, with the top ranked action being the implementation of a large communication campaign. The addition of criteria did not significantly alter the intervention rankings, but increased the capacity of the model to discriminate between highest and lowest ranked interventions. The current study suggests that beyond the specificity of the MCDA models developed for Quebec and Switzerland, their general structure captures the fundamental and common issues that characterize the complexity of vector-borne disease prevention. These results should encourage public health organizations to adapt, use and share MCDA models as an effective and functional approach to enable the integration of multiple perspectives and considerations in the prevention and control of complex public health issues such as Lyme disease or other vector

The need for consumer behavior analysis in health care coverage decisions.

Science.gov (United States)

Thompson, A M; Rao, C P

1990-01-01

Demographic analysis has been the primary form of analysis connected with health care coverage decisions. This paper reviews past demographic research and shows the need to use behavioral analyses for health care coverage policy decisions. A behavioral model based research study is presented and a case is made for integrated study into why consumers make health care coverage decisions.

Hierarchical decision modeling essays in honor of Dundar F. Kocaoglu

CERN Document Server

2016-01-01

This volume, developed in honor of Dr. Dundar F. Kocaoglu, aims to demonstrate the applications of the Hierarchical Decision Model (HDM) in different sectors and its capacity in decision analysis. It is comprised of essays from noted scholars, academics and researchers of engineering and technology management around the world. This book is organized into four parts: Technology Assessment, Strategic Planning, National Technology Planning and Decision Making Tools. Dr. Dundar F. Kocaoglu is one of the pioneers of multiple decision models using hierarchies, and creator of the HDM in decision analysis. HDM is a mission-oriented method for evaluation and/or selection among alternatives. A wide range of alternatives can be considered, including but not limited to, different technologies, projects, markets, jobs, products, cities to live in, houses to buy, apartments to rent, and schools to attend. Dr. Kocaoglu’s approach has been adopted for decision problems in many industrial sectors, including electronics rese...

An extended data envelopment analysis for the decision-making

Directory of Open Access Journals (Sweden)

Xiao-Li Meng

2017-10-01

Full Text Available Abstract Based on the CCR model, we propose an extended data envelopment analysis to evaluate the efficiency of decision making units with historical input and output data. The contributions of the work are threefold. First, the input and output data of the evaluated decision making unit are variable over time, and time series method is used to analyze and predict the data. Second, there are many sample decision making units, which are divided into several ordered sample standards in terms of production strategy, and the constraint condition consists of one of the sample standards. Furthermore, the efficiency is illustrated by considering the efficiency relationship between the evaluated decision making unit and sample decision making units from constraint condition. Third, to reduce the computation complexity, we introduce an algorithm based on the binary search tree in the model to choose the sample standard that has similar behavior with the evaluated decision making unit. Finally, we provide two numerical examples to illustrate the proposed model.

Climate change decision-making: Model & parameter uncertainties explored

Energy Technology Data Exchange (ETDEWEB)

Dowlatabadi, H.; Kandlikar, M.; Linville, C.

1995-12-31

A critical aspect of climate change decision-making is uncertainties in current understanding of the socioeconomic, climatic and biogeochemical processes involved. Decision-making processes are much better informed if these uncertainties are characterized and their implications understood. Quantitative analysis of these uncertainties serve to inform decision makers about the likely outcome of policy initiatives, and help set priorities for research so that outcome ambiguities faced by the decision-makers are reduced. A family of integrated assessment models of climate change have been developed at Carnegie Mellon. These models are distinguished from other integrated assessment efforts in that they were designed from the outset to characterize and propagate parameter, model, value, and decision-rule uncertainties. The most recent of these models is ICAM 2.1. This model includes representation of the processes of demographics, economic activity, emissions, atmospheric chemistry, climate and sea level change and impacts from these changes and policies for emissions mitigation, and adaptation to change. The model has over 800 objects of which about one half are used to represent uncertainty. In this paper we show, that when considering parameter uncertainties, the relative contribution of climatic uncertainties are most important, followed by uncertainties in damage calculations, economic uncertainties and direct aerosol forcing uncertainties. When considering model structure uncertainties we find that the choice of policy is often dominated by model structure choice, rather than parameter uncertainties.

Quantification of a decision-making failure probability of the accident management using cognitive analysis model

Energy Technology Data Exchange (ETDEWEB)

Yoshida, Yoshitaka; Ohtani, Masanori [Institute of Nuclear Safety System, Inc., Mihama, Fukui (Japan); Fujita, Yushi [TECNOVA Corp., Tokyo (Japan)

2002-09-01

In the nuclear power plant, much knowledge is acquired through probabilistic safety assessment (PSA) of a severe accident, and accident management (AM) is prepared. It is necessary to evaluate the effectiveness of AM using the decision-making failure probability of an emergency organization, operation failure probability of operators, success criteria of AM and reliability of AM equipments in PSA. However, there has been no suitable qualification method for PSA so far to obtain the decision-making failure probability, because the decision-making failure of an emergency organization treats the knowledge based error. In this work, we developed a new method for quantification of the decision-making failure probability of an emergency organization using cognitive analysis model, which decided an AM strategy, in a nuclear power plant at the severe accident, and tried to apply it to a typical pressurized water reactor (PWR) plant. As a result: (1) It could quantify the decision-making failure probability adjusted to PSA for general analysts, who do not necessarily possess professional human factors knowledge, by choosing the suitable value of a basic failure probability and an error-factor. (2) The decision-making failure probabilities of six AMs were in the range of 0.23 to 0.41 using the screening evaluation method and in the range of 0.10 to 0.19 using the detailed evaluation method as the result of trial evaluation based on severe accident analysis of a typical PWR plant, and a result of sensitivity analysis of the conservative assumption, failure probability decreased about 50%. (3) The failure probability using the screening evaluation method exceeded that using detailed evaluation method by 99% of probability theoretically, and the failure probability of AM in this study exceeded 100%. From this result, it was shown that the decision-making failure probability was more conservative than the detailed evaluation method, and the screening evaluation method satisfied

Quantification of a decision-making failure probability of the accident management using cognitive analysis model

International Nuclear Information System (INIS)

Yoshida, Yoshitaka; Ohtani, Masanori; Fujita, Yushi

2002-01-01

In the nuclear power plant, much knowledge is acquired through probabilistic safety assessment (PSA) of a severe accident, and accident management (AM) is prepared. It is necessary to evaluate the effectiveness of AM using the decision-making failure probability of an emergency organization, operation failure probability of operators, success criteria of AM and reliability of AM equipments in PSA. However, there has been no suitable qualification method for PSA so far to obtain the decision-making failure probability, because the decision-making failure of an emergency organization treats the knowledge based error. In this work, we developed a new method for quantification of the decision-making failure probability of an emergency organization using cognitive analysis model, which decided an AM strategy, in a nuclear power plant at the severe accident, and tried to apply it to a typical pressurized water reactor (PWR) plant. As a result: (1) It could quantify the decision-making failure probability adjusted to PSA for general analysts, who do not necessarily possess professional human factors knowledge, by choosing the suitable value of a basic failure probability and an error-factor. (2) The decision-making failure probabilities of six AMs were in the range of 0.23 to 0.41 using the screening evaluation method and in the range of 0.10 to 0.19 using the detailed evaluation method as the result of trial evaluation based on severe accident analysis of a typical PWR plant, and a result of sensitivity analysis of the conservative assumption, failure probability decreased about 50%. (3) The failure probability using the screening evaluation method exceeded that using detailed evaluation method by 99% of probability theoretically, and the failure probability of AM in this study exceeded 100%. From this result, it was shown that the decision-making failure probability was more conservative than the detailed evaluation method, and the screening evaluation method satisfied

Differential impairments underlying decision making in anorexia nervosa and bulimia nervosa: a cognitive modeling analysis.

Science.gov (United States)

Chan, Trista Wai Sze; Ahn, Woo-Young; Bates, John E; Busemeyer, Jerome R; Guillaume, Sebastien; Redgrave, Graham W; Danner, Unna N; Courtet, Philippe

2014-03-01

This study examined the underlying processes of decision-making impairments in individuals with anorexia nervosa (AN) and bulimia nervosa (BN). We deconstructed their performance on the widely used decision task, the Iowa Gambling Task (IGT) into cognitive, motivational, and response processes using cognitive modeling analysis. We hypothesized that IGT performance would be characterized by impaired memory functions and heightened punishment sensitivity in AN, and by elevated sensitivity to reward as opposed to punishment in BN. We analyzed trial-by-trial data of IGT obtained from 224 individuals: 94 individuals with AN, 63 with BN, and 67 healthy comparison individuals (HC). The prospect valence learning model was used to assess cognitive, motivational, and response processes underlying IGT performance. Individuals with AN showed marginally impaired IGT performance compared to HC. Their performance was characterized by impairments in memory functions. Individuals with BN showed significantly impaired IGT performance compared to HC. They showed greater relative sensitivity to gains as opposed to losses than HC. Memory functions in AN were positively correlated with body mass index. This study identified differential impairments underlying IGT performance in AN and BN. Findings suggest that impaired decision making in AN might involve impaired memory functions. Impaired decision making in BN might involve altered reward and punishment sensitivity. Copyright © 2013 Wiley Periodicals, Inc.

Decision making model design for antivirus software selection using Factor Analysis and Analytical Hierarchy Process

OpenAIRE

Nurhayati Ai; Gautama Aditya; Naseer Muchammad

2018-01-01

Virus spread increase significantly through the internet in 2017. One of the protection method is using antivirus software. The wide variety of antivirus software in the market tends to creating confusion among consumer. Selecting the right antivirus according to their needs has become difficult. This is the reason we conduct our research. We formulate a decision making model for antivirus software consumer. The model is constructed by using factor analysis and AHP method. First we spread que...

Risk Decision Making Based on Decision-theoretic Rough Set: A Three-way View Decision Model

OpenAIRE

Huaxiong Li; Xianzhong Zhou

2011-01-01

Rough set theory has witnessed great success in data mining and knowledge discovery, which provides a good support for decision making on a certain data. However, a practical decision problem always shows diversity under the same circumstance according to different personality of the decision makers. A simplex decision model can not provide a full description on such diverse decisions. In this article, a review of Pawlak rough set models and probabilistic rough set models is presented, and a ...

Building a maintenance policy through a multi-criterion decision-making model

Science.gov (United States)

Faghihinia, Elahe; Mollaverdi, Naser

2012-08-01

A major competitive advantage of production and service systems is establishing a proper maintenance policy. Therefore, maintenance managers should make maintenance decisions that best fit their systems. Multi-criterion decision-making methods can take into account a number of aspects associated with the competitiveness factors of a system. This paper presents a multi-criterion decision-aided maintenance model with three criteria that have more influence on decision making: reliability, maintenance cost, and maintenance downtime. The Bayesian approach has been applied to confront maintenance failure data shortage. Therefore, the model seeks to make the best compromise between these three criteria and establish replacement intervals using Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE II), integrating the Bayesian approach with regard to the preference of the decision maker to the problem. Finally, using a numerical application, the model has been illustrated, and for a visual realization and an illustrative sensitivity analysis, PROMETHEE GAIA (the visual interactive module) has been used. Use of PROMETHEE II and PROMETHEE GAIA has been made with Decision Lab software. A sensitivity analysis has been made to verify the robustness of certain parameters of the model.

Best-estimate analysis and decision making under uncertainty

International Nuclear Information System (INIS)

Orechwa, Y.

2004-01-01

In many engineering analyses of system safety the traditional reliance on conservative evaluation model calculations is being replaced with so called best-estimate analysis. These best-estimate analyses differentiate themselves from the traditional conservative analyses through two ingredients, namely realistic models and an account of the residual uncertainty associated with the model calculations. Best-estimate analysis, in the context of this paper, refers to the numerical evaluation of system properties of interest in situations where direct confirmatory measurements are not feasible. A decision with regard to the safety of the system is then made based on the computed numerical values of the system properties of interest. These situations generally arise in the design of systems that require computed and generally nontrivial extrapolations from the available data. In the case of nuclear reactors, examples are criticality of spent fuel pools, neutronic parameters of new advanced designs where insufficient material is available for mockup critical experiments and, the large break loss of coolant accident (LOCA). In this paper the case of LOCA, is taken to discuss the best-estimate analysis and decision making. Central to decision making is information. Thus, of interest is the source, quantity and quality of the information obtained in a best-estimate analysis, and used to define the acceptance criteria and to formulate a decision rule. This in effect expands the problem from the calculation of a conservative margin to a predefined acceptance criterion, to the formulation of a consistent decision rule and the computation of a test statistic for application of the decision rule. The latter view is a necessary condition for developing risk informed decision rules, and, thus, the relation between design basis analysis criteria and probabilistic risk assessment criteria is key. The discussion is in the context of making a decision under uncertainty for a reactor

Modelling decision-making by pilots

Science.gov (United States)

Patrick, Nicholas J. M.

1993-01-01

Our scientific goal is to understand the process of human decision-making. Specifically, a model of human decision-making in piloting modern commercial aircraft which prescribes optimal behavior, and against which we can measure human sub-optimality is sought. This model should help us understand such diverse aspects of piloting as strategic decision-making, and the implicit decisions involved in attention allocation. Our engineering goal is to provide design specifications for (1) better computer-based decision-aids, and (2) better training programs for the human pilot (or human decision-maker, DM).

A novel sustainable decision making model for municipal solid waste management

International Nuclear Information System (INIS)

Hung, M.-L.; Ma Hwongwen; Yang, W.-F.

2007-01-01

This paper reviews several models developed to support decision making in municipal solid waste management (MSWM). The concepts underlying sustainable MSWM models can be divided into two categories: one incorporates social factors into decision making methods, and the other includes public participation in the decision-making process. The public is only apprised or takes part in discussion, and has little effect on decision making in most research efforts. Few studies have considered public participation in the decision-making process, and the methods have sought to strike a compromise between concerned criteria, not between stakeholders. However, the source of the conflict arises from the stakeholders' complex web of value. Such conflict affects the feasibility of implementing any decision. The purpose of this study is to develop a sustainable decision making model for MSWM to overcome these shortcomings. The proposed model combines multicriteria decision making (MCDM) and a consensus analysis model (CAM). The CAM is built up to aid in decision-making when MCDM methods are utilized and, subsequently, a novel sustainable decision making model for MSWM is developed. The main feature of CAM is the assessment of the degree of consensus between stakeholders for particular alternatives. A case study for food waste management in Taiwan is presented to demonstrate the practicality of this model

A Composite Modelling Approach to Decision Support by the Use of the CBA-DK Model

DEFF Research Database (Denmark)

Barfod, Michael Bruhn; Salling, Kim Bang; Leleur, Steen

2007-01-01

This paper presents a decision support system for assessment of transport infrastructure projects. The composite modelling approach, COSIMA, combines a cost-benefit analysis by use of the CBA-DK model with multi-criteria analysis applying the AHP and SMARTER techniques. The modelling uncertaintie...

Decision-Making Theories and Models: A Discussion of Rational and Psychological Decision-Making Theories and Models: The Search for a Cultural-Ethical Decision-Making Model

OpenAIRE

Oliveira, Arnaldo

2007-01-01

This paper examines rational and psychological decision-making models. Descriptive and normative methodologies such as attribution theory, schema theory, prospect theory, ambiguity model, game theory, and expected utility theory are discussed. The definition of culture is reviewed, and the relationship between culture and decision making is also highlighted as many organizations use a cultural-ethical decision-making model.

Using discriminant analysis for credit decision

Directory of Open Access Journals (Sweden)

Gheorghiţa DINCĂ

2015-12-01

Full Text Available This paper follows to highlight the link between the results obtained applying discriminant analysis and lending decision. For this purpose, we have carried out the research on a sample of 24 Romanian private companies, pertaining to 12 different economic sectors, from I and II categories of Bucharest Stock Exchange, for the period 2010-2012. Our study works with two popular bankruptcy risk’s prediction models, the Altman model and the Anghel model. We have double-checked and confirmed the results of our research by comparing the results from applying the two fore-mentioned models as well as by checking existing debt commitments of each analyzed company to credit institutions during the 2010-2012 period. The aim of this paper was the classification of studied companies into potential bankrupt and non-bankrupt, to assist credit institutions in their decision to grant credit, understanding the approval or rejection algorithm of loan applications and even help potential investors in these ompanies.

Strategic decision analysis applied to borehole seismology

International Nuclear Information System (INIS)

Menke, M.M.; Paulsson, B.N.P.

1994-01-01

Strategic Decision Analysis (SDA) is the evolving body of knowledge on how to achieve high quality in the decision that shapes an organization's future. SDA comprises philosophy, process concepts, methodology, and tools for making good decisions. It specifically incorporates many concepts and tools from economic evaluation and risk analysis. Chevron Petroleum Technology Company (CPTC) has applied SDA to evaluate and prioritize a number of its most important and most uncertain R and D projects, including borehole seismology. Before SDA, there were significant issues and concerns about the value to CPTC of continuing to work on borehole seismology. The SDA process created a cross-functional team of experts to structure and evaluate this project. A credible economic model was developed, discrete risks and continuous uncertainties were assessed, and an extensive sensitivity analysis was performed. The results, even applied to a very restricted drilling program for a few years, were good enough to demonstrate the value of continuing the project. This paper explains the SDA philosophy concepts, and process and demonstrates the methodology and tools using the borehole seismology project example. SDA is useful in the upstream industry not just in the R and D/technology decisions, but also in major exploration and production decisions. Since a major challenge for upstream companies today is to create and realize value, the SDA approach should have a very broad applicability

Risk-based systems analysis for emerging technologies: Applications of a technology risk assessment model to public decision making

International Nuclear Information System (INIS)

Quadrel, M.J.; Fowler, K.M.; Cameron, R.; Treat, R.J.; McCormack, W.D.; Cruse, J.

1995-01-01

The risk-based systems analysis model was designed to establish funding priorities among competing technologies for tank waste remediation. The model addresses a gap in the Department of Energy's (DOE's) ''toolkit'' for establishing funding priorities among emerging technologies by providing disciplined risk and cost assessments of candidate technologies within the context of a complete remediation system. The model is comprised of a risk and cost assessment and a decision interface. The former assesses the potential reductions in risk and cost offered by new technology relative to the baseline risk and cost of an entire system. The latter places this critical information in context of other values articulated by decision makers and stakeholders in the DOE system. The risk assessment portion of the model is demonstrated for two candidate technologies for tank waste retrieval (arm-based mechanical retrieval -- the ''long reach arm'') and subsurface barriers (close-coupled chemical barriers). Relative changes from the base case in cost and risk are presented for these two technologies to illustrate how the model works. The model and associated software build on previous work performed for DOE's Office of Technology Development and the former Underground Storage Tank Integrated Demonstration, and complement a decision making tool presented at Waste Management 1994 for integrating technical judgements and non-technical (stakeholder) values when making technology funding decisions

Dynamics of Metabolism and Decision Making During Alcohol Consumption: Modeling and Analysis.

Science.gov (United States)

Giraldo, Luis Felipe; Passino, Kevin M; Clapp, John D; Ruderman, Danielle

2017-11-01

Heavy alcohol consumption is considered an important public health issue in the United States as over 88 000 people die every year from alcohol-related causes. Research is being conducted to understand the etiology of alcohol consumption and to develop strategies to decrease high-risk consumption and its consequences, but there are still important gaps in determining the main factors that influence the consumption behaviors throughout the drinking event. There is a need for methodologies that allow us not only to identify such factors but also to have a comprehensive understanding of how they are connected and how they affect the dynamical evolution of a drinking event. In this paper, we use previous empirical findings from laboratory and field studies to build a mathematical model of the blood alcohol concentration dynamics in individuals that are in drinking events. We characterize these dynamics as the result of the interaction between a decision-making system and the metabolic process for alcohol. We provide a model of the metabolic process for arbitrary alcohol intake patterns and a characterization of the mechanisms that drive the decision-making process of a drinker during the drinking event. We use computational simulations and Lyapunov stability theory to analyze the effects of the parameters of the model on the blood alcohol concentration dynamics that are characterized. Also, we propose a methodology to inform the model using data collected in situ and to make estimations that provide additional information to the analysis. We show how this model allows us to analyze and predict previously observed behaviors, to design new approaches for the collection of data that improves the construction of the model, and help with the design of interventions.

Applying air pollution modelling within a multi-criteria decision analysis framework to evaluate UK air quality policies

Science.gov (United States)

Chalabi, Zaid; Milojevic, Ai; Doherty, Ruth M.; Stevenson, David S.; MacKenzie, Ian A.; Milner, James; Vieno, Massimo; Williams, Martin; Wilkinson, Paul

2017-10-01

A decision support system for evaluating UK air quality policies is presented. It combines the output from a chemistry transport model, a health impact model and other impact models within a multi-criteria decision analysis (MCDA) framework. As a proof-of-concept, the MCDA framework is used to evaluate and compare idealized emission reduction policies in four sectors (combustion in energy and transformation industries, non-industrial combustion plants, road transport and agriculture) and across six outcomes or criteria (mortality, health inequality, greenhouse gas emissions, biodiversity, crop yield and air quality legal compliance). To illustrate a realistic use of the MCDA framework, the relative importance of the criteria were elicited from a number of stakeholders acting as proxy policy makers. In the prototype decision problem, we show that reducing emissions from industrial combustion (followed very closely by road transport and agriculture) is more advantageous than equivalent reductions from the other sectors when all the criteria are taken into account. Extensions of the MCDA framework to support policy makers in practice are discussed.

Assessing the economic impact of paternal involvement: a comparison of the generalized linear model versus decision analysis trees.

Science.gov (United States)

Salihu, Hamisu M; Salemi, Jason L; Nash, Michelle C; Chandler, Kristen; Mbah, Alfred K; Alio, Amina P

2014-08-01

Lack of paternal involvement has been shown to be associated with adverse pregnancy outcomes, including infant morbidity and mortality, but the impact on health care costs is unknown. Various methodological approaches have been used in cost minimization and cost effectiveness analyses and it remains unclear how cost estimates vary according to the analytic strategy adopted. We illustrate a methodological comparison of decision analysis modeling and generalized linear modeling (GLM) techniques using a case study that assesses the cost-effectiveness of potential father involvement interventions. We conducted a 12-year retrospective cohort study using a statewide enhanced maternal-infant database that contains both clinical and nonclinical information. A missing name for the father on the infant's birth certificate was used as a proxy for lack of paternal involvement, the main exposure of this study. Using decision analysis modeling and GLM, we compared all infant inpatient hospitalization costs over the first year of life. Costs were calculated from hospital charges using department-level cost-to-charge ratios and were adjusted for inflation. In our cohort of 2,243,891 infants, 9.2% had a father uninvolved during pregnancy. Lack of paternal involvement was associated with higher rates of preterm birth, small-for-gestational age, and infant morbidity and mortality. Both analytic approaches estimate significantly higher per-infant costs for father uninvolved pregnancies (decision analysis model: $1,827, GLM: $1,139). This paper provides sufficient evidence that healthcare costs could be significantly reduced through enhanced father involvement during pregnancy, and buttresses the call for a national program to involve fathers in antenatal care.

Decision models in engineering and management

CERN Document Server

2015-01-01

Providing a comprehensive overview of various methods  and applications in decision engineering, this book presents chapters written by a range experts in the field. It presents conceptual aspects of decision support applications in various areas including finance, vendor selection, construction, process management, water management and energy, agribusiness , production scheduling and control, and waste management. In addition to this, a special focus is given to methods of multi-criteria decision analysis. Decision making in organizations is a recurrent theme and is essential for business continuity.  Managers from various fields including public, private, industrial, trading or service sectors are required to make decisions. Consequently managers need the support of these structured methods in order to engage in effective decision making. This book provides a valuable resource for graduate students, professors and researchers of decision analysis, multi-criteria decision analysis and group decision analys...

Insights from quantum cognitive models for organizational decision making

OpenAIRE

White, L.C.; Pothos, E. M.; Busemeyer, J. R.

2015-01-01

Organizational decision making is often explored with theories from the heuristics and biases research program, which have demonstrated great value as descriptions of how people in organizations make decisions. Nevertheless, rational analysis and classical probability theory are still seen by many as the best accounts of how decisions should be made and classical probability theory is the preferred framework for cognitive modelling for many researchers. The focus of this work is quantum proba...

Analysis on Dynamic Decision-Making Model of the Enterprise Technological Innovation Investment under Uncertain Environment

Directory of Open Access Journals (Sweden)

Yong Long

2012-01-01

Full Text Available Under the environment of fuzzy factors including the return of market, performance of product, and the demanding level of market, we use the method of dynamic programming and establish the model of investment decision, in technology innovation project of enterprise, based on the dynamic programming. Analysis of the influence caused by the changes of fuzzy uncertainty factors to technological innovation project investment of enterprise.

Logit Estimation of a Gravity Model of the College Enrollment Decision.

Science.gov (United States)

Leppel, Karen

1993-01-01

A study investigated the factors influencing students' decisions about attending a college to which they had been admitted. Logit analysis confirmed gravity model predictions that geographic distance and student ability would most influence the enrollment decision and found other variables, although affecting earlier stages of decision making, did…

Risk analysis as a decision tool

International Nuclear Information System (INIS)

Yadigaroglu, G.; Chakraborty, S.

1985-01-01

From 1983 - 1985 a lecture series entitled ''Risk-benefit analysis'' was held at the Swiss Federal Institute of Technology (ETH), Zurich, in cooperation with the Central Department for the Safety of Nuclear Installations of the Swiss Federal Agency of Energy Economy. In that setting the value of risk-oriented evaluation models as a decision tool in safety questions was discussed on a broad basis. Experts of international reputation from the Federal Republic of Germany, France, Canada, the United States and Switzerland have contributed to report in this joint volume on the uses of such models. Following an introductory synopsis on risk analysis and risk assessment the book deals with practical examples in the fields of medicine, nuclear power, chemistry, transport and civil engineering. Particular attention is paid to the dialogue between analysts and decision makers taking into account the economic-technical aspects and social values. The recent chemical disaster in the Indian city of Bhopal again signals the necessity of such analyses. All the lectures were recorded individually. (orig./HP) [de

Decision-based model development for nuclear material theft, smuggling, and illicit use

International Nuclear Information System (INIS)

Scott, B.

2002-01-01

Full text: Nuclear material is vulnerable to a range of theft, sabotage, smuggling and illicit use scenarios. These scenarios are dependent on the choices of individuals and organizations involved in these activities. These choices, in turn; are dependent on the perceived payoff vectors of the involved players. These payoff vectors can include monetary gain, ability to avoid detection, penalties for detection, difficulty of accomplishment, resource constraints, infrastructure support, etc. Threat scenarios can be developed from these individual choices, and the set of worst-case threat scenarios can be compiled into a threat definition. The implementation of physical protection controls is dependent on the developed threat scenarios. The analysis of the composition of the postulated threat can be based on the analysis of the postulated decisions of the individuals and organizations involved on theft, smuggling, and illicit use. This paper proposes a model to systematically analyze the significant decision points that an individual or organization addresses as result of its goals. The model's dependence on assumptions is discussed. Using these assumptions, a model is developed that assigns probabilities to a set of decisions performed by the individuals involved in theft/smuggling. The individual and organisation's decisions are based on the perceived cost/benefit of the decisions and the resource constraints. Methods for functionally obtaining decision probabilities from perceived cost/benefit are proposed. The treatment of high-consequence/low-probability events is discussed in terms of analysis of precursor events, and the use of sensitivity analysis is discussed. An example of a simplified model for nuclear material theft, smuggling, and illicit use is presented, and the results of this simplified model are evaluated. By attempting to model the potential distribution of nuclear material theft/smuggling events, this model increases the analytical tools available

Analysis of the decision-making process of nurse managers: a collective reflection.

Science.gov (United States)

Eduardo, Elizabete Araujo; Peres, Aida Maris; de Almeida, Maria de Lourdes; Roglio, Karina de Dea; Bernardino, Elizabeth

2015-01-01

to analyze the decision-making model adopted by nurses from the perspective of some decision-making process theories. qualitative approach, based on action research. Semi-structured questionnaires and seminars were conducted from April to June 2012 in order to understand the nature of decisions and the decision-making process of nine nurses in position of managers at a public hospital in Southern Brazil. Data were subjected to content analysis. data were classified in two categories: the current situation of decision-making, which showed a lack of systematization; the construction and collective decision-making, which emphasizes the need to develop a decision-making model. the decision-making model used by nurses is limited because it does not consider two important factors: the limits of human rationality, and the external and internal organizational environments that influence and determine right decisions.

Slower Perception Followed by Faster Lexical Decision in Longer Words: A Diffusion Model Analysis.

Science.gov (United States)

Oganian, Yulia; Froehlich, Eva; Schlickeiser, Ulrike; Hofmann, Markus J; Heekeren, Hauke R; Jacobs, Arthur M

2015-01-01

Effects of stimulus length on reaction times (RTs) in the lexical decision task are the topic of extensive research. While slower RTs are consistently found for longer pseudo-words, a finding coined the word length effect (WLE), some studies found no effects for words, and yet others reported faster RTs for longer words. Moreover, the WLE depends on the orthographic transparency of a language, with larger effects in more transparent orthographies. Here we investigate processes underlying the WLE in lexical decision in German-English bilinguals using a diffusion model (DM) analysis, which we compared to a linear regression approach. In the DM analysis, RT-accuracy distributions are characterized using parameters that reflect latent sub-processes, in particular evidence accumulation and decision-independent perceptual encoding, instead of typical parameters such as mean RT and accuracy. The regression approach showed a decrease in RTs with length for pseudo-words, but no length effect for words. However, DM analysis revealed that the null effect for words resulted from opposing effects of length on perceptual encoding and rate of evidence accumulation. Perceptual encoding times increased with length for words and pseudo-words, whereas the rate of evidence accumulation increased with length for real words but decreased for pseudo-words. A comparison between DM parameters in German and English suggested that orthographic transparency affects perceptual encoding, whereas effects of length on evidence accumulation are likely to reflect contextual information and the increase in available perceptual evidence with length. These opposing effects may account for the inconsistent findings on WLEs.

METHODOLOGY FOR ANALYSIS OF DECISION MAKING IN AIR NAVIGATION SYSTEM

Directory of Open Access Journals (Sweden)

Volodymyr Kharchenko

2011-03-01

Full Text Available Abstract. In the research of Air Navigation System as a complex socio-technical system the methodologyof analysis of human-operator's decision-making has been developed. The significance of individualpsychologicalfactors as well as the impact of socio-psychological factors on the professional activities of ahuman-operator during the flight situation development from normal to catastrophic were analyzed. On thebasis of the reflexive theory of bipolar choice the expected risks of decision-making by the Air NavigationSystem's operator influenced by external environment, previous experience and intentions were identified.The methods for analysis of decision-making by the human-operator of Air Navigation System usingstochastic networks have been developed.Keywords: Air Navigation System, bipolar choice, human operator, decision-making, expected risk, individualpsychologicalfactors, methodology of analysis, reflexive model, socio-psychological factors, stochastic network.

Lone ranger decision making versus consensus decision making: Descriptive analysis

OpenAIRE

Maite Sara Mashego

2015-01-01

Consensus decision making, concerns group members make decisions together with the requirement of reaching a consensus that is all members abiding by the decision outcome. Lone ranging worked for sometime in a autocratic environment. Researchers are now pointing to consensus decision-making in organizations bringing dividend to many organizations. This article used a descriptive analysis to compare the goodness of consensus decision making and making lone ranging decision management. This art...

Analysis of a decision model in the context of equilibrium pricing and order book pricing

Science.gov (United States)

Wagner, D. C.; Schmitt, T. A.; Schäfer, R.; Guhr, T.; Wolf, D. E.

2014-12-01

An agent-based model for financial markets has to incorporate two aspects: decision making and price formation. We introduce a simple decision model and consider its implications in two different pricing schemes. First, we study its parameter dependence within a supply-demand balance setting. We find realistic behavior in a wide parameter range. Second, we embed our decision model in an order book setting. Here, we observe interesting features which are not present in the equilibrium pricing scheme. In particular, we find a nontrivial behavior of the order book volumes which reminds of a trend switching phenomenon. Thus, the decision making model alone does not realistically represent the trading and the stylized facts. The order book mechanism is crucial.

New decision analytical models for management of intracranial aneurysms

NARCIS (Netherlands)

Koffijberg, H.

2008-01-01

This thesis addresses decision analysis, cost-effectiveness models and the analysis of heterogeneity, applied to intracranial aneurysms and subarachnoid hemorrhage (SAH). Subarachnoid hemorrhage is a subset of stroke that usually occurs at relatively young age and has poor prognosis. Although, the

A Model of Decision-Making Based on Critical Thinking

OpenAIRE

Uluçınar, Ufuk; Aypay, Ahmet

2016-01-01

The aim of this study is to examine the causal relationships between high school students' inquisitiveness, open-mindedness, causal thinking, and rational and intuitive decision-making dispositions through an assumed model based on research data. This study was designed in correlational model. Confirmatory factor analysis and path analysis, which are structural equation modelling applications, were used to explain these relationships. The participants were 404 students studying in five high s...

Applications of decision analysis and related techniques to industrial engineering problems at KSC

Science.gov (United States)

Evans, Gerald W.

1995-01-01

This report provides: (1) a discussion of the origination of decision analysis problems (well-structured problems) from ill-structured problems; (2) a review of the various methodologies and software packages for decision analysis and related problem areas; (3) a discussion of how the characteristics of a decision analysis problem affect the choice of modeling methodologies, thus providing a guide as to when to choose a particular methodology; and (4) examples of applications of decision analysis to particular problems encountered by the IE Group at KSC. With respect to the specific applications at KSC, particular emphasis is placed on the use of the Demos software package (Lumina Decision Systems, 1993).

Methodology and preliminary models for analyzing nuclear safeguards decisions

International Nuclear Information System (INIS)

1978-11-01

This report describes a general analytical tool designed to assist the NRC in making nuclear safeguards decisions. The approach is based on decision analysis--a quantitative procedure for making decisions under uncertain conditions. The report: describes illustrative models that quantify the probability and consequences of diverted special nuclear material and the costs of safeguarding the material, demonstrates a methodology for using this information to set safeguards regulations (safeguards criteria), and summarizes insights gained in a very preliminary assessment of a hypothetical reprocessing plant

Risk Analysis and Decision Making FY 2013 Milestone Report

Energy Technology Data Exchange (ETDEWEB)

Engel, David W.; Dalton, Angela C.; Dale, Crystal; Jones, Edward; Thompson, J.

2013-06-01

Risk analysis and decision making is one of the critical objectives of CCSI, which seeks to use information from science-based models with quantified uncertainty to inform decision makers who are making large capital investments. The goal of this task is to develop tools and capabilities to facilitate the development of risk models tailored for carbon capture technologies, quantify the uncertainty of model predictions, and estimate the technical and financial risks associated with the system. This effort aims to reduce costs by identifying smarter demonstrations, which could accelerate development and deployment of the technology by several years.

Theoretical Background for the Decision-Making Process Modelling under Controlled Intervention Conditions

Directory of Open Access Journals (Sweden)

Bakanauskienė Irena

2017-12-01

Full Text Available This article is intended to theoretically justify the decision-making process model for the cases, when active participation of investing entities in controlling the activities of an organisation and their results is noticeable. Based on scientific literature analysis, a concept of controlled conditions is formulated, and using a rational approach to the decision-making process, a model of the 11-steps decision-making process under controlled intervention is presented. Also, there have been unified conditions, describing the case of controlled interventions thus providing preconditions to ensure the adequacy of the proposed decision-making process model.

Sensitivity analysis for decision-making using the MORE method-A Pareto approach

International Nuclear Information System (INIS)

Ravalico, Jakin K.; Maier, Holger R.; Dandy, Graeme C.

2009-01-01

Integrated Assessment Modelling (IAM) incorporates knowledge from different disciplines to provide an overarching assessment of the impact of different management decisions. The complex nature of these models, which often include non-linearities and feedback loops, requires special attention for sensitivity analysis. This is especially true when the models are used to form the basis of management decisions, where it is important to assess how sensitive the decisions being made are to changes in model parameters. This research proposes an extension to the Management Option Rank Equivalence (MORE) method of sensitivity analysis; a new method of sensitivity analysis developed specifically for use in IAM and decision-making. The extension proposes using a multi-objective Pareto optimal search to locate minimum combined parameter changes that result in a change in the preferred management option. It is demonstrated through a case study of the Namoi River, where results show that the extension to MORE is able to provide sensitivity information for individual parameters that takes into account simultaneous variations in all parameters. Furthermore, the increased sensitivities to individual parameters that are discovered when joint parameter variation is taken into account shows the importance of ensuring that any sensitivity analysis accounts for these changes.

A novel computer based expert decision making model for prostate cancer disease management.

Science.gov (United States)

Richman, Martin B; Forman, Ernest H; Bayazit, Yildirim; Einstein, Douglas B; Resnick, Martin I; Stovsky, Mark D

2005-12-01

We propose a strategic, computer based, prostate cancer decision making model based on the analytic hierarchy process. We developed a model that improves physician-patient joint decision making and enhances the treatment selection process by making this critical decision rational and evidence based. Two groups (patient and physician-expert) completed a clinical study comparing an initial disease management choice with the highest ranked option generated by the computer model. Participants made pairwise comparisons to derive priorities for the objectives and subobjectives related to the disease management decision. The weighted comparisons were then applied to treatment options to yield prioritized rank lists that reflect the likelihood that a given alternative will achieve the participant treatment goal. Aggregate data were evaluated by inconsistency ratio analysis and sensitivity analysis, which assessed the influence of individual objectives and subobjectives on the final rank list of treatment options. Inconsistency ratios less than 0.05 were reliably generated, indicating that judgments made within the model were mathematically rational. The aggregate prioritized list of treatment options was tabulated for the patient and physician groups with similar outcomes for the 2 groups. Analysis of the major defining objectives in the treatment selection decision demonstrated the same rank order for the patient and physician groups with cure, survival and quality of life being more important than controlling cancer, preventing major complications of treatment, preventing blood transfusion complications and limiting treatment cost. Analysis of subobjectives, including quality of life and sexual dysfunction, produced similar priority rankings for the patient and physician groups. Concordance between initial treatment choice and the highest weighted model option differed between the groups with the patient group having 59% concordance and the physician group having only 42

Fuzziness and fuzzy modelling in Bulgaria's energy policy decision-making dilemma

International Nuclear Information System (INIS)

Wang Xingquan

2006-01-01

The decision complexity resulting from imprecision in decision variables and parameters, a major difficulty for conventional decision analysis methods, can be relevantly analysed and modelled by fuzzy logic. Bulgaria's nuclear policy decision-making process implicates such complexity of imprecise nature: stakeholders, criteria, measurement, etc. Given the suitable applicability of fuzzy logic in this case, this article tries to offer a concrete fuzzy paradigm including delimitation of decision space, quantification of imprecise variables, and, of course, parameterisation. (author)

Advancing Alternative Analysis: Integration of Decision Science.

Science.gov (United States)

Malloy, Timothy F; Zaunbrecher, Virginia M; Batteate, Christina M; Blake, Ann; Carroll, William F; Corbett, Charles J; Hansen, Steffen Foss; Lempert, Robert J; Linkov, Igor; McFadden, Roger; Moran, Kelly D; Olivetti, Elsa; Ostrom, Nancy K; Romero, Michelle; Schoenung, Julie M; Seager, Thomas P; Sinsheimer, Peter; Thayer, Kristina A

2017-06-13

Decision analysis-a systematic approach to solving complex problems-offers tools and frameworks to support decision making that are increasingly being applied to environmental challenges. Alternatives analysis is a method used in regulation and product design to identify, compare, and evaluate the safety and viability of potential substitutes for hazardous chemicals. We assessed whether decision science may assist the alternatives analysis decision maker in comparing alternatives across a range of metrics. A workshop was convened that included representatives from government, academia, business, and civil society and included experts in toxicology, decision science, alternatives assessment, engineering, and law and policy. Participants were divided into two groups and were prompted with targeted questions. Throughout the workshop, the groups periodically came together in plenary sessions to reflect on other groups' findings. We concluded that the further incorporation of decision science into alternatives analysis would advance the ability of companies and regulators to select alternatives to harmful ingredients and would also advance the science of decision analysis. We advance four recommendations: a ) engaging the systematic development and evaluation of decision approaches and tools; b ) using case studies to advance the integration of decision analysis into alternatives analysis; c ) supporting transdisciplinary research; and d ) supporting education and outreach efforts. https://doi.org/10.1289/EHP483.

Ethics and rationality in information-enriched decisions: A model for technical communication

Science.gov (United States)

Dressel, S. B.; Carlson, P.; Killingsworth, M. J.

1993-12-01

In a technological culture, information has a crucial impact upon decisions, but exactly how information plays into decisions is not always clear. Decisions that are effective, efficient, and ethical must be rational. That is, we must be able to determine and present good reasons for our actions. The topic in this paper is how information relates to good reasons and thereby affects the best decisions. A brief sketch of a model for decision-making, is presented which offers a synthesis of theoretical approaches to argument and to information analysis. Then the model is applied to a brief hypothetical case. The main purpose is to put the model before an interested audience in hopes of stimulating discussion and further research.

A fuzzy multi-criteria decision-making model for trigeneration system

International Nuclear Information System (INIS)

Wang Jiangjiang; Jing Youyin; Zhang Chunfa; Shi Guohua; Zhang Xutao

2008-01-01

The decision making for trigeneration systems is a compositive project and it should be evaluated and compared in a multi-criteria analysis method. This paper presents a fuzzy multi-criteria decision-making model (FMCDM) for trigeneration systems selection and evaluation. The multi-criteria decision-making methods are briefly reviewed combining the general decision-making process. Then the fuzzy set theory, weighting method and the FMCDM model are presented. Finally, several kinds of trigeneration systems, whose dynamical sources are, respectively stirling engine, gas turbine, gas engine and solid oxide fuel cell, are compared and evaluated with a separate generation system. The case for selecting the optimal trigeneration system applied to a residential building is assessed from the technical, economical, environmental and social aspects, and the FMCDM model combining analytic hierarchical process is applied to the trigeneration case to demonstrate the decision-making process and effectiveness of proposed model. The results show that the gas engine plus lithium bromide absorption water heater/chiller unit for the residential building is the best scheme in the five options

Optimizing medical device buying. Value analysis models can help you improve decision-making process.

Science.gov (United States)

Feldstein, Josh; Brooks, Elizabeth

2010-05-01

Value Analysis Models (VAMs) are a burgeoning analytical tool that can help materials managers, operating room managers, CFOs and others to make comparative value assessments before reaching a critical purchasing decision. Although relatively new to the hospital field, more and more manufacturers are supporting these initiatives to bring critical information to their customers and the health care industry. VAMs aren't designed to conclude that one product is better than another but to be a tool that can help make the product acquisition process much easier.

Reliability analysis framework for computer-assisted medical decision systems

International Nuclear Information System (INIS)

Habas, Piotr A.; Zurada, Jacek M.; Elmaghraby, Adel S.; Tourassi, Georgia D.

2007-01-01

We present a technique that enhances computer-assisted decision (CAD) systems with the ability to assess the reliability of each individual decision they make. Reliability assessment is achieved by measuring the accuracy of a CAD system with known cases similar to the one in question. The proposed technique analyzes the feature space neighborhood of the query case to dynamically select an input-dependent set of known cases relevant to the query. This set is used to assess the local (query-specific) accuracy of the CAD system. The estimated local accuracy is utilized as a reliability measure of the CAD response to the query case. The underlying hypothesis of the study is that CAD decisions with higher reliability are more accurate. The above hypothesis was tested using a mammographic database of 1337 regions of interest (ROIs) with biopsy-proven ground truth (681 with masses, 656 with normal parenchyma). Three types of decision models, (i) a back-propagation neural network (BPNN), (ii) a generalized regression neural network (GRNN), and (iii) a support vector machine (SVM), were developed to detect masses based on eight morphological features automatically extracted from each ROI. The performance of all decision models was evaluated using the Receiver Operating Characteristic (ROC) analysis. The study showed that the proposed reliability measure is a strong predictor of the CAD system's case-specific accuracy. Specifically, the ROC area index for CAD predictions with high reliability was significantly better than for those with low reliability values. This result was consistent across all decision models investigated in the study. The proposed case-specific reliability analysis technique could be used to alert the CAD user when an opinion that is unlikely to be reliable is offered. The technique can be easily deployed in the clinical environment because it is applicable with a wide range of classifiers regardless of their structure and it requires neither additional

Economic modelling for life extension decision making

International Nuclear Information System (INIS)

Farber, M.A.; Harrison, D.L.; Carlson, D.D.

1987-01-01

This paper presents a methodology for the economic and financial analysis of nuclear plant life extension under uncertainty and demonstrates its use in a case analysis. While the economic and financial evaluation of life extension does not require new analytical tools, such studies should be based on the following three premises. First, the methodology should examine effects at the level of the company or utility system, because the most important economic implications of life extension relate to the altered generation system expansion plan. Second, it should focus on the implications of uncertainty in order to understand the factors that most affect life extension benefits and identify risk management efforts. Third, the methodology should address multiple objectives, at a minimum, both economic and financial objectives. An analysis of the role of life extension for Virginia Power's generating system was performed using the MIDAS model, developed by the Electric Power Research Institute. MIDAS is particularly well suited to this type of study because of its decision analysis framework. The model incorporates modules for load analysis, capacity expansion, production costing, financial analysis, and rates. The decision tree structure facilitates the multiple-scenario analysis of uncertainty. The model's output includes many economic and financial measures, including capital expenditures, fuel and purchases power costs, revenue requirements, average rates, external financing requirements, and coverage ratio. Based on findings for Virginia Power's Surry 1 plant, nuclear plant life extension has economic benefits for a utility's customers and financial benefits for the utility's investors. These benefits depend on a number of economic, technical and regulatory factors. The economic analysis presented in this paper identifies many of the key factors and issues relevant to life extension planning

Multi-criteria decision analysis: Limitations, pitfalls, and practical difficulties

Energy Technology Data Exchange (ETDEWEB)

Kujawski, Edouard

2003-02-01

The 2002 Winter Olympics women's figure skating competition is used as a case study to illustrate some of the limitations, pitfalls, and practical difficulties of Multi-Criteria Decision Analysis (MCDA). The paper compares several widely used models for synthesizing the multiple attributes into a single aggregate value. The various MCDA models can provide conflicting rankings of the alternatives for a common set of information even under states of certainty. Analysts involved in MCDA need to deal with the following challenging tasks: (1) selecting an appropriate analysis method, and (2) properly interpreting the results. An additional trap is the availability of software tools that implement specific MCDA models that can beguile the user with quantitative scores. These conclusions are independent of the decision domain and they should help foster better MCDA practices in many fields including systems engineering trade studies.

SPATIAL ANALYSIS AND DECISION ASSISTANCE (SADA) TRAINING COURSE

Science.gov (United States)

Spatial Analysis and Decision Assistance (SADA) is a Windows freeware program that incorporates tools from environmental assessment into an effective problem-solving environment. SADA was developed by the Institute for Environmental Modeling at the University of Tennessee and inc...

Frame-based safety analysis approach for decision-based errors

International Nuclear Information System (INIS)

Fan, Chin-Feng; Yihb, Swu

1997-01-01

A frame-based approach is proposed to analyze decision-based errors made by automatic controllers or human operators due to erroneous reference frames. An integrated framework, Two Frame Model (TFM), is first proposed to model the dynamic interaction between the physical process and the decision-making process. Two important issues, consistency and competing processes, are raised. Consistency between the physical and logic frames makes a TFM-based system work properly. Loss of consistency refers to the failure mode that the logic frame does not accurately reflect the state of the controlled processes. Once such failure occurs, hazards may arise. Among potential hazards, the competing effect between the controller and the controlled process is the most severe one, which may jeopardize a defense-in-depth design. When the logic and physical frames are inconsistent, conventional safety analysis techniques are inadequate. We propose Frame-based Fault Tree; Analysis (FFTA) and Frame-based Event Tree Analysis (FETA) under TFM to deduce the context for decision errors and to separately generate the evolution of the logical frame as opposed to that of the physical frame. This multi-dimensional analysis approach, different from the conventional correctness-centred approach, provides a panoramic view in scenario generation. Case studies using the proposed techniques are also given to demonstrate their usage and feasibility

Plutonium-238 Decision Analysis

International Nuclear Information System (INIS)

Brown, Mike; Lechel, David J.; Leigh, C.D.

1999-01-01

Five transuranic (TRU) waste sites in the Department of Energy (DOE) complex, collectively, have more than 2,100 cubic meters of Plutonium-238 (Pu-238) TRU waste that exceed the wattage restrictions of the Transuranic Package Transporter-II (TRUPACT-11). The Waste Isolation Pilot Plant (WIPP) is being developed by the DOE as a repository for TRU waste. With the Waste Isolation Pilot Plant (WIPP) opening in 1999, these sites are faced with a need to develop waste management practices that will enable the transportation of Pu-238 TRU waste to WIPP for disposal. This paper describes a decision analysis that provided a logical framework for addressing the Pu-238 TRU waste issue. The insights that can be gained by performing a formalized decision analysis are multifold. First and foremost, the very process. of formulating a decision tree forces the decision maker into structured, logical thinking where alternatives can be evaluated one against the other using a uniform set of criteria. In the process of developing the decision tree for transportation of Pu-238 TRU waste, several alternatives were eliminated and the logical order for decision making was discovered. Moreover, the key areas of uncertainty for proposed alternatives were identified and quantified. The decision analysis showed that the DOE can employ a combination approach where they will (1) use headspace gas analyses to show that a fraction of the Pu-238 TRU waste drums are no longer generating hydrogen gas and can be shipped to WIPP ''as-is'', (2) use drums and bags with advanced filter systems to repackage Pu-238 TRU waste drums that are still generating hydrogen, and (3) add hydrogen getter materials to the inner containment vessel of the TRUPACT-11to relieve the build-up of hydrogen gas during transportation of the Pu-238 TRU waste drums

A decision model for energy resource selection in China

International Nuclear Information System (INIS)

Wang Bing; Kocaoglu, Dundar F.; Daim, Tugrul U.; Yang Jiting

2010-01-01

This paper evaluates coal, petroleum, natural gas, nuclear energy and renewable energy resources as energy alternatives for China through use of a hierarchical decision model. The results indicate that although coal is still the major preferred energy alternative, it is followed closely by renewable energy. The sensitivity analysis indicates that the most critical criterion for energy selection is the current energy infrastructure. A hierarchical decision model is used, and expert judgments are quantified, to evaluate the alternatives. Criteria used for the evaluations are availability, current energy infrastructure, price, safety, environmental impacts and social impacts.

Theoretical Background for the Decision-Making Process Modelling under Controlled Intervention Conditions

OpenAIRE

Bakanauskienė Irena; Baronienė Laura

2017-01-01

This article is intended to theoretically justify the decision-making process model for the cases, when active participation of investing entities in controlling the activities of an organisation and their results is noticeable. Based on scientific literature analysis, a concept of controlled conditions is formulated, and using a rational approach to the decision-making process, a model of the 11-steps decision-making process under controlled intervention is presented. Also, there have been u...

Making ethical choices: a comprehensive decision-making model for Canadian psychologists.

Science.gov (United States)

Hadjistavropoulos, T; Malloy, D C

2000-05-01

This paper proposes a theoretical augmentation of the seven-step decision-making model outlined in the Canadian Code of Ethics for Psychologists. We propose that teleological, deontological, and existential ethical perspectives should be taken into account in the decision-making process. We also consider the influence of individual, issue-specific, significant-other, situational, and external factors on ethical decision-making. This theoretical analysis demonstrates the richness and complexity of ethical decision-making.

Applied decision analysis and risk evaluation

International Nuclear Information System (INIS)

Ferse, W.; Kruber, S.

1995-01-01

During 1994 the workgroup 'Applied Decision Analysis and Risk Evaluation; continued the work on the knowledge based decision support system XUMA-GEFA for the evaluation of the hazard potential of contaminated sites. Additionally a new research direction was started which aims at the support of a later stage of the treatment of contaminated sites: The clean-up decision. For the support of decisions arising at this stage, the methods of decision analysis will be used. Computational aids for evaluation and decision support were implemented and a case study at a waste disposal site in Saxony which turns out to be a danger for the surrounding groundwater ressource was initiated. (orig.)

Critical infrastructure protection decision support system decision model : overview and quick-start user's guide.

Energy Technology Data Exchange (ETDEWEB)

Samsa, M.; Van Kuiken, J.; Jusko, M.; Decision and Information Sciences

2008-12-01

The Critical Infrastructure Protection Decision Support System Decision Model (CIPDSS-DM) is a useful tool for comparing the effectiveness of alternative risk-mitigation strategies on the basis of CIPDSS consequence scenarios. The model is designed to assist analysts and policy makers in evaluating and selecting the most effective risk-mitigation strategies, as affected by the importance assigned to various impact measures and the likelihood of an incident. A typical CIPDSS-DM decision map plots the relative preference of alternative risk-mitigation options versus the annual probability of an undesired incident occurring once during the protective life of the investment, assumed to be 20 years. The model also enables other types of comparisons, including a decision map that isolates a selected impact variable and displays the relative preference for the options of interest--parameterized on the basis of the contribution of the isolated variable to total impact, as well as the likelihood of the incident. Satisfaction/regret analysis further assists the analyst or policy maker in evaluating the confidence with which one option can be selected over another.

Decision-case mix model for analyzing variation in cesarean rates.

Science.gov (United States)

Eldenburg, L; Waller, W S

2001-01-01

This article contributes a decision-case mix model for analyzing variation in c-section rates. Like recent contributions to the literature, the model systematically takes into account the effect of case mix. Going beyond past research, the model highlights differences in physician decision making in response to obstetric factors. Distinguishing the effects of physician decision making and case mix is important in understanding why c-section rates vary and in developing programs to effect change in physician behavior. The model was applied to a sample of deliveries at a hospital where physicians exhibited considerable variation in their c-section rates. Comparing groups with a low versus high rate, the authors' general conclusion is that the difference in physician decision tendencies (to perform a c-section), in response to specific obstetric factors, is at least as important as case mix in explaining variation in c-section rates. The exact effects of decision making versus case mix depend on how the model application defines the obstetric condition of interest and on the weighting of deliveries by their estimated "risk of Cesarean." The general conclusion is supported by an additional analysis that uses the model's elements to predict individual physicians' annual c-section rates.

Methodology and preliminary models for analyzing nuclear-safeguards decisions

International Nuclear Information System (INIS)

Judd, B.R.; Weissenberger, S.

1978-11-01

This report describes a general analytical tool designed with Lawrence Livermore Laboratory to assist the Nuclear Regulatory Commission in making nuclear safeguards decisions. The approach is based on decision analysis - a quantitative procedure for making decisions under uncertain conditions. The report: describes illustrative models that quantify the probability and consequences of diverted special nuclear material and the costs of safeguarding the material; demonstrates a methodology for using this information to set safeguards regulations (safeguards criteria); and summarizes insights gained in a very preliminary assessment of a hypothetical reprocessing plant

A regret theory approach to decision curve analysis: a novel method for eliciting decision makers' preferences and decision-making.

Science.gov (United States)

Tsalatsanis, Athanasios; Hozo, Iztok; Vickers, Andrew; Djulbegovic, Benjamin

2010-09-16

Decision curve analysis (DCA) has been proposed as an alternative method for evaluation of diagnostic tests, prediction models, and molecular markers. However, DCA is based on expected utility theory, which has been routinely violated by decision makers. Decision-making is governed by intuition (system 1), and analytical, deliberative process (system 2), thus, rational decision-making should reflect both formal principles of rationality and intuition about good decisions. We use the cognitive emotion of regret to serve as a link between systems 1 and 2 and to reformulate DCA. First, we analysed a classic decision tree describing three decision alternatives: treat, do not treat, and treat or no treat based on a predictive model. We then computed the expected regret for each of these alternatives as the difference between the utility of the action taken and the utility of the action that, in retrospect, should have been taken. For any pair of strategies, we measure the difference in net expected regret. Finally, we employ the concept of acceptable regret to identify the circumstances under which a potentially wrong strategy is tolerable to a decision-maker. We developed a novel dual visual analog scale to describe the relationship between regret associated with "omissions" (e.g. failure to treat) vs. "commissions" (e.g. treating unnecessary) and decision maker's preferences as expressed in terms of threshold probability. We then proved that the Net Expected Regret Difference, first presented in this paper, is equivalent to net benefits as described in the original DCA. Based on the concept of acceptable regret we identified the circumstances under which a decision maker tolerates a potentially wrong decision and expressed it in terms of probability of disease. We present a novel method for eliciting decision maker's preferences and an alternative derivation of DCA based on regret theory. Our approach may be intuitively more appealing to a decision-maker, particularly

Modeling Human Elements of Decision-Making

Science.gov (United States)

2002-06-01

include factors such as personality, emotion , and level of expertise, which vary from individual to individual. The process of decision - making during... rational choice theories such as utility theory, to more descriptive psychological models that focus more on the process of decision - making ...descriptive nature, they provide a more realistic representation of human decision - making than the rationally based models. However these models do

Identification of reverse logistics decision types from mathematical models

Directory of Open Access Journals (Sweden)

Pascual Cortés Pellicer

2018-04-01

Full Text Available Purpose: The increase in social awareness, politics and environmental regulation, the scarcity of raw materials and the desired “green” image, are some of the reasons that lead companies to decide for implement processes of Reverse Logistics (RL. At the time when incorporate new RL processes as key business processes, new and important decisions need to be made. Identification and knowledge of these decisions, including the information available and the implications for the company or supply chain, will be fundamental for decision-makers to achieve the best results. In the present work, the main types of RL decisions are identified. Design/methodology/approach: This paper is based on the analysis of mathematical models designed as tools to aid decision making in the field of RL. Once the types of interest work to be analyzed are defined, those studies that really deal about the object of study are searched and analyzed. The decision variables that are taken at work are identified and grouped according to the type of decision and, finally, are showed the main types of decisions used in mathematical models developed in the field of RL.     Findings: The principal conclusion of the research is that the most commonly addressed decisions with mathematical models in the field of RL are those related to the network’s configuration, followed by tactical/operative decisions such as the selections of product’s treatments to realize and the policy of returns or prices, among other decisions. Originality/value: The identification of the main decisions types of the reverse logistics will allow the managers of these processes to know and understand them better, while offer an integrated vision of them, favoring the achievement of better results.

Combining morphological analysis and Bayesian Networks for strategic decision support

CSIR Research Space (South Africa)

De Waal, AJ

2007-12-01

Full Text Available Morphological analysis (MA) and Bayesian networks (BN) are two closely related modelling methods, each of which has its advantages and disadvantages for strategic decision support modelling. MA is a method for defining, linking and evaluating...

Do violations of the axioms of expected utility theory threaten decision analysis?

Science.gov (United States)

Nease, R F

1996-01-01

Research demonstrates that people violate the independence principle of expected utility theory, raising the question of whether expected utility theory is normative for medical decision making. The author provides three arguments that violations of the independence principle are less problematic than they might first appear. First, the independence principle follows from other more fundamental axioms whose appeal may be more readily apparent than that of the independence principle. Second, the axioms need not be descriptive to be normative, and they need not be attractive to all decision makers for expected utility theory to be useful for some. Finally, by providing a metaphor of decision analysis as a conversation between the actual decision maker and a model decision maker, the author argues that expected utility theory need not be purely normative for decision analysis to be useful. In short, violations of the independence principle do not necessarily represent direct violations of the axioms of expected utility theory; behavioral violations of the axioms of expected utility theory do not necessarily imply that decision analysis is not normative; and full normativeness is not necessary for decision analysis to generate valuable insights.

Simulation Models of Human Decision-Making Processes

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Nina RIZUN

2014-10-01

Full Text Available The main purpose of the paper is presentation of the new concept of human decision-making process modeling via using the analogy with Automatic Control Theory. From the author's point of view this concept allows to develop and improve the theory of decision-making in terms of the study and classification of specificity of the human intellectual processes in different conditions. It was proved that the main distinguishing feature between the Heuristic / Intuitive and Rational Decision-Making Models is the presence of so-called phenomenon of "enrichment" of the input information with human propensity, hobbies, tendencies, expectations, axioms and judgments, presumptions or bias and their justification. In order to obtain additional knowledge about the basic intellectual processes as well as the possibility of modeling the decision results in various parameters characterizing the decision-maker, the complex of the simulation models was developed. These models are based on the assumptions that:  basic intellectual processes of the Rational Decision-Making Model can be adequately simulated and identified by the transient processes of the proportional-integral-derivative controller; basic intellectual processes of the Bounded Rationality and Intuitive Models can be adequately simulated and identified by the transient processes of the nonlinear elements.The taxonomy of the most typical automatic control theory elements and their compliance with certain decision-making models with a point of view of decision-making process specificity and decision-maker behavior during a certain time of professional activity was obtained.

Advancing Alternative Analysis: Integration of Decision Science

DEFF Research Database (Denmark)

Malloy, Timothy F; Zaunbrecher, Virginia M; Batteate, Christina

2016-01-01

Decision analysis-a systematic approach to solving complex problems-offers tools and frameworks to support decision making that are increasingly being applied to environmental challenges. Alternatives analysis is a method used in regulation and product design to identify, compare, and evaluate......, and civil society and included experts in toxicology, decision science, alternatives assessment, engineering, and law and policy. Participants were divided into two groups and prompted with targeted questions. Throughout the workshop, the groups periodically came together in plenary sessions to reflect......) engaging the systematic development and evaluation of decision approaches and tools; (2) using case studies to advance the integration of decision analysis into alternatives analysis; (3) supporting transdisciplinary research; and (4) supporting education and outreach efforts....

Using the Logistic Regression model in supporting decisions of establishing marketing strategies

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Cristinel CONSTANTIN

2015-12-01

Full Text Available This paper is about an instrumental research regarding the using of Logistic Regression model for data analysis in marketing research. The decision makers inside different organisation need relevant information to support their decisions regarding the marketing strategies. The data provided by marketing research could be computed in various ways but the multivariate data analysis models can enhance the utility of the information. Among these models we can find the Logistic Regression model, which is used for dichotomous variables. Our research is based on explanation the utility of this model and interpretation of the resulted information in order to help practitioners and researchers to use it in their future investigations

Accommodating complexity and human behaviors in decision analysis.

Energy Technology Data Exchange (ETDEWEB)

Backus, George A.; Siirola, John Daniel; Schoenwald, David Alan; Strip, David R.; Hirsch, Gary B.; Bastian, Mark S.; Braithwaite, Karl R.; Homer, Jack [Homer Consulting

2007-11-01

This is the final report for a LDRD effort to address human behavior in decision support systems. One sister LDRD effort reports the extension of this work to include actual human choices and additional simulation analyses. Another provides the background for this effort and the programmatic directions for future work. This specific effort considered the feasibility of five aspects of model development required for analysis viability. To avoid the use of classified information, healthcare decisions and the system embedding them became the illustrative example for assessment.

The potential for meta-analysis to support decision analysis in ecology.

Science.gov (United States)

Mengersen, Kerrie; MacNeil, M Aaron; Caley, M Julian

2015-06-01

Meta-analysis and decision analysis are underpinned by well-developed methods that are commonly applied to a variety of problems and disciplines. While these two fields have been closely linked in some disciplines such as medicine, comparatively little attention has been paid to the potential benefits of linking them in ecology, despite reasonable expectations that benefits would be derived from doing so. Meta-analysis combines information from multiple studies to provide more accurate parameter estimates and to reduce the uncertainty surrounding them. Decision analysis involves selecting among alternative choices using statistical information that helps to shed light on the uncertainties involved. By linking meta-analysis to decision analysis, improved decisions can be made, with quantification of the costs and benefits of alternate decisions supported by a greater density of information. Here, we briefly review concepts of both meta-analysis and decision analysis, illustrating the natural linkage between them and the benefits from explicitly linking one to the other. We discuss some examples in which this linkage has been exploited in the medical arena and how improvements in precision and reduction of structural uncertainty inherent in a meta-analysis can provide substantive improvements to decision analysis outcomes by reducing uncertainty in expected loss and maximising information from across studies. We then argue that these significant benefits could be translated to ecology, in particular to the problem of making optimal ecological decisions in the face of uncertainty. Copyright © 2013 John Wiley & Sons, Ltd.

Decision tree analysis in subarachnoid hemorrhage: prediction of outcome parameters during the course of aneurysmal subarachnoid hemorrhage using decision tree analysis.

Science.gov (United States)

Hostettler, Isabel Charlotte; Muroi, Carl; Richter, Johannes Konstantin; Schmid, Josef; Neidert, Marian Christoph; Seule, Martin; Boss, Oliver; Pangalu, Athina; Germans, Menno Robbert; Keller, Emanuela

2018-01-19

OBJECTIVE The aim of this study was to create prediction models for outcome parameters by decision tree analysis based on clinical and laboratory data in patients with aneurysmal subarachnoid hemorrhage (aSAH). METHODS The database consisted of clinical and laboratory parameters of 548 patients with aSAH who were admitted to the Neurocritical Care Unit, University Hospital Zurich. To examine the model performance, the cohort was randomly divided into a derivation cohort (60% [n = 329]; training data set) and a validation cohort (40% [n = 219]; test data set). The classification and regression tree prediction algorithm was applied to predict death, functional outcome, and ventriculoperitoneal (VP) shunt dependency. Chi-square automatic interaction detection was applied to predict delayed cerebral infarction on days 1, 3, and 7. RESULTS The overall mortality was 18.4%. The accuracy of the decision tree models was good for survival on day 1 and favorable functional outcome at all time points, with a difference between the training and test data sets of decision trees enables exploration of dependent variables in the context of multiple changing influences over the course of an illness. The decision tree currently generated increases awareness of the early systemic stress response, which is seemingly pertinent for prognostication.

Single- versus dual-process models of lexical decision performance: insights from response time distributional analysis.

Science.gov (United States)

Yap, Melvin J; Balota, David A; Cortese, Michael J; Watson, Jason M

2006-12-01

This article evaluates 2 competing models that address the decision-making processes mediating word recognition and lexical decision performance: a hybrid 2-stage model of lexical decision performance and a random-walk model. In 2 experiments, nonword type and word frequency were manipulated across 2 contrasts (pseudohomophone-legal nonword and legal-illegal nonword). When nonwords became more wordlike (i.e., BRNTA vs. BRANT vs. BRANE), response latencies to nonwords were slowed and the word frequency effect increased. More important, distributional analyses revealed that the Nonword Type = Word Frequency interaction was modulated by different components of the response time distribution, depending on the specific nonword contrast. A single-process random-walk model was able to account for this particular set of findings more successfully than the hybrid 2-stage model. (c) 2006 APA, all rights reserved.

Short term decisions for long term problems - The effect of foresight on model based energy systems analysis

International Nuclear Information System (INIS)

Keppo, Ilkka; Strubegger, Manfred

2010-01-01

This paper presents the development and demonstration of a limited foresight energy system model. The presented model is implemented as an extension to a large, linear optimization model, MESSAGE. The motivation behind changing the model is to provide an alternative decision framework, where information for the full time frame is not available immediately and sequential decision making under incomplete information is implied. While the traditional optimization framework provides the globally optimal decisions for the modeled problem, the framework presented here may offer a better description of the decision environment, under which decision makers must operate. We further modify the model to accommodate flexible dynamic constraints, which give an option to implement investments faster, albeit with a higher cost. Finally, the operation of the model is demonstrated using a moving window of foresight, with which decisions are taken for the next 30 years, but can be reconsidered later, when more information becomes available. We find that the results demonstrate some of the pitfalls of short term planning, e.g. lagging investments during earlier periods lead to higher requirements later during the century. Furthermore, the energy system remains more reliant on fossil based energy carriers, leading to higher greenhouse gas emissions.

Decision support models for solid waste management: Review and game-theoretic approaches

International Nuclear Information System (INIS)

Karmperis, Athanasios C.; Aravossis, Konstantinos; Tatsiopoulos, Ilias P.; Sotirchos, Anastasios

2013-01-01

Highlights: ► The mainly used decision support frameworks for solid waste management are reviewed. ► The LCA, CBA and MCDM models are presented and their strengths, weaknesses, similarities and possible combinations are analyzed. ► The game-theoretic approach in a solid waste management context is presented. ► The waste management bargaining game is introduced as a specific decision support framework. ► Cooperative and non-cooperative game-theoretic approaches to decision support for solid waste management are discussed. - Abstract: This paper surveys decision support models that are commonly used in the solid waste management area. Most models are mainly developed within three decision support frameworks, which are the life-cycle assessment, the cost–benefit analysis and the multi-criteria decision-making. These frameworks are reviewed and their strengths and weaknesses as well as their critical issues are analyzed, while their possible combinations and extensions are also discussed. Furthermore, the paper presents how cooperative and non-cooperative game-theoretic approaches can be used for the purpose of modeling and analyzing decision-making in situations with multiple stakeholders. Specifically, since a waste management model is sustainable when considering not only environmental and economic but also social aspects, the waste management bargaining game is introduced as a specific decision support framework in which future models can be developed

Decision support models for solid waste management: Review and game-theoretic approaches

Energy Technology Data Exchange (ETDEWEB)

Karmperis, Athanasios C., E-mail: athkarmp@mail.ntua.gr [Sector of Industrial Management and Operational Research, School of Mechanical Engineering, National Technical University of Athens, Iroon Polytechniou 9, 15780 Athens (Greece); Army Corps of Engineers, Hellenic Army General Staff, Ministry of Defence (Greece); Aravossis, Konstantinos; Tatsiopoulos, Ilias P.; Sotirchos, Anastasios [Sector of Industrial Management and Operational Research, School of Mechanical Engineering, National Technical University of Athens, Iroon Polytechniou 9, 15780 Athens (Greece)

2013-05-15

Highlights: ► The mainly used decision support frameworks for solid waste management are reviewed. ► The LCA, CBA and MCDM models are presented and their strengths, weaknesses, similarities and possible combinations are analyzed. ► The game-theoretic approach in a solid waste management context is presented. ► The waste management bargaining game is introduced as a specific decision support framework. ► Cooperative and non-cooperative game-theoretic approaches to decision support for solid waste management are discussed. - Abstract: This paper surveys decision support models that are commonly used in the solid waste management area. Most models are mainly developed within three decision support frameworks, which are the life-cycle assessment, the cost–benefit analysis and the multi-criteria decision-making. These frameworks are reviewed and their strengths and weaknesses as well as their critical issues are analyzed, while their possible combinations and extensions are also discussed. Furthermore, the paper presents how cooperative and non-cooperative game-theoretic approaches can be used for the purpose of modeling and analyzing decision-making in situations with multiple stakeholders. Specifically, since a waste management model is sustainable when considering not only environmental and economic but also social aspects, the waste management bargaining game is introduced as a specific decision support framework in which future models can be developed.

Markov Decision Process Measurement Model.

Science.gov (United States)

LaMar, Michelle M

2018-03-01

Within-task actions can provide additional information on student competencies but are challenging to model. This paper explores the potential of using a cognitive model for decision making, the Markov decision process, to provide a mapping between within-task actions and latent traits of interest. Psychometric properties of the model are explored, and simulation studies report on parameter recovery within the context of a simple strategy game. The model is then applied to empirical data from an educational game. Estimates from the model are found to correlate more strongly with posttest results than a partial-credit IRT model based on outcome data alone.

Decision analysis of shoreline protection under climate change uncertainty

Science.gov (United States)

Chao, Philip T.; Hobbs, Benjamin F.

1997-04-01

If global warming occurs, it could significantly affect water resource distribution and availability. Yet it is unclear whether the prospect of such change is relevant to water resources management decisions being made today. We model a shoreline protection decision problem with a stochastic dynamic program (SDP) to determine whether consideration of the possibility of climate change would alter the decision. Three questions are addressed with the SDP: (l) How important is climate change compared to other uncertainties?, (2) What is the economic loss if climate change uncertainty is ignored?, and (3) How does belief in climate change affect the timing of the decision? In the case study, sensitivity analysis shows that uncertainty in real discount rates has a stronger effect upon the decision than belief in climate change. Nevertheless, a strong belief in climate change makes the shoreline protection project less attractive and often alters the decision to build it.

Groundwater potentiality mapping using geoelectrical-based aquifer hydraulic parameters: A GIS-based multi-criteria decision analysis modeling approach

Directory of Open Access Journals (Sweden)

Kehinde Anthony Mogaji Hwee San Lim

2017-01-01

Full Text Available This study conducted a robust analysis on acquired 2D resistivity imaging data and borehole pumping test records to optimize groundwater potentiality mapping in Perak province, Malaysia using derived aquifer hydraulic properties. The transverse resistance (TR parameter was determined from the interpreted 2D resistivity imaging data by applying the Dar-Zarrouk parameter equation. Linear regression and GIS techniques were used to regress the estimated values for TR parameters with the aquifer transmissivity values extracted from the geospatially produced BPT records-based aquifer transmissivity map to develop the aquifer transmissivity parameter predictive (ATPP model. The reliability evaluated ATPP model using the Theil inequality coefficient measurement approach was used to establish geoelectrical-based hydraulic parameters (GHP modeling equations for the modeling of transmissivity (Tr, hydraulic conductivity (K, storativity (St, and hydraulic diffusivity (D properties. The applied GHP modeling equation results to the delineated aquifer media was used to produce aquifer potential conditioning factor maps for Tr, K, St, and D. The maps were modeled to develop an aquifer potential mapping index (APMI model via applying the multi-criteria decision analysis-analytic hierarchy process principle. The area groundwater reservoir productivity potential model map produced based on the processed APMI model estimates in the GIS environment was found to be 71% accurate. This study establishes a good alternative approach to determine aquifer hydraulic parameters even in areas where pumping test information is unavailable using a cost effective geophysical data. The produced map can be explored for hydrological decision making.

A regret theory approach to decision curve analysis: A novel method for eliciting decision makers' preferences and decision-making

Directory of Open Access Journals (Sweden)

Vickers Andrew

2010-09-01

Full Text Available Abstract Background Decision curve analysis (DCA has been proposed as an alternative method for evaluation of diagnostic tests, prediction models, and molecular markers. However, DCA is based on expected utility theory, which has been routinely violated by decision makers. Decision-making is governed by intuition (system 1, and analytical, deliberative process (system 2, thus, rational decision-making should reflect both formal principles of rationality and intuition about good decisions. We use the cognitive emotion of regret to serve as a link between systems 1 and 2 and to reformulate DCA. Methods First, we analysed a classic decision tree describing three decision alternatives: treat, do not treat, and treat or no treat based on a predictive model. We then computed the expected regret for each of these alternatives as the difference between the utility of the action taken and the utility of the action that, in retrospect, should have been taken. For any pair of strategies, we measure the difference in net expected regret. Finally, we employ the concept of acceptable regret to identify the circumstances under which a potentially wrong strategy is tolerable to a decision-maker. Results We developed a novel dual visual analog scale to describe the relationship between regret associated with "omissions" (e.g. failure to treat vs. "commissions" (e.g. treating unnecessary and decision maker's preferences as expressed in terms of threshold probability. We then proved that the Net Expected Regret Difference, first presented in this paper, is equivalent to net benefits as described in the original DCA. Based on the concept of acceptable regret we identified the circumstances under which a decision maker tolerates a potentially wrong decision and expressed it in terms of probability of disease. Conclusions We present a novel method for eliciting decision maker's preferences and an alternative derivation of DCA based on regret theory. Our approach may

Medical decision making tools: Bayesian analysis and ROC analysis

International Nuclear Information System (INIS)

Lee, Byung Do

2006-01-01

During the diagnostic process of the various oral and maxillofacial lesions, we should consider the following: 'When should we order diagnostic tests? What tests should be ordered? How should we interpret the results clinically? And how should we use this frequently imperfect information to make optimal medical decision?' For the clinicians to make proper judgement, several decision making tools are suggested. This article discusses the concept of the diagnostic accuracy (sensitivity and specificity values) with several decision making tools such as decision matrix, ROC analysis and Bayesian analysis. The article also explain the introductory concept of ORAD program

Managing health care decisions and improvement through simulation modeling.

Science.gov (United States)

Forsberg, Helena Hvitfeldt; Aronsson, Håkan; Keller, Christina; Lindblad, Staffan

2011-01-01

Simulation modeling is a way to test changes in a computerized environment to give ideas for improvements before implementation. This article reviews research literature on simulation modeling as support for health care decision making. The aim is to investigate the experience and potential value of such decision support and quality of articles retrieved. A literature search was conducted, and the selection criteria yielded 59 articles derived from diverse applications and methods. Most met the stated research-quality criteria. This review identified how simulation can facilitate decision making and that it may induce learning. Furthermore, simulation offers immediate feedback about proposed changes, allows analysis of scenarios, and promotes communication on building a shared system view and understanding of how a complex system works. However, only 14 of the 59 articles reported on implementation experiences, including how decision making was supported. On the basis of these articles, we proposed steps essential for the success of simulation projects, not just in the computer, but also in clinical reality. We also presented a novel concept combining simulation modeling with the established plan-do-study-act cycle for improvement. Future scientific inquiries concerning implementation, impact, and the value for health care management are needed to realize the full potential of simulation modeling.

Comprehensive decision tree models in bioinformatics.

Directory of Open Access Journals (Sweden)

Gregor Stiglic

Full Text Available PURPOSE: Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. METHODS: This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. RESULTS: The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. CONCLUSIONS: The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets

Comprehensive decision tree models in bioinformatics.

Science.gov (United States)

Stiglic, Gregor; Kocbek, Simon; Pernek, Igor; Kokol, Peter

2012-01-01

Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class attributes and a high number of possibly

Risk Decision Making Model for Reservoir Floodwater resources Utilization

Science.gov (United States)

Huang, X.

2017-12-01

Floodwater resources utilization(FRU) can alleviate the shortage of water resources, but there are risks. In order to safely and efficiently utilize the floodwater resources, it is necessary to study the risk of reservoir FRU. In this paper, the risk rate of exceeding the design flood water level and the risk rate of exceeding safety discharge are estimated. Based on the principle of the minimum risk and the maximum benefit of FRU, a multi-objective risk decision making model for FRU is constructed. Probability theory and mathematical statistics method is selected to calculate the risk rate; C-D production function method and emergy analysis method is selected to calculate the risk benefit; the risk loss is related to flood inundation area and unit area loss; the multi-objective decision making problem of the model is solved by the constraint method. Taking the Shilianghe reservoir in Jiangsu Province as an example, the optimal equilibrium solution of FRU of the Shilianghe reservoir is found by using the risk decision making model, and the validity and applicability of the model are verified.

A spiral model of musical decision-making.

Science.gov (United States)

Bangert, Daniel; Schubert, Emery; Fabian, Dorottya

2014-01-01

This paper describes a model of how musicians make decisions about performing notated music. The model builds on psychological theories of decision-making and was developed from empirical studies of Western art music performance that aimed to identify intuitive and deliberate processes of decision-making, a distinction consistent with dual-process theories of cognition. The model proposes that the proportion of intuitive (Type 1) and deliberate (Type 2) decision-making processes changes with increasing expertise and conceptualizes this change as movement along a continually narrowing upward spiral where the primary axis signifies principal decision-making type and the vertical axis marks level of expertise. The model is intended to have implications for the development of expertise as described in two main phases. The first is movement from a primarily intuitive approach in the early stages of learning toward greater deliberation as analytical techniques are applied during practice. The second phase occurs as deliberate decisions gradually become automatic (procedural), increasing the role of intuitive processes. As a performer examines more issues or reconsiders decisions, the spiral motion toward the deliberate side and back to the intuitive is repeated indefinitely. With increasing expertise, the spiral tightens to signify greater control over decision type selection. The model draws on existing theories, particularly Evans' (2011) Intervention Model of dual-process theories, Cognitive Continuum Theory Hammond et al. (1987), Hammond (2007), Baylor's (2001) U-shaped model for the development of intuition by level of expertise. By theorizing how musical decision-making operates over time and with increasing expertise, this model could be used as a framework for future research in music performance studies and performance science more generally.

A spiral model of musical decision-making

Directory of Open Access Journals (Sweden)

Daniel eBangert

2014-04-01

Full Text Available This paper describes a model of how musicians make decisions about performing notated music. The model builds on psychological theories of decision-making and was developed from empirical studies of Western art music performance that aimed to identify intuitive and deliberate processes of decision-making, a distinction consistent with dual-process theories of cognition. The model proposes that the proportion of intuitive (Type 1 and deliberate (Type 2 decision-making processes changes with increasing expertise and conceptualises this change as movement along a continually narrowing upward spiral where the primary axis signifies principal decision-making type and the vertical axis marks level of expertise. The model is intended to have implications for the development of expertise as described in two main phases. The first is movement from a primarily intuitive approach in the early stages of learning towards greater deliberation as analytical techniques are applied during practice. The second phase occurs as deliberate decisions gradually become automatic (procedural, increasing the role of intuitive processes. As a performer examines more issues or reconsiders decisions, the spiral motion towards the deliberate side and back to the intuitive is repeated indefinitely. With increasing expertise, the spiral tightens to signify greater control over decision type selection. The model draws on existing theories, particularly Evans’ (2011 Intervention Model of dual-process theories, Cognitive Continuum Theory (Hammond et al., 1987; Hammond, 2007, and Baylor’s (2001 U-shaped model for the development of intuition by level of expertise. By theorising how musical decision-making operates over time and with increasing expertise, this model could be used as a framework for future research in music performance studies and performance science more generally.

Decision Analysis of Advertising and Price for Bilateral Competing Supply Chain

Directory of Open Access Journals (Sweden)

Cheng-Tang Zhang

2013-01-01

Full Text Available The outcome of centralized equilibrium, prisoner's dilemma equilibrium, and decentralized equilibrium under different decision models has been provided with regards to bilateral competing supply chain system, either side of which is composed of one manufacturer and one retailer. Theoretical analysis indicates a positive correlation between price and one's own advertising investment level and a negative correlation between price and the opponent's advertising investment level. Through analysis of numerical examples, the results reveal a first mover advantage that leads to prisoner's dilemma in the system as well as the impact that price and advertising competition intensity has on the supply chain's choice of decision model.

[A prediction model for internet game addiction in adolescents: using a decision tree analysis].

Science.gov (United States)

Kim, Ki Sook; Kim, Kyung Hee

2010-06-01

This study was designed to build a theoretical frame to provide practical help to prevent and manage adolescent internet game addiction by developing a prediction model through a comprehensive analysis of related factors. The participants were 1,318 students studying in elementary, middle, and high schools in Seoul and Gyeonggi Province, Korea. Collected data were analyzed using the SPSS program. Decision Tree Analysis using the Clementine program was applied to build an optimum and significant prediction model to predict internet game addiction related to various factors, especially parent related factors. From the data analyses, the prediction model for factors related to internet game addiction presented with 5 pathways. Causative factors included gender, type of school, siblings, economic status, religion, time spent alone, gaming place, payment to Internet café, frequency, duration, parent's ability to use internet, occupation (mother), trust (father), expectations regarding adolescent's study (mother), supervising (both parents), rearing attitude (both parents). The results suggest preventive and managerial nursing programs for specific groups by path. Use of this predictive model can expand the role of school nurses, not only in counseling addicted adolescents but also, in developing and carrying out programs with parents and approaching adolescents individually through databases and computer programming.

Towards a Decision Making Model for City Break Travel

OpenAIRE

Dunne, Gerard; Flanagan, Sheila; Buckley, Joan

2011-01-01

Purpose The purpose of this paper is to examine the city break travel decision and in particular to develop a decision making model that reflects the characteristics of this type of trip taking. Method The research follows a sequential mixed methods approach consisting of two phases. Phase One involves a quantitative survey of 1,000 visitors to Dublin, from which city break and non city break visitor cohorts are separated and compared. Phase Two entails a qualitative analysis (involvin...

A communication model of shared decision making: accounting for cancer treatment decisions.

Science.gov (United States)

Siminoff, Laura A; Step, Mary M

2005-07-01

The authors present a communication model of shared decision making (CMSDM) that explicitly identifies the communication process as the vehicle for decision making in cancer treatment. In this view, decision making is necessarily a sociocommunicative process whereby people enter into a relationship, exchange information, establish preferences, and choose a course of action. The model derives from contemporary notions of behavioral decision making and ethical conceptions of the doctor-patient relationship. This article briefly reviews the theoretical approaches to decision making, notes deficiencies, and embeds a more socially based process into the dynamics of the physician-patient relationship, focusing on cancer treatment decisions. In the CMSDM, decisions depend on (a) antecedent factors that have potential to influence communication, (b) jointly constructed communication climate, and (c) treatment preferences established by the physician and the patient.

Support System Model for Value based Group Decision on Roof System Selection

Directory of Open Access Journals (Sweden)

Christiono Utomo

2011-02-01

Full Text Available A group decision support system is required on a value-based decision because there are different concern caused by differing preferences, experiences, and background. It is to enable each decision-maker to evaluate and rank the solution alternatives before engaging into negotiation with other decision-makers. Stakeholder of multi-criteria decision making problems usually evaluates the alternative solution from different perspective, making it possible to have a dominant solution among the alternatives. Each stakeholder needs to identify the goals that can be optimized and those that can be compromised in order to reach an agreement with other stakeholders. This paper presents group decision model involving three decision-makers on the selection of suitable system for a building’s roof. The objective of the research is to find an agreement options model and coalition algorithms for multi person decision with two main preferences of value which are function and cost. The methodology combines value analysis method using Function Analysis System Technique (FAST; Life Cycle Cost analysis, group decision analysis method based on Analytical Hierarchy Process (AHP in a satisfying options, and Game theory-based agent system to develop agreement option and coalition formation for the support system. The support system bridges theoretical gap between automated design in construction domain and automated negotiation in information technology domain by providing a structured methodology which can lead to systematic support system and automated negotiation. It will contribute to value management body of knowledge as an advanced method for creativity and analysis phase, since the practice of this knowledge is teamwork based. In the case of roof system selection, it reveals the start of the first negotiation round. Some of the solutions are not an option because no individual stakeholder or coalition of stakeholders desires to select it. The result indicates

A model-referenced procedure to support adversarial decision processes

International Nuclear Information System (INIS)

Bunn, D.W.; Vlahos, K.

1992-01-01

In public enquiries concerning major facilities, such as the construction of a new electric power plant, it is observed that a useable decision model should be made commonly available alongside the open provision of data and assumptions. The protagonist, eg the electric utility, generally makes use of a complex, proprietary model for detailed evaluation of options. A simple emulator of this, based upon a regression analysis of numerous scenarios, and validated by further simulations is shown to be feasible and potentially attractive. It would be in the interests of the utility to make such a model-referenced decision support method generally available. The approach is considered in relation to the recent Hinkley Point C public enquiry for a new nuclear power plant in the UK. (Author)

CRITICAL ANALYSIS OF THE RELIABILITY OF INTUITIVE MORAL DECISIONS

Directory of Open Access Journals (Sweden)

V. V. Nadurak

2017-06-01

Full Text Available Purpose of the research is a critical analysis of the reliability of intuitive moral decisions. Methodology. The work is based on the methodological attitude of empirical ethics, involving the use of findings from empirical research in ethical reflection and decision making. Originality. The main kinds of intuitive moral decisions are identified: 1 intuitively emotional decisions (i.e. decisions made under the influence of emotions that accompanies the process of moral decision making; 2 decisions made under the influence of moral risky psychological aptitudes (unconscious human tendencies that makes us think in a certain way and make decisions, unacceptable from the logical and ethical point of view; 3 intuitively normative decisions (decisions made under the influence of socially learned norms, that cause evaluative feeling «good-bad», without conscious reasoning. It was found that all of these kinds of intuitive moral decisions can lead to mistakes in the moral life. Conclusions. Considering the fact that intuition systematically leads to erroneous moral decisions, intuitive reaction cannot be the only source for making such decisions. The conscious rational reasoning can compensate for weaknesses of intuition. In this case, there is a necessity in theoretical model that would structure the knowledge about the interactions between intuitive and rational factors in moral decisions making and became the basis for making suggestions that would help us to make the right moral decision.

Multi-criteria decision analysis using hydrological indicators for decision support - a conceptual framework.

Science.gov (United States)

Butchart-Kuhlmann, Daniel; Kralisch, Sven; Meinhardt, Markus; Fleischer, Melanie

2017-04-01

Assessing the quantity and quality of water available in water stressed environments under various potential climate and land-use changes is necessary for good water and environmental resources management and governance. Within the region covered by the Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) project, such areas are common. One goal of the SASSCAL project is to develop and provide an integrated decision support system (DSS) with which decision makers (DMs) within a given catchment can obtain objective information regarding potential changes in water flow quantity and timing. The SASSCAL DSS builds upon existing data storage and distribution capability, through the SASSCAL Information System (IS), as well as the J2000 hydrological model. Using output from validated J2000 models, the SASSCAL DSS incorporates the calculation of a range of hydrological indicators based upon Indicators of Hydrological Alteration/Environmental Flow Components (IHA/EFC) calculated for a historic time series (pre-impact) and a set of model simulations based upon a selection of possible climate and land-use change scenarios (post-impact). These indicators, obtained using the IHA software package, are then used as input for a multi-criteria decision analysis (MCDA) undertaken using the open source diviz software package. The results of these analyses will provide DMs with an indication as to how various hydrological indicators within a catchment may be altered under different future scenarios, as well providing a ranking of how each scenario is preferred according to different DM preferences. Scenarios are represented through a combination of model input data and parameter settings in J2000, and preferences are represented through criteria weighting in the MCDA. Here, the methodology is presented and applied to the J2000 Luanginga model results using a set of hypothetical decision maker preference values as input for an MCDA based on

Relating cost-benefit analysis results with transport project decisions in the Netherlands

NARCIS (Netherlands)

Annema, Jan Anne; Frenken, Koen|info:eu-repo/dai/nl/207145253; Koopmans, Carl; Kroesen, Maarten

2017-01-01

This paper relates the cost-benefit analysis (CBA) results of transportation policy proposals in the Netherlands with the decision to implement or abandon the proposal. The aim of this study is to explore the relation between the CBA results and decision-making. Multinomial logit regression models

Using Consumer Behavior and Decision Models to Aid Students in Choosing a Major.

Science.gov (United States)

Kaynama, Shohreh A.; Smith, Louise W.

1996-01-01

A study found that using consumer behavior and decision models to guide students to a major can be useful and enjoyable for students. Students consider many of the basic parameters through multi-attribute and decision-analysis models, so time with professors, who were found to be the most influential group, can be used for more individual and…

Neural systems analysis of decision making during goal-directed navigation.

Science.gov (United States)

Penner, Marsha R; Mizumori, Sheri J Y

2012-01-01

The ability to make adaptive decisions during goal-directed navigation is a fundamental and highly evolved behavior that requires continual coordination of perceptions, learning and memory processes, and the planning of behaviors. Here, a neurobiological account for such coordination is provided by integrating current literatures on spatial context analysis and decision-making. This integration includes discussions of our current understanding of the role of the hippocampal system in experience-dependent navigation, how hippocampal information comes to impact midbrain and striatal decision making systems, and finally the role of the striatum in the implementation of behaviors based on recent decisions. These discussions extend across cellular to neural systems levels of analysis. Not only are key findings described, but also fundamental organizing principles within and across neural systems, as well as between neural systems functions and behavior, are emphasized. It is suggested that studying decision making during goal-directed navigation is a powerful model for studying interactive brain systems and their mediation of complex behaviors. Copyright © 2011. Published by Elsevier Ltd.

Multiscale Modelling and Analysis of Collective Decision Making in Swarm Robotics

Science.gov (United States)

Vigelius, Matthias; Meyer, Bernd; Pascoe, Geoffrey

2014-01-01

We present a unified approach to describing certain types of collective decision making in swarm robotics that bridges from a microscopic individual-based description to aggregate properties. Our approach encompasses robot swarm experiments, microscopic and probabilistic macroscopic-discrete simulations as well as an analytic mathematical model. Following up on previous work, we identify the symmetry parameter, a measure of the progress of the swarm towards a decision, as a fundamental integrated swarm property and formulate its time evolution as a continuous-time Markov process. Contrary to previous work, which justified this approach only empirically and a posteriori, we justify it from first principles and derive hard limits on the parameter regime in which it is applicable. PMID:25369026

Multiscale modelling and analysis of collective decision making in swarm robotics.

Science.gov (United States)

Vigelius, Matthias; Meyer, Bernd; Pascoe, Geoffrey

2014-01-01

We present a unified approach to describing certain types of collective decision making in swarm robotics that bridges from a microscopic individual-based description to aggregate properties. Our approach encompasses robot swarm experiments, microscopic and probabilistic macroscopic-discrete simulations as well as an analytic mathematical model. Following up on previous work, we identify the symmetry parameter, a measure of the progress of the swarm towards a decision, as a fundamental integrated swarm property and formulate its time evolution as a continuous-time Markov process. Contrary to previous work, which justified this approach only empirically and a posteriori, we justify it from first principles and derive hard limits on the parameter regime in which it is applicable.

Market orientation in the mental models of decision-makers

DEFF Research Database (Denmark)

Grunert, Klaus G.; Trondsen, Torbjørn; Campos, Emilio Gonzalo

2010-01-01

Purpose: This study determines whether predictions about different degrees of market orientation in two cross-border value chains also appear in the mental models of decision makers at two levels of these value chains. Design: The laddering method elicits mental models of actors in two value chains......: Norwegian salmon exported to Japan and Danish pork exported to Japan. The analysis of the mental models centers on potential overlap and linkages between actors in the value chain, including elements in the mental models that may relate to the actors' market orientation. Findings: In both value chains......, decision makers exhibit overlap in their views of what drives their business. The pork chain appears dominated by a focus on efficiency, technology, and quality control, though it also acknowledges communication as important. The salmon chain places more emphasis on new product development and good...

An Evaluation on Factors Influencing Decision making for Malaysia Disaster Management: The Confirmatory Factor Analysis Approach

Science.gov (United States)

Zubir, S. N. A.; Thiruchelvam, S.; Mustapha, K. N. M.; Che Muda, Z.; Ghazali, A.; Hakimie, H.

2017-12-01

For the past few years, natural disaster has been the subject of debate in disaster management especially in flood disaster. Each year, natural disaster results in significant loss of life, destruction of homes and public infrastructure, and economic hardship. Hence, an effective and efficient flood disaster management would assure non-futile efforts for life saving. The aim of this article is to examine the relationship between approach, decision maker, influence factor, result, and ethic to decision making for flood disaster management in Malaysia. The key elements of decision making in the disaster management were studied based on the literature. Questionnaire surveys were administered among lead agencies at East Coast of Malaysia in the state of Kelantan and Pahang. A total of 307 valid responses had been obtained for further analysis. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were carried out to analyse the measurement model involved in the study. The CFA for second-order reflective and first-order reflective measurement model indicates that approach, decision maker, influence factor, result, and ethic have a significant and direct effect on decision making during disaster. The results from this study showed that decision- making during disaster is an important element for disaster management to necessitate a successful collaborative decision making. The measurement model is accepted to proceed with further analysis known as Structural Equation Modeling (SEM) and can be assessed for the future research.

A New Group Decision Model Based on Grey-Intuitionistic Fuzzy-ELECTRE and VIKOR for Contractor Assessment Problem

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Hassan Hashemi

2018-05-01

Full Text Available This study introduces a new decision model with multi-criteria analysis by a group of decision makers (DMs with intuitionistic fuzzy sets (IFSs. The presented model depends on a new integration of IFSs theory, ELECTRE and VIKOR along with grey relational analysis (GRA. To portray uncertain real-life situations and take account of complex decision problem, multi-criteria group decision-making (MCGDM model by totally unknown importance are introduced with IF-setting. Hence, a weighting method depended on Entropy and IFSs, is developed to present the weights of DMs and evaluation factors. A new ranking approach is provided for prioritizing the alternatives. To indicate the applicability of the presented new decision model, an industrial application for assessing contractors in the construction industry is given and discussed from the recent literature.

The two-model problem in rational decision making

NARCIS (Netherlands)

Boumans, Marcel

2011-01-01

A model of a decision problem frames that problem in three dimensions: sample space, target probability and information structure. Each specific model imposes a specific rational decision. As a result, different models may impose different, even contradictory, rational decisions, creating choice

Manager’s decision-making in organizations –empirical analysis of bureaucratic vs. learning approach

Directory of Open Access Journals (Sweden)

Jana Frenová

2010-06-01

Full Text Available The paper is focused on the study of manager’s decision-making with respect to the basic model of learning organization, presented by P. Senge as a system model of management. On one hand, the empirical research was conducted in connection with key dimensions of organizational learning such as: 1. system thinking, 2. personal mastery, 3. mental models, 4. team learning, 5. building shared vision and 6. dynamics causes. On the other hand, the research was connected with the analysis of the bureaucratic logic of decision-making process, characterized by non-functional stability, inflexibility, individualism, power, authority and hierarchy, centralization, vagueness, fragmentariness. The objective of the research was to analyse to what extent manager’s decision–making is based on bureaucratic tools or organizational learning in either complex problem-solving or non-problemsolving decision-making. (MANOVA, method of the repeated measure, intersubject factor – situation: 1. non problematic, 2. problematic. The conclusion of analysis is that there are significant differences in character of solving of problem situation and non-problem situation decision-making: the bureaucratic attributes of decision-making are more intensive in problematic situations while learning approach is more actual in non-problematic situations. The results of our analysis have shown that managers who apply the learning organization attributes in their decision-making. are more successful in problem-solving.

IDENTIFYING OPERATIONAL REQUIREMENTS TO SELECT SUITABLE DECISION MODELS FOR A PUBLIC SECTOR EPROCUREMENT DECISION SUPPORT SYSTEM

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Mohamed Adil

2014-10-01

Full Text Available Public sector procurement should be a transparent and fair process. Strict legal requirements are enforced on public sector procurement to make it a standardised process. To make fair decisions on selecting suppliers, a practical method which adheres to legal requirements is important. The research that is the base for this paper aimed at identifying a suitable Multi-Criteria Decision Analysis (MCDA method for the specific legal and functional needs of the Maldivian Public Sector. To identify such operational requirements, a set of focus group interviews were conducted in the Maldives with public officials responsible for procurement decision making. Based on the operational requirements identified through focus groups, criteria-based evaluation is done on published MCDA methods to identify the suitable methods for e-procurement decision making. This paper describes the identification of the operational requirements and the results of the evaluation to select suitable decision models for the Maldivian context.

Preference, resistance to change, and the cumulative decision model.

Science.gov (United States)

Grace, Randolph C

2018-01-01

According to behavioral momentum theory (Nevin & Grace, 2000a), preference in concurrent chains and resistance to change in multiple schedules are independent measures of a common construct representing reinforcement history. Here I review the original studies on preference and resistance to change in which reinforcement variables were manipulated parametrically, conducted by Nevin, Grace and colleagues between 1997 and 2002, as well as more recent research. The cumulative decision model proposed by Grace and colleagues for concurrent chains is shown to provide a good account of both preference and resistance to change, and is able to predict the increased sensitivity to reinforcer rate and magnitude observed with constant-duration components. Residuals from fits of the cumulative decision model to preference and resistance to change data were positively correlated, supporting the prediction of behavioral momentum theory. Although some questions remain, the learning process assumed by the cumulative decision model, in which outcomes are compared against a criterion that represents the average outcome value in the current context, may provide a plausible model for the acquisition of differential resistance to change. © 2018 Society for the Experimental Analysis of Behavior.

An Agent-Based Model of Farmer Decision Making in Jordan

Science.gov (United States)

Selby, Philip; Medellin-Azuara, Josue; Harou, Julien; Klassert, Christian; Yoon, Jim

2016-04-01

We describe an agent based hydro-economic model of groundwater irrigated agriculture in the Jordan Highlands. The model employs a Multi-Agent-Simulation (MAS) framework and is designed to evaluate direct and indirect outcomes of climate change scenarios and policy interventions on farmer decision making, including annual land use, groundwater use for irrigation, and water sales to a water tanker market. Land use and water use decisions are simulated for groups of farms grouped by location and their behavioural and economic similarities. Decreasing groundwater levels, and the associated increase in pumping costs, are important drivers for change within Jordan'S agricultural sector. We describe how this is considered by coupling of agricultural and groundwater models. The agricultural production model employs Positive Mathematical Programming (PMP), a method for calibrating agricultural production functions to observed planted areas. PMP has successfully been used with disaggregate models for policy analysis. We adapt the PMP approach to allow explicit evaluation of the impact of pumping costs, groundwater purchase fees and a water tanker market. The work demonstrates the applicability of agent-based agricultural decision making assessment in the Jordan Highlands and its integration with agricultural model calibration methods. The proposed approach is designed and implemented with software such that it could be used to evaluate a variety of physical and human influences on decision making in agricultural water management.

A neural model of decision making

OpenAIRE

Larsen, Torben

2008-01-01

Background: A descriptive neuroeconomic model is aimed for relativity of the concept of economic man to empirical science.Method: A 4-level client-server-integrator model integrating the brain models of McLean and Luria is the general framework for the model of empirical findings.Results: Decision making relies on integration across brain levels of emotional intelligence (LU) and logico-matematico intelligence (RIA), respectively. The integrated decision making formula approaching zero by bot...

Path analysis and multi-criteria decision making: an approach for multivariate model selection and analysis in health.

Science.gov (United States)

Vasconcelos, A G; Almeida, R M; Nobre, F F

2001-08-01

This paper introduces an approach that includes non-quantitative factors for the selection and assessment of multivariate complex models in health. A goodness-of-fit based methodology combined with fuzzy multi-criteria decision-making approach is proposed for model selection. Models were obtained using the Path Analysis (PA) methodology in order to explain the interrelationship between health determinants and the post-neonatal component of infant mortality in 59 municipalities of Brazil in the year 1991. Socioeconomic and demographic factors were used as exogenous variables, and environmental, health service and agglomeration as endogenous variables. Five PA models were developed and accepted by statistical criteria of goodness-of fit. These models were then submitted to a group of experts, seeking to characterize their preferences, according to predefined criteria that tried to evaluate model relevance and plausibility. Fuzzy set techniques were used to rank the alternative models according to the number of times a model was superior to ("dominated") the others. The best-ranked model explained above 90% of the endogenous variables variation, and showed the favorable influences of income and education levels on post-neonatal mortality. It also showed the unfavorable effect on mortality of fast population growth, through precarious dwelling conditions and decreased access to sanitation. It was possible to aggregate expert opinions in model evaluation. The proposed procedure for model selection allowed the inclusion of subjective information in a clear and systematic manner.

Evidence synthesis and decision modelling to support complex decisions: stockpiling neuraminidase inhibitors for pandemic influenza usage [version 2; referees: 2 approved

Directory of Open Access Journals (Sweden)

Samuel I. Watson

2017-03-01

Full Text Available Objectives: The stockpiling of neuraminidase inhibitor (NAI antivirals as a defence against pandemic influenza is a significant public health policy decision that must be made despite a lack of conclusive evidence from randomised controlled trials regarding the effectiveness of NAIs on important clinical end points such as mortality. The objective of this study was to determine whether NAIs should be stockpiled for treatment of pandemic influenza on the basis of current evidence. Methods: A decision model for stockpiling was designed. Data on previous pandemic influenza epidemiology was combined with data on the effectiveness of NAIs in reducing mortality obtained from a recent individual participant meta-analysis using observational data. Evidence synthesis techniques and a bias modelling method for observational data were used to incorporate the evidence into the model. The stockpiling decision was modelled for adults (≥16 years old and the United Kingdom was used as an example. The main outcome was the expected net benefits of stockpiling in monetary terms. Health benefits were estimated from deaths averted through stockpiling. Results: After adjusting for biases in the estimated effectiveness of NAIs, the expected net benefit of stockpiling in the baseline analysis was £444 million, assuming a willingness to pay of £20,000/QALY ($31,000/QALY. The decision would therefore be to stockpile NAIs. There was a greater probability that the stockpile would not be utilised than utilised. However, the rare but catastrophic losses from a severe pandemic justified the decision to stockpile. Conclusions: Taking into account the available epidemiological data and evidence of effectiveness of NAIs in reducing mortality, including potential biases, a decision maker should stockpile anti-influenza medication in keeping with the postulated decision rule.

Modeling Common-Sense Decisions

Science.gov (United States)

Zak, Michail

This paper presents a methodology for efficient synthesis of dynamical model simulating a common-sense decision making process. The approach is based upon the extension of the physics' First Principles that includes behavior of living systems. The new architecture consists of motor dynamics simulating actual behavior of the object, and mental dynamics representing evolution of the corresponding knowledge-base and incorporating it in the form of information flows into the motor dynamics. The autonomy of the decision making process is achieved by a feedback from mental to motor dynamics. This feedback replaces unavailable external information by an internal knowledgebase stored in the mental model in the form of probability distributions.

Probabilistic Analysis in Management Decision Making

DEFF Research Database (Denmark)

Delmar, M. V.; Sørensen, John Dalsgaard

1992-01-01

The target group in this paper is people concerned with mathematical economic decision theory. It is shown how the numerically effective First Order Reliability Methods (FORM) can be used in rational management decision making, where some parameters in the applied decision basis are uncertainty...... quantities. The uncertainties are taken into account consistently and the decision analysis is based on the general decision theory in combination with reliability and optimization theory. Examples are shown where the described technique is used and some general conclusion are stated....

Decision strategy research: system analysis

International Nuclear Information System (INIS)

Carle, B.

2000-01-01

The objective of SCK-CEN's R and D programme on decision strategies is (1) to develop theories, methods and software tools which help decision makers shape, analyse and understand their decisions; (2) to study group processes in decision making; (3) to apply theories, methods and tools in a context related to nuclear emergency preparedness and more generally to support in a context dealing with ionising radiation; (4) to increase SCK-CEN's knowledge on general emergency preparedness and to introduce SCK-CEN staff to computer supported decision techniques. Ongoing R and D has two components: (1) the study of the use of information and knowledge transfer in group decision processes, and more specific studying important factors when computers are used as information source and communication tool; and (2) the study of preference modelling individually and during group decision processes. Principal achievements in 1999 are described

Decision strategy research: system analysis

Energy Technology Data Exchange (ETDEWEB)

Carle, B

2000-07-01

The objective of SCK-CEN's R and D programme on decision strategies is (1) to develop theories, methods and software tools which help decision makers shape, analyse and understand their decisions; (2) to study group processes in decision making; (3) to apply theories, methods and tools in a context related to nuclear emergency preparedness and more generally to support in a context dealing with ionising radiation; (4) to increase SCK-CEN's knowledge on general emergency preparedness and to introduce SCK-CEN staff to computer supported decision techniques. Ongoing R and D has two components: (1) the study of the use of information and knowledge transfer in group decision processes, and more specific studying important factors when computers are used as information source and communication tool; and (2) the study of preference modelling individually and during group decision processes. Principal achievements in 1999 are described.

Demonstration of a modelling-based multi-criteria decision analysis procedure for prioritisation of occupational risks from manufactured nanomaterials.

Science.gov (United States)

Hristozov, Danail; Zabeo, Alex; Alstrup Jensen, Keld; Gottardo, Stefania; Isigonis, Panagiotis; Maccalman, Laura; Critto, Andrea; Marcomini, Antonio

2016-11-01

Several tools to facilitate the risk assessment and management of manufactured nanomaterials (MN) have been developed. Most of them require input data on physicochemical properties, toxicity and scenario-specific exposure information. However, such data are yet not readily available, and tools that can handle data gaps in a structured way to ensure transparent risk analysis for industrial and regulatory decision making are needed. This paper proposes such a quantitative risk prioritisation tool, based on a multi-criteria decision analysis algorithm, which combines advanced exposure and dose-response modelling to calculate margins of exposure (MoE) for a number of MN in order to rank their occupational risks. We demonstrated the tool in a number of workplace exposure scenarios (ES) involving the production and handling of nanoscale titanium dioxide, zinc oxide (ZnO), silver and multi-walled carbon nanotubes. The results of this application demonstrated that bag/bin filling, manual un/loading and dumping of large amounts of dry powders led to high emissions, which resulted in high risk associated with these ES. The ZnO MN revealed considerable hazard potential in vivo, which significantly influenced the risk prioritisation results. In order to study how variations in the input data affect our results, we performed probabilistic Monte Carlo sensitivity/uncertainty analysis, which demonstrated that the performance of the proposed model is stable against changes in the exposure and hazard input variables.

Risk-based decision analysis for groundwater operable units

International Nuclear Information System (INIS)

Chiaramonte, G.R.

1995-01-01

This document proposes a streamlined approach and methodology for performing risk assessment in support of interim remedial measure (IRM) decisions involving the remediation of contaminated groundwater on the Hanford Site. This methodology, referred to as ''risk-based decision analysis,'' also supports the specification of target cleanup volumes and provides a basis for design and operation of the groundwater remedies. The risk-based decision analysis can be completed within a short time frame and concisely documented. The risk-based decision analysis is more versatile than the qualitative risk assessment (QRA), because it not only supports the need for IRMs, but also provides criteria for defining the success of the IRMs and provides the risk-basis for decisions on final remedies. For these reasons, it is proposed that, for groundwater operable units, the risk-based decision analysis should replace the more elaborate, costly, and time-consuming QRA

[Decision modeling for economic evaluation of health technologies].

Science.gov (United States)

de Soárez, Patrícia Coelho; Soares, Marta Oliveira; Novaes, Hillegonda Maria Dutilh

2014-10-01

Most economic evaluations that participate in decision-making processes for incorporation and financing of technologies of health systems use decision models to assess the costs and benefits of the compared strategies. Despite the large number of economic evaluations conducted in Brazil, there is a pressing need to conduct an in-depth methodological study of the types of decision models and their applicability in our setting. The objective of this literature review is to contribute to the knowledge and use of decision models in the national context of economic evaluations of health technologies. This article presents general definitions about models and concerns with their use; it describes the main models: decision trees, Markov chains, micro-simulation, simulation of discrete and dynamic events; it discusses the elements involved in the choice of model; and exemplifies the models addressed in national economic evaluation studies of diagnostic and therapeutic preventive technologies and health programs.

Modelling of the costs of decision support for small and medium-sized enterprises

Directory of Open Access Journals (Sweden)

Viera Tomišová

2017-01-01

Full Text Available The support of decision-making activities in small and medium-sized enterprises (SME has its specific features. When suggesting steps for the implementation of decision-support tools in the enterprise, we identified two main ways of decision-making support based on the data analysis: ERP (Enterprise Resource Planning without BI (Business Intelligence and ERP with BI. In our contribution, we present costs models of both mentioned decision support systems and their practical interpretation.

Leadership of risk decision making in a complex, technology organization: The deliberative decision making model

Science.gov (United States)

Flaming, Susan C.

2007-12-01

The continuing saga of satellite technology development is as much a story of successful risk management as of innovative engineering. How do program leaders on complex, technology projects manage high stakes risks that threaten business success and satellite performance? This grounded theory study of risk decision making portrays decision leadership practices at one communication satellite company. Integrated product team (IPT) leaders of multi-million dollar programs were interviewed and observed to develop an extensive description of the leadership skills required to navigate organizational influences and drive challenging risk decisions to closure. Based on the study's findings the researcher proposes a new decision making model, Deliberative Decision Making, to describe the program leaders' cognitive and organizational leadership practices. This Deliberative Model extends the insights of prominent decision making models including the rational (or classical) and the naturalistic and qualifies claims made by bounded rationality theory. The Deliberative Model describes how leaders proactively engage resources to play a variety of decision leadership roles. The Model incorporates six distinct types of leadership decision activities, undertaken in varying sequence based on the challenges posed by specific risks. Novel features of the Deliberative Decision Model include: an inventory of leadership methods for managing task challenges, potential stakeholder bias and debates; four types of leadership meta-decisions that guide decision processes, and aligned organizational culture. Both supporting and constraining organizational influences were observed as leaders managed major risks, requiring active leadership on the most difficult decisions. Although the company's engineering culture emphasized the importance of data-based decisions, the uncertainties intrinsic to satellite risks required expert engineering judgment to be exercised throughout. An investigation into

Decision-making models in the analysis of portal films: a clinical pilot study

International Nuclear Information System (INIS)

See, A.; Johansen, J.; Hamilton, C.; Bydder, S.A.; Hawkins, J.; Roff, M.; Denham, J.; Kron, T.

2000-01-01

Portal films continue to play an important role in the verification of radiotherapy treatment. There is still some discussion, however, as to what action should be taken after a port film has shown a radiation field deviation from the prescribed volume. It was the aim of the present pilot study to investigate the performance of three decision-making models ('Amsterdam', 'Quebec' and 'Newcastle') and an expert panel basing their decision on intuition rather than formal rules after portal film acquisition in a clinical setting. Portal films were acquired on every day during the first week of treatment for five head and neck and five prostate cancer patients (diagnostic phase). If required, the field position was modified according to our normal practice following the recommendation of the expert panel. In order to analyse the results of the models, however, additional port films were taken in the following 3 treatment weeks with the patient moved as required by the different models (intervention phase). The portal films were taken over 4 consecutive days, positioning the patient according to each of the different models on one day each. None of the models diagnosed a field misplacement in the head and neck patients, while the 'Amsterdam' and 'Quebec' models predicted a move in one prostate patient. The 'Newcastle' model, which is based on Hotelling's T 2 statistic, proved to be more sensitive and diagnosed a systematic displacement for three prostate patients. The intervention phase confirmed the diagnosis of the model, even if the three portal films taken with the patient position adjusted as required by the model proved to be insufficient to demonstrate an improvement. The 'Newcastle' model does not rely on assumptions about the random movement of patients and requires five portal films before a decision can be reached. This approach lends itself well to incorporation into electronic portal imaging 'packages', where repeated image acquisitions present no logistical

Decision-making in irrigation networks: Selecting appropriate canal structures using multi-attribute decision analysis.

Science.gov (United States)

Hosseinzade, Zeinab; Pagsuyoin, Sheree A; Ponnambalam, Kumaraswamy; Monem, Mohammad J

2017-12-01

The stiff competition for water between agriculture and non-agricultural production sectors makes it necessary to have effective management of irrigation networks in farms. However, the process of selecting flow control structures in irrigation networks is highly complex and involves different levels of decision makers. In this paper, we apply multi-attribute decision making (MADM) methodology to develop a decision analysis (DA) framework for evaluating, ranking and selecting check and intake structures for irrigation canals. The DA framework consists of identifying relevant attributes for canal structures, developing a robust scoring system for alternatives, identifying a procedure for data quality control, and identifying a MADM model for the decision analysis. An application is illustrated through an analysis for automation purposes of the Qazvin irrigation network, one of the oldest and most complex irrigation networks in Iran. A survey questionnaire designed based on the decision framework was distributed to experts, managers, and operators of the Qazvin network and to experts from the Ministry of Power in Iran. Five check structures and four intake structures were evaluated. A decision matrix was generated from the average scores collected from the survey, and was subsequently solved using TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method. To identify the most critical structure attributes for the selection process, optimal attribute weights were calculated using Entropy method. For check structures, results show that the duckbill weir is the preferred structure while the pivot weir is the least preferred. Use of the duckbill weir can potentially address the problem with existing Amil gates where manual intervention is required to regulate water levels during periods of flow extremes. For intake structures, the Neyrpic® gate and constant head orifice are the most and least preferred alternatives, respectively. Some advantages

A decision analysis approach for risk management of near-earth objects

Science.gov (United States)

Lee, Robert C.; Jones, Thomas D.; Chapman, Clark R.

2014-10-01

Risk management of near-Earth objects (NEOs; e.g., asteroids and comets) that can potentially impact Earth is an important issue that took on added urgency with the Chelyabinsk event of February 2013. Thousands of NEOs large enough to cause substantial damage are known to exist, although only a small fraction of these have the potential to impact Earth in the next few centuries. The probability and location of a NEO impact are subject to complex physics and great uncertainty, and consequences can range from minimal to devastating, depending upon the size of the NEO and location of impact. Deflecting a potential NEO impactor would be complex and expensive, and inter-agency and international cooperation would be necessary. Such deflection campaigns may be risky in themselves, and mission failure may result in unintended consequences. The benefits, risks, and costs of different potential NEO risk management strategies have not been compared in a systematic fashion. We present a decision analysis framework addressing this hazard. Decision analysis is the science of informing difficult decisions. It is inherently multi-disciplinary, especially with regard to managing catastrophic risks. Note that risk analysis clarifies the nature and magnitude of risks, whereas decision analysis guides rational risk management. Decision analysis can be used to inform strategic, policy, or resource allocation decisions. First, a problem is defined, including the decision situation and context. Second, objectives are defined, based upon what the different decision-makers and stakeholders (i.e., participants in the decision) value as important. Third, quantitative measures or scales for the objectives are determined. Fourth, alternative choices or strategies are defined. Fifth, the problem is then quantitatively modeled, including probabilistic risk analysis, and the alternatives are ranked in terms of how well they satisfy the objectives. Sixth, sensitivity analyses are performed in

MRI-based decision tree model for diagnosis of biliary atresia.

Science.gov (United States)

Kim, Yong Hee; Kim, Myung-Joon; Shin, Hyun Joo; Yoon, Haesung; Han, Seok Joo; Koh, Hong; Roh, Yun Ho; Lee, Mi-Jung

2018-02-23

To evaluate MRI findings and to generate a decision tree model for diagnosis of biliary atresia (BA) in infants with jaundice. We retrospectively reviewed features of MRI and ultrasonography (US) performed in infants with jaundice between January 2009 and June 2016 under approval of the institutional review board, including the maximum diameter of periportal signal change on MRI (MR triangular cord thickness, MR-TCT) or US (US-TCT), visibility of common bile duct (CBD) and abnormality of gallbladder (GB). Hepatic subcapsular flow was reviewed on Doppler US. We performed conditional inference tree analysis using MRI findings to generate a decision tree model. A total of 208 infants were included, 112 in the BA group and 96 in the non-BA group. Mean age at the time of MRI was 58.7 ± 36.6 days. Visibility of CBD, abnormality of GB and MR-TCT were good discriminators for the diagnosis of BA and the MRI-based decision tree using these findings with MR-TCT cut-off 5.1 mm showed 97.3 % sensitivity, 94.8 % specificity and 96.2 % accuracy. MRI-based decision tree model reliably differentiates BA in infants with jaundice. MRI can be an objective imaging modality for the diagnosis of BA. • MRI-based decision tree model reliably differentiates biliary atresia in neonatal cholestasis. • Common bile duct, gallbladder and periportal signal changes are the discriminators. • MRI has comparable performance to ultrasonography for diagnosis of biliary atresia.

Safety assessment of dangerous goods transport enterprise based on the relative entropy aggregation in group decision making model.

Science.gov (United States)

Wu, Jun; Li, Chengbing; Huo, Yueying

2014-01-01

Safety of dangerous goods transport is directly related to the operation safety of dangerous goods transport enterprise. Aiming at the problem of the high accident rate and large harm in dangerous goods logistics transportation, this paper took the group decision making problem based on integration and coordination thought into a multiagent multiobjective group decision making problem; a secondary decision model was established and applied to the safety assessment of dangerous goods transport enterprise. First of all, we used dynamic multivalue background and entropy theory building the first level multiobjective decision model. Secondly, experts were to empower according to the principle of clustering analysis, and combining with the relative entropy theory to establish a secondary rally optimization model based on relative entropy in group decision making, and discuss the solution of the model. Then, after investigation and analysis, we establish the dangerous goods transport enterprise safety evaluation index system. Finally, case analysis to five dangerous goods transport enterprises in the Inner Mongolia Autonomous Region validates the feasibility and effectiveness of this model for dangerous goods transport enterprise recognition, which provides vital decision making basis for recognizing the dangerous goods transport enterprises.

Economic decision-models for climate adaptation: a survey; Ekonomiska verktyg som beslutsstoed i klimatanpassningsarbetet: en metodoeversikt

Energy Technology Data Exchange (ETDEWEB)

Kaagebro, Elin; Vredin Johansson, Maria

2008-05-15

Several of the adaptations to the climate change we are about to experience will occur successively and voluntarily in response to the climate change experienced. In many cases these adaptations will work perfectly but, for investments and activities with relatively long life-times (say more than 25 years) and for investments and activities that are sensitive to climate extremes, climate change requires increased planning and foresight. In these situations economic decision models can aid the decision-makers through providing well-founded bases for the decisions, as well as tools for prioritizations. In this report we describe the most common economic decision-models: cost-benefit analysis (CBA), cost-effectiveness analysis (CEA) and multi-criteria analysis (MCA). The descriptions will form a foundation for the continuing work on generating tools that can be useful for local decision-makers in their pursuit of coping with climate change within the Climatools programme

Computational modelling and analysis of hippocampal-prefrontal information coding during a spatial decision-making task

Directory of Open Access Journals (Sweden)

Thomas eJahans-Price

2014-03-01

Full Text Available We introduce a computational model describing rat behaviour and the interactions of neural populations processing spatial and mnemonic information during a maze-based, decision-making task. The model integrates sensory input and implements a working memory to inform decisions at a choice point, reproducing rat behavioural data and predicting the occurrence of turn- and memory-dependent activity in neuronal networks supporting task performance. We tested these model predictions using a new software toolbox (Maze Query Language, MQL to analyse activity of medial prefrontal cortical (mPFC and dorsal hippocampal (dCA1 neurons recorded from 6 adult rats during task performance. The firing rates of dCA1 neurons discriminated context (i.e. the direction of the previous turn, whilst a subset of mPFC neurons was selective for current turn direction or context, with some conjunctively encoding both. mPFC turn-selective neurons displayed a ramping of activity on approach to the decision turn and turn-selectivity in mPFC was significantly reduced during error trials. These analyses complement data from neurophysiological recordings in non-human primates indicating that firing rates of cortical neurons correlate with integration of sensory evidence used to inform decision-making.

Probabilistic sensitivity analysis on Markov models with uncertain transition probabilities: an application in evaluating treatment decisions for type 2 diabetes.

Science.gov (United States)

Zhang, Yuanhui; Wu, Haipeng; Denton, Brian T; Wilson, James R; Lobo, Jennifer M

2017-10-27

Markov models are commonly used for decision-making studies in many application domains; however, there are no widely adopted methods for performing sensitivity analysis on such models with uncertain transition probability matrices (TPMs). This article describes two simulation-based approaches for conducting probabilistic sensitivity analysis on a given discrete-time, finite-horizon, finite-state Markov model using TPMs that are sampled over a specified uncertainty set according to a relevant probability distribution. The first approach assumes no prior knowledge of the probability distribution, and each row of a TPM is independently sampled from the uniform distribution on the row's uncertainty set. The second approach involves random sampling from the (truncated) multivariate normal distribution of the TPM's maximum likelihood estimators for its rows subject to the condition that each row has nonnegative elements and sums to one. The two sampling methods are easily implemented and have reasonable computation times. A case study illustrates the application of these methods to a medical decision-making problem involving the evaluation of treatment guidelines for glycemic control of patients with type 2 diabetes, where natural variation in a patient's glycated hemoglobin (HbA1c) is modeled as a Markov chain, and the associated TPMs are subject to uncertainty.

Info-gap decision theory decisions under severe uncertainty

CERN Document Server

Ben-Haim, Yakov

2006-01-01

Everyone makes decisions, but not everyone is a decision analyst. A decision analyst uses quantitative models and computational methods to formulate decision algorithms, assess decision performance, identify and evaluate options, determine trade-offs and risks, evaluate strategies for investigation, and so on. This book is written for decision analysts. The term ""decision analyst"" covers an extremely broad range of practitioners. Virtually all engineers involved in design (of buildings, machines, processes, etc.) or analysis (of safety, reliability, feasibility, etc.) are decision analysts,

Uncertainty modeling and decision support

International Nuclear Information System (INIS)

Yager, Ronald R.

2004-01-01

We first formulate the problem of decision making under uncertainty. The importance of the representation of our knowledge about the uncertainty in formulating a decision process is pointed out. We begin with a brief discussion of the case of probabilistic uncertainty. Next, in considerable detail, we discuss the case of decision making under ignorance. For this case the fundamental role of the attitude of the decision maker is noted and its subjective nature is emphasized. Next the case in which a Dempster-Shafer belief structure is used to model our knowledge of the uncertainty is considered. Here we also emphasize the subjective choices the decision maker must make in formulating a decision function. The case in which the uncertainty is represented by a fuzzy measure (monotonic set function) is then investigated. We then return to the Dempster-Shafer belief structure and show its relationship to the fuzzy measure. This relationship allows us to get a deeper understanding of the formulation the decision function used Dempster- Shafer framework. We discuss how this deeper understanding allows a decision analyst to better make the subjective choices needed in the formulation of the decision function

Aggregated systems model for nuclear safeguards decisions

International Nuclear Information System (INIS)

1979-03-01

This report summarizes a general analytical tool designed to assist nuclear safeguards decision-makers. The approach is based on decision analysis--a quantitative procedure for evaluating complex decision alternatives with uncertain outcomes. The report describes the general analytical approach in the context of safeguards decisions at a hypothetical nuclear fuel reprocessing plant

Dual processing model of medical decision-making

Science.gov (United States)

2012-01-01

Background Dual processing theory of human cognition postulates that reasoning and decision-making can be described as a function of both an intuitive, experiential, affective system (system I) and/or an analytical, deliberative (system II) processing system. To date no formal descriptive model of medical decision-making based on dual processing theory has been developed. Here we postulate such a model and apply it to a common clinical situation: whether treatment should be administered to the patient who may or may not have a disease. Methods We developed a mathematical model in which we linked a recently proposed descriptive psychological model of cognition with the threshold model of medical decision-making and show how this approach can be used to better understand decision-making at the bedside and explain the widespread variation in treatments observed in clinical practice. Results We show that physician’s beliefs about whether to treat at higher (lower) probability levels compared to the prescriptive therapeutic thresholds obtained via system II processing is moderated by system I and the ratio of benefit and harms as evaluated by both system I and II. Under some conditions, the system I decision maker’s threshold may dramatically drop below the expected utility threshold derived by system II. This can explain the overtreatment often seen in the contemporary practice. The opposite can also occur as in the situations where empirical evidence is considered unreliable, or when cognitive processes of decision-makers are biased through recent experience: the threshold will increase relative to the normative threshold value derived via system II using expected utility threshold. This inclination for the higher diagnostic certainty may, in turn, explain undertreatment that is also documented in the current medical practice. Conclusions We have developed the first dual processing model of medical decision-making that has potential to enrich the current medical

Dual processing model of medical decision-making.

Science.gov (United States)

Djulbegovic, Benjamin; Hozo, Iztok; Beckstead, Jason; Tsalatsanis, Athanasios; Pauker, Stephen G

2012-09-03

Dual processing theory of human cognition postulates that reasoning and decision-making can be described as a function of both an intuitive, experiential, affective system (system I) and/or an analytical, deliberative (system II) processing system. To date no formal descriptive model of medical decision-making based on dual processing theory has been developed. Here we postulate such a model and apply it to a common clinical situation: whether treatment should be administered to the patient who may or may not have a disease. We developed a mathematical model in which we linked a recently proposed descriptive psychological model of cognition with the threshold model of medical decision-making and show how this approach can be used to better understand decision-making at the bedside and explain the widespread variation in treatments observed in clinical practice. We show that physician's beliefs about whether to treat at higher (lower) probability levels compared to the prescriptive therapeutic thresholds obtained via system II processing is moderated by system I and the ratio of benefit and harms as evaluated by both system I and II. Under some conditions, the system I decision maker's threshold may dramatically drop below the expected utility threshold derived by system II. This can explain the overtreatment often seen in the contemporary practice. The opposite can also occur as in the situations where empirical evidence is considered unreliable, or when cognitive processes of decision-makers are biased through recent experience: the threshold will increase relative to the normative threshold value derived via system II using expected utility threshold. This inclination for the higher diagnostic certainty may, in turn, explain undertreatment that is also documented in the current medical practice. We have developed the first dual processing model of medical decision-making that has potential to enrich the current medical decision-making field, which is still to the

Model Driven Integrated Decision-Making in Manufacturing Enterprises

Directory of Open Access Journals (Sweden)

Richard H. Weston

2012-01-01

Full Text Available Decision making requirements and solutions are observed in four world class Manufacturing Enterprises (MEs. Observations made focus on deployed methods of complexity handling that facilitate multi-purpose, distributed decision making. Also observed are examples of partially deficient “integrated decision making” which stem from lack of understanding about how ME structural relations enable and/or constrain reachable ME behaviours. To begin to address this deficiency the paper outlines the use of a “reference model of ME decision making” which can inform the structural design of decision making systems in MEs. Also outlined is a “systematic model driven approach to modelling ME systems” which can particularise the reference model in specific case enterprises and thereby can “underpin integrated ME decision making”. Coherent decomposition and representational mechanisms have been incorporated into the model driven approach to systemise complexity handling. The paper also describes in outline an application of the modelling method in a case study ME and explains how its use has improved the integration of previously distinct planning functions. The modelling approach is particularly innovative in respect to the way it structures the coherent creation and experimental re-use of “fit for purpose” discrete event (predictive simulation models at the multiple levels of abstraction.

Environmental sustainable decision making – The need and obstacles for integration of LCA into decision analysis

DEFF Research Database (Denmark)

Dong, Yan; Miraglia, Simona; Manzo, Stefano

2018-01-01

systems, revealing potential problem shifting between life cycle stages. Through the integration with traditional risk based decision analysis, LCA may thus facilitate a better informed decision process. In this study we explore how environmental impacts are taken into account in different fields......Decision analysis is often used to help decision makers choose among alternatives, based on the expected utility associated to each alternative as function of its consequences and potential impacts. Environmental impacts are not always among the prioritized concerns of traditional decision making...... of interest for decision makers to identify the need, potential and obstacles for integrating LCA into conventional approaches to decision problems. Three application areas are used as examples: transportation planning, flood management, and food production and consumption. The analysis of these cases shows...

Modular Architecture for Integrated Model-Based Decision Support.

Science.gov (United States)

Gaebel, Jan; Schreiber, Erik; Oeser, Alexander; Oeltze-Jafra, Steffen

2018-01-01

Model-based decision support systems promise to be a valuable addition to oncological treatments and the implementation of personalized therapies. For the integration and sharing of decision models, the involved systems must be able to communicate with each other. In this paper, we propose a modularized architecture of dedicated systems for the integration of probabilistic decision models into existing hospital environments. These systems interconnect via web services and provide model sharing and processing capabilities for clinical information systems. Along the lines of IHE integration profiles from other disciplines and the meaningful reuse of routinely recorded patient data, our approach aims for the seamless integration of decision models into hospital infrastructure and the physicians' daily work.

Integrating decision management with UML modeling concepts and tools

DEFF Research Database (Denmark)

Könemann, Patrick

2009-01-01

, but also for guiding the user by proposing subsequent decisions. In model-based software development, many decisions directly affect the structural and behavioral models used to describe and develop a software system and its architecture. However, the decisions are typically not connected to these models...... of formerly disconnected tools could improve tool usability as well as decision maker productivity....

Mental models of decision-making in a healthcare executive

OpenAIRE

Long, Katrina

2017-01-01

Purpose The importance of shared mental models in teamwork has been explored in a diverse array of artificial work groups. This study extends such research to explore the role of mental models in the strategic decision-making of a real-world senior management group. Design/Methodology Data were collected from an intact group of senior healthcare executives (N=13) through semi-structured interviews, meeting observations and internal document analysis. Participants responded to intervi...

Change Analysis and Decision Tree Based Detection Model for Residential Objects across Multiple Scales

Directory of Open Access Journals (Sweden)

CHEN Liyan

2018-03-01

Full Text Available Change analysis and detection plays important role in the updating of multi-scale databases.When overlap an updated larger-scale dataset and a to-be-updated smaller-scale dataset,people usually focus on temporal changes caused by the evolution of spatial entities.Little attention is paid to the representation changes influenced by map generalization.Using polygonal building data as an example,this study examines the changes from different perspectives,such as the reasons for their occurrence,their performance format.Based on this knowledge,we employ decision tree in field of machine learning to establish a change detection model.The aim of the proposed model is to distinguish temporal changes that need to be applied as updates to the smaller-scale dataset from representation changes.The proposed method is validated through tests using real-world building data from Guangzhou city.The experimental results show the overall precision of change detection is more than 90%,which indicates our method is effective to identify changed objects.

Human-centric decision-making models for social sciences

CERN Document Server

Pedrycz, Witold

2014-01-01

The volume delivers a wealth of effective methods to deal with various types of uncertainty inherently existing in human-centric decision problems. It elaborates on  comprehensive decision frameworks to handle different decision scenarios, which help use effectively the explicit and tacit knowledge and intuition, model perceptions and preferences in a more human-oriented style. The book presents original approaches and delivers new results on fundamentals and applications related to human-centered decision making approaches to business, economics and social systems. Individual chapters cover multi-criteria (multiattribute) decision making, decision making with prospect theory, decision making with incomplete probabilistic information, granular models of decision making and decision making realized with the use of non-additive measures. New emerging decision theories being presented as along with a wide spectrum of ongoing research make the book valuable to all interested in the field of advanced decision-mak...

Decision model incorporating utility theory and measurement of social values applied to nuclear waste management

International Nuclear Information System (INIS)

Litchfield, J.W.; Hansen, J.V.; Beck, L.C.

1975-07-01

A generalized computer-based decision analysis model was developed and tested. Several alternative concepts for ultimate disposal have already been developed; however, significant research is still required before any of these can be implemented. To make a choice based on technical estimates of the costs, short-term safety, long-term safety, and accident detection and recovery requires estimating the relative importance of each of these factors or attributes. These relative importance estimates primarily involve social values and therefore vary from one individual to the next. The approach used was to sample various public groups to determine the relative importance of each of the factors to the public. These estimates of importance weights were combined in a decision analysis model with estimates, furnished by technical experts, of the degree to which each alternative concept achieves each of the criteria. This model then integrates the two separate and unique sources of information and provides the decision maker with information as to the preferences and concerns of the public as well as the technical areas within each concept which need further research. The model can rank the alternatives using sampled public opinion and techno-economic data. This model provides a decision maker with a structured approach to subdividing complex alternatives into a set of more easily considered attributes, measuring the technical performance of each alternative relative to each attribute, estimating relevant social values, and assimilating quantitative information in a rational manner to estimate total value for each alternative. Because of the explicit nature of this decision analysis, the decision maker can select a specific alternative supported by clear documentation and justification for his assumptions and estimates. (U.S.)

Arational heuristic model of economic decision making

OpenAIRE

Grandori, Anna

2010-01-01

The article discuss the limits of both the rational actor and the behavioral paradigms in explaining and guiding innovative decision making and outlines a model of economic decision making that in the course of being 'heuristic' (research and discovery oriented) is also 'rational' (in the broad sense of following correct reasoning and scientific methods, non 'biasing'). The model specifies a set of 'rational heuristics' for innovative decision making, for the various sub-processes of problem ...

Ethical analysis to improve decision-making on health technologies

DEFF Research Database (Denmark)

Saarni, Samuli I; Hofmann, Bjørn; Lampe, Kristian

2008-01-01

Health technology assessment (HTA) is the multidisciplinary study of the implications of the development, diffusion and use of health technologies. It supports health-policy decisions by providing a joint knowledge base for decision-makers. To increase its policy relevance, HTA tries to extend...... beyond effectiveness and costs to also considering the social, organizational and ethical implications of technologies. However, a commonly accepted method for analysing the ethical aspects of health technologies is lacking. This paper describes a model for ethical analysis of health technology...... to only analyse the ethical consequences of a technology, but also the ethical issues of the whole HTA process must be considered. Selection of assessment topics, methods and outcomes is essentially a value-laden decision. Health technologies may challenge moral or cultural values and beliefs...

INCLUDING RISK IN ECONOMIC FEASIBILITY ANALYSIS:A STOCHASTIC SIMULATION MODEL FOR BLUEBERRY INVESTMENT DECISIONS IN CHILE

Directory of Open Access Journals (Sweden)

GERMÁN LOBOS

2015-12-01

Full Text Available ABSTRACT The traditional method of net present value (NPV to analyze the economic profitability of an investment (based on a deterministic approach does not adequately represent the implicit risk associated with different but correlated input variables. Using a stochastic simulation approach for evaluating the profitability of blueberry (Vaccinium corymbosum L. production in Chile, the objective of this study is to illustrate the complexity of including risk in economic feasibility analysis when the project is subject to several but correlated risks. The results of the simulation analysis suggest that the non-inclusion of the intratemporal correlation between input variables underestimate the risk associated with investment decisions. The methodological contribution of this study illustrates the complexity of the interrelationships between uncertain variables and their impact on the convenience of carrying out this type of business in Chile. The steps for the analysis of economic viability were: First, adjusted probability distributions for stochastic input variables (SIV were simulated and validated. Second, the random values of SIV were used to calculate random values of variables such as production, revenues, costs, depreciation, taxes and net cash flows. Third, the complete stochastic model was simulated with 10,000 iterations using random values for SIV. This result gave information to estimate the probability distributions of the stochastic output variables (SOV such as the net present value, internal rate of return, value at risk, average cost of production, contribution margin and return on capital. Fourth, the complete stochastic model simulation results were used to analyze alternative scenarios and provide the results to decision makers in the form of probabilities, probability distributions, and for the SOV probabilistic forecasts. The main conclusion shown that this project is a profitable alternative investment in fruit trees in

A decision modeling for phasor measurement unit location selection in smart grid systems

Science.gov (United States)

Lee, Seung Yup

As a key technology for enhancing the smart grid system, Phasor Measurement Unit (PMU) provides synchronized phasor measurements of voltages and currents of wide-area electric power grid. With various benefits from its application, one of the critical issues in utilizing PMUs is the optimal site selection of units. The main aim of this research is to develop a decision support system, which can be used in resource allocation task for smart grid system analysis. As an effort to suggest a robust decision model and standardize the decision modeling process, a harmonized modeling framework, which considers operational circumstances of component, is proposed in connection with a deterministic approach utilizing integer programming. With the results obtained from the optimal PMU placement problem, the advantages and potential that the harmonized modeling process possesses are assessed and discussed.

Handbook of Marketing Decision Models

NARCIS (Netherlands)

B. Wierenga (Berend)

2008-01-01

textabstractThis book presents the state of the art in marketing decision models, dealing with new modeling areas such as customer relationship management, customer value and online marketing, but also describes recent developments in other areas. In the category of marketing mix models, the latest

Subjective Expected Utility: A Model of Decision-Making.

Science.gov (United States)

Fischoff, Baruch; And Others

1981-01-01

Outlines a model of decision making known to researchers in the field of behavioral decision theory (BDT) as subjective expected utility (SEU). The descriptive and predictive validity of the SEU model, probability and values assessment using SEU, and decision contexts are examined, and a 54-item reference list is provided. (JL)

Modeling and Testing Landslide Hazard Using Decision Tree

Directory of Open Access Journals (Sweden)

Mutasem Sh. Alkhasawneh

2014-01-01

Full Text Available This paper proposes a decision tree model for specifying the importance of 21 factors causing the landslides in a wide area of Penang Island, Malaysia. These factors are vegetation cover, distance from the fault line, slope angle, cross curvature, slope aspect, distance from road, geology, diagonal length, longitude curvature, rugosity, plan curvature, elevation, rain perception, soil texture, surface area, distance from drainage, roughness, land cover, general curvature, tangent curvature, and profile curvature. Decision tree models are used for prediction, classification, and factors importance and are usually represented by an easy to interpret tree like structure. Four models were created using Chi-square Automatic Interaction Detector (CHAID, Exhaustive CHAID, Classification and Regression Tree (CRT, and Quick-Unbiased-Efficient Statistical Tree (QUEST. Twenty-one factors were extracted using digital elevation models (DEMs and then used as input variables for the models. A data set of 137570 samples was selected for each variable in the analysis, where 68786 samples represent landslides and 68786 samples represent no landslides. 10-fold cross-validation was employed for testing the models. The highest accuracy was achieved using Exhaustive CHAID (82.0% compared to CHAID (81.9%, CRT (75.6%, and QUEST (74.0% model. Across the four models, five factors were identified as most important factors which are slope angle, distance from drainage, surface area, slope aspect, and cross curvature.

Clarity versus complexity: land-use modeling as a practical tool for decision-makers

Science.gov (United States)

Sohl, Terry L.; Claggett, Peter

2013-01-01

The last decade has seen a remarkable increase in the number of modeling tools available to examine future land-use and land-cover (LULC) change. Integrated modeling frameworks, agent-based models, cellular automata approaches, and other modeling techniques have substantially improved the representation of complex LULC systems, with each method using a different strategy to address complexity. However, despite the development of new and better modeling tools, the use of these tools is limited for actual planning, decision-making, or policy-making purposes. LULC modelers have become very adept at creating tools for modeling LULC change, but complicated models and lack of transparency limit their utility for decision-makers. The complicated nature of many LULC models also makes it impractical or even impossible to perform a rigorous analysis of modeling uncertainty. This paper provides a review of land-cover modeling approaches and the issues causes by the complicated nature of models, and provides suggestions to facilitate the increased use of LULC models by decision-makers and other stakeholders. The utility of LULC models themselves can be improved by 1) providing model code and documentation, 2) through the use of scenario frameworks to frame overall uncertainties, 3) improving methods for generalizing key LULC processes most important to stakeholders, and 4) adopting more rigorous standards for validating models and quantifying uncertainty. Communication with decision-makers and other stakeholders can be improved by increasing stakeholder participation in all stages of the modeling process, increasing the transparency of model structure and uncertainties, and developing user-friendly decision-support systems to bridge the link between LULC science and policy. By considering these options, LULC science will be better positioned to support decision-makers and increase real-world application of LULC modeling results.

Decision-making for supplying energy projects: A four-dimensional model

International Nuclear Information System (INIS)

Smith Stegen, Karen; Palovic, Martin

2014-01-01

Highlights: • Extant pipeline evaluation models offer insufficient supplier analysis tools. • We offer a four-dimensional decision-making tool to augment extant models. • Model employs four filters to help decision makers eliminate unsuitable suppliers. • Aids in prioritization of best courses of action for overcoming obstacles. • Case study of Nabucco pipeline shows Azerbaijan would have been best supply option. - Abstract: Importing states and regions employ myriad strategies to enhance energy security, from stockpiling to diversification to efficiency programs. As has occurred in recent years, importers can seek diversification by initiating pipeline and liquefied natural gas projects, meaning they may also have to select suppliers. However, most extant pipeline evaluation models erroneously assume suppliers are known and thus neglect supplier selection. We propose a decision-making tool to augment these older models: a systematic and replicable four-dimensional model to help policymakers and managers identify suitable suppliers and prioritize the best courses of action for overcoming obstacles. The first three dimensions—timeframe, supply availability and infrastructure constraints—filter out unsuitable suppliers. The fourth dimension then assesses the political, geopolitical and commercial stability of the remaining candidates. To demonstrate the model in practice, we assess the original Nabucco pipeline proposal, which was designed to transport gas from the Caspian and Middle East regions to Europe

Dual processing model of medical decision-making

Directory of Open Access Journals (Sweden)

Djulbegovic Benjamin

2012-09-01

Full Text Available Abstract Background Dual processing theory of human cognition postulates that reasoning and decision-making can be described as a function of both an intuitive, experiential, affective system (system I and/or an analytical, deliberative (system II processing system. To date no formal descriptive model of medical decision-making based on dual processing theory has been developed. Here we postulate such a model and apply it to a common clinical situation: whether treatment should be administered to the patient who may or may not have a disease. Methods We developed a mathematical model in which we linked a recently proposed descriptive psychological model of cognition with the threshold model of medical decision-making and show how this approach can be used to better understand decision-making at the bedside and explain the widespread variation in treatments observed in clinical practice. Results We show that physician’s beliefs about whether to treat at higher (lower probability levels compared to the prescriptive therapeutic thresholds obtained via system II processing is moderated by system I and the ratio of benefit and harms as evaluated by both system I and II. Under some conditions, the system I decision maker’s threshold may dramatically drop below the expected utility threshold derived by system II. This can explain the overtreatment often seen in the contemporary practice. The opposite can also occur as in the situations where empirical evidence is considered unreliable, or when cognitive processes of decision-makers are biased through recent experience: the threshold will increase relative to the normative threshold value derived via system II using expected utility threshold. This inclination for the higher diagnostic certainty may, in turn, explain undertreatment that is also documented in the current medical practice. Conclusions We have developed the first dual processing model of medical decision-making that has potential to

Knowledge Co-production Strategies for Water Resources Modeling and Decision Making

Science.gov (United States)

Gober, P.

2016-12-01

The limited impact of scientific information on policy making and climate adaptation in North America has raised awareness of the need for new modeling strategies and knowledge transfer processes. This paper outlines the rationale for a new paradigm in water resources modeling and management, using examples from the USA and Canada. Principles include anticipatory modeling, complex system dynamics, decision making under uncertainty, visualization, capacity to represent and manipulate critical trade-offs, stakeholder engagement, local knowledge, context-specific activities, social learning, vulnerability analysis, iterative and collaborative modeling, and the concept of a boundary organization. In this framework, scientists and stakeholders are partners in the production and dissemination of knowledge for decision making, and local knowledge is fused with scientific observation and methodology. Discussion draws from experience in building long-term collaborative boundary organizations in Phoenix, Arizona in the USA and the Saskatchewan River Basin (SRB) in Canada. Examples of boundary spanning activities include the use of visualization, the concept of a decision theater, infrastructure to support social learning, social networks, and reciprocity, simulation modeling to explore "what if" scenarios of the future, surveys to elicit how water problems are framed by scientists and stakeholders, and humanistic activities (theatrical performances, art exhibitions, etc.) to draw attention to local water issues. The social processes surrounding model development and dissemination are at least as important as modeling assumptions, procedures, and results in determining whether scientific knowledge will be used effectively for water resources decision making.

Dynamics of Entropy in Quantum-like Model of Decision Making

Science.gov (United States)

Basieva, Irina; Khrennikov, Andrei; Asano, Masanari; Ohya, Masanori; Tanaka, Yoshiharu

2011-03-01

We present a quantum-like model of decision making in games of the Prisoner's Dilemma type. By this model the brain processes information by using representation of mental states in complex Hilbert space. Driven by the master equation the mental state of a player, say Alice, approaches an equilibrium point in the space of density matrices. By using this equilibrium point Alice determines her mixed (i.e., probabilistic) strategy with respect to Bob. Thus our model is a model of thinking through decoherence of initially pure mental state. Decoherence is induced by interaction with memory and external environment. In this paper we study (numerically) dynamics of quantum entropy of Alice's state in the process of decision making. Our analysis demonstrates that this dynamics depends nontrivially on the initial state of Alice's mind on her own actions and her prediction state (for possible actions of Bob.)

Cognitive processes, models and metaphors in decision research

Directory of Open Access Journals (Sweden)

Ben Newell

2008-03-01

Full Text Available Decision research in psychology has traditionally been influenced by the extit{homo oeconomicus} metaphor with its emphasis on normative models and deviations from the predictions of those models. In contrast, the principal metaphor of cognitive psychology conceptualizes humans as `information processors', employing processes of perception, memory, categorization, problem solving and so on. Many of the processes described in cognitive theories are similar to those involved in decision making, and thus increasing cross-fertilization between the two areas is an important endeavour. A wide range of models and metaphors has been proposed to explain and describe `information processing' and many models have been applied to decision making in ingenious ways. This special issue encourages cross-fertilization between cognitive psychology and decision research by providing an overview of current perspectives in one area that continues to highlight the benefits of the synergistic approach: cognitive modeling of multi-attribute decision making. In this introduction we discuss aspects of the cognitive system that need to be considered when modeling multi-attribute decision making (e.g., automatic versus controlled processing, learning and memory constraints, metacognition and illustrate how such aspects are incorporated into the approaches proposed by contributors to the special issue. We end by discussing the challenges posed by the contrasting and sometimes incompatible assumptions of the models and metaphors.

Research of Strategic Alliance Stable Decision-making Model Based on Rough Set and DEA

OpenAIRE

Zhang Yi

2013-01-01

This article uses rough set theory for stability evaluation system of strategic alliance at first. Uses data analysis method for reduction, eliminates redundant indexes. Selected 6 enterprises as a decision-making unit, then select 4 inputs and 2 outputs indexes data, using DEA model to calculate, analysis reasons for poor benefit of decision-making unit, find out improvement direction and quantity for changing, provide a reference for the alliance stability.

Models of sequential decision making in consumer lending

OpenAIRE

Kanshukan Rajaratnam; Peter A. Beling; George A. Overstreet

2016-01-01

Abstract In this paper, we introduce models of sequential decision making in consumer lending. From the definition of adverse selection in static lending models, we show that homogenous borrowers take-up offers at different instances of time when faced with a sequence of loan offers. We postulate that bounded rationality and diverse decision heuristics used by consumers drive the decisions they make about credit offers. Under that postulate, we show how observation of early decisions in a seq...

Multicriteria decision group model for the selection of suppliers

Directory of Open Access Journals (Sweden)

Luciana Hazin Alencar

2008-08-01

Full Text Available Several authors have been studying group decision making over the years, which indicates how relevant it is. This paper presents a multicriteria group decision model based on ELECTRE IV and VIP Analysis methods, to those cases where there is great divergence among the decision makers. This model includes two stages. In the first, the ELECTRE IV method is applied and a collective criteria ranking is obtained. In the second, using criteria ranking, VIP Analysis is applied and the alternatives are selected. To illustrate the model, a numerical application in the context of the selection of suppliers in project management is used. The suppliers that form part of the project team have a crucial role in project management. They are involved in a network of connected activities that can jeopardize the success of the project, if they are not undertaken in an appropriate way. The question tackled is how to select service suppliers for a project on behalf of an enterprise that assists the multiple objectives of the decision-makers.Vários autores têm estudado decisão em grupo nos últimos anos, o que indica a relevância do assunto. Esse artigo apresenta um modelo multicritério de decisão em grupo baseado nos métodos ELECTRE IV e VIP Analysis, adequado aos casos em que se tem uma grande divergência entre os decisores. Esse modelo é composto por dois estágios. No primeiro, o método ELECTRE IV é aplicado e uma ordenação dos critérios é obtida. No próximo estágio, com a ordenação dos critérios, o método VIP Analysis é aplicado e as alternativas são selecionadas. Para ilustrar o modelo, uma aplicação numérica no contexto da seleção de fornecedores em projetos é realizada. Os fornecedores que fazem parte da equipe do projeto têm um papel fundamental no gerenciamento de projetos. Eles estão envolvidos em uma rede de atividades conectadas que, caso não sejam executadas de forma apropriada, podem colocar em risco o sucesso do

Reviewing model application to support animal health decision making.

Science.gov (United States)

Singer, Alexander; Salman, Mo; Thulke, Hans-Hermann

2011-04-01

Animal health is of societal importance as it affects human welfare, and anthropogenic interests shape decision making to assure animal health. Scientific advice to support decision making is manifold. Modelling, as one piece of the scientific toolbox, is appreciated for its ability to describe and structure data, to give insight in complex processes and to predict future outcome. In this paper we study the application of scientific modelling to support practical animal health decisions. We reviewed the 35 animal health related scientific opinions adopted by the Animal Health and Animal Welfare Panel of the European Food Safety Authority (EFSA). Thirteen of these documents were based on the application of models. The review took two viewpoints, the decision maker's need and the modeller's approach. In the reviewed material three types of modelling questions were addressed by four specific model types. The correspondence between tasks and models underpinned the importance of the modelling question in triggering the modelling approach. End point quantifications were the dominating request from decision makers, implying that prediction of risk is a major need. However, due to knowledge gaps corresponding modelling studies often shed away from providing exact numbers. Instead, comparative scenario analyses were performed, furthering the understanding of the decision problem and effects of alternative management options. In conclusion, the most adequate scientific support for decision making - including available modelling capacity - might be expected if the required advice is clearly stated. Copyright © 2011 Elsevier B.V. All rights reserved.

Multi-criteria Decision Analysis to Model Ixodes ricinus Habitat Suitability.

Science.gov (United States)

Rousseau, Raphaël; McGrath, Guy; McMahon, Barry J; Vanwambeke, Sophie O

2017-09-01

Tick-borne diseases present a major threat to both human and livestock health throughout Europe. The risk of infection is directly related to the presence of its vector. Thereby it is important to know their distribution, which is strongly associated with environmental factors: the presence and availability of a suitable habitat, of a suitable climate and of hosts. The present study models the habitat suitability for Ixodes ricinus in Ireland, where data on tick distribution are scarce. Tick habitat suitability was estimated at a coarse scale (10 km) with a multi-criteria decision analysis (MCDA) method according to four different scenarios (depending on the variables used and on the weights granted to each of them). The western part of Ireland and the Wicklow mountains in the East were estimated to be the most suitable areas for I. ricinus in the island. There was a good level of agreement between results from the MCDA and recorded tick presence. The different scenarios did not affect the spatial outputs substantially. The current study suggests that tick habitat suitability can be mapped accurately at a coarse scale in a data-scarce context using knowledge-based methods. It can serve as a guideline for future countrywide sampling that would help to determine local risk of tick presence and refining knowledge on tick habitat suitability in Ireland.

Decision making model design for antivirus software selection using Factor Analysis and Analytical Hierarchy Process

Directory of Open Access Journals (Sweden)

Nurhayati Ai

2018-01-01

Full Text Available Virus spread increase significantly through the internet in 2017. One of the protection method is using antivirus software. The wide variety of antivirus software in the market tends to creating confusion among consumer. Selecting the right antivirus according to their needs has become difficult. This is the reason we conduct our research. We formulate a decision making model for antivirus software consumer. The model is constructed by using factor analysis and AHP method. First we spread questionnaires to consumer, then from those questionnaires we identified 16 variables that needs to be considered on selecting antivirus software. This 16 variables then divided into 5 factors by using factor analysis method in SPSS software. These five factors are security, performance, internal, time and capacity. To rank those factors we spread questionnaires to 6 IT expert then the data is analyzed using AHP method. The result is that performance factors gained the highest rank from all of the other factors. Thus, consumer can select antivirus software by judging the variables in the performance factors. Those variables are software loading speed, user friendly, no excessive memory use, thorough scanning, and scanning virus fast and accurately.

[Analysis of the characteristics of the older adults with depression using data mining decision tree analysis].

Science.gov (United States)

Park, Myonghwa; Choi, Sora; Shin, A Mi; Koo, Chul Hoi

2013-02-01

The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method. A large dataset from the 2008 Korean Elderly Survey was used and data of 14,970 elderly people were analyzed. Target variable was depression and 53 input variables were general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment. Data were analyzed by decision tree analysis, a data mining technique using SPSS Window 19.0 and Clementine 12.0 programs. The decision trees were classified into five different rules to define the characteristics of older adults with depression. Classification & Regression Tree (C&RT) showed the best prediction with an accuracy of 80.81% among data mining models. Factors in the rules were life satisfaction, nutritional status, daily activity difficulty due to pain, functional limitation for basic or instrumental daily activities, number of chronic diseases and daily activity difficulty due to disease. The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to these individual characteristics.

Composite decision support by combining cost-benefit and multi-criteria decision

DEFF Research Database (Denmark)

Barfod, Michael Bruhn; Salling, Kim Bang; Leleur, Steen

2011-01-01

This paper concerns composite decision support based on combining cost-benefit analysis (CBA) with multi-criteria decision analysis (MCDA) for the assessment of economic as well as strategic impacts within transport projects. Specifically a composite model for assessment (COSIMA) is presented...

An EGR performance evaluation and decision-making approach based on grey theory and grey entropy analysis.

Science.gov (United States)

Zu, Xianghuan; Yang, Chuanlei; Wang, Hechun; Wang, Yinyan

2018-01-01

Exhaust gas recirculation (EGR) is one of the main methods of reducing NOX emissions and has been widely used in marine diesel engines. This paper proposes an optimized comprehensive assessment method based on multi-objective grey situation decision theory, grey relation theory and grey entropy analysis to evaluate the performance and optimize rate determination of EGR, which currently lack clear theoretical guidance. First, multi-objective grey situation decision theory is used to establish the initial decision-making model according to the main EGR parameters. The optimal compromise between diesel engine combustion and emission performance is transformed into a decision-making target weight problem. After establishing the initial model and considering the characteristics of EGR under different conditions, an optimized target weight algorithm based on grey relation theory and grey entropy analysis is applied to generate the comprehensive evaluation and decision-making model. Finally, the proposed method is successfully applied to a TBD234V12 turbocharged diesel engine, and the results clearly illustrate the feasibility of the proposed method for providing theoretical support and a reference for further EGR optimization.

A Common Decision of Compartmental Models on the Base of Laplace Transform and Retain Function Concept

International Nuclear Information System (INIS)

Dimitrov, L.; Tzvetkova, A.; Nikolov, A.

1997-01-01

The compartmental models have a variety of applications in the analysis of the transport of radioactive and non-radioactive material in complex systems as atmosphere, hydrosphere, food chains, human body. The analysis of the biokinetic behaviour of the radioactive material into a human body gives a possibility for correct assessment of the dose from internal irradiation. Skrable has given a decision of non-cyclic linear compartmental models in case of a single intake of material in the compartments as an initial condition. The main purpose of our article is to write down a procedure for analysis of a general compartmental model in case of continuous intake of material into the compartments. This procedure is related to retain function concept and had developed on the base of Laplace transform. On the base on the proposed procedure a non-cyclic linear compartmental model decisions are given in case of both a single and a continuous intake. The Laplace images of cyclic and circular linear compartmental model decisions and their originals in some cases are given too. (author)

Application of decision analysis in antibiotic formulary choices.

Science.gov (United States)

Szymusiak-Mutnick, B; Mutnick, A H

1994-01-01

To introduce the reader to the fundamentals involved in using decision analysis as a tool in evaluating the associated costs and effectiveness of comparable therapeutic agents. Currently available literature citations were used to provide the reader with basic references whose purpose is to provide a step-by-step approach for using Decision Analysis in conducting a cost-effective comparison of three commonly used antibiotics. Data were gathered from a previously conducted retrospective chart review where the three antibiotics were used for either prophylactic, empiric, or documented infections. Although this study was limited by its retrospective nature, the reader can use the data to appreciate the fundamentals of decision analysis. The continually changing climate in healthcare and the added visibility of pharmacologic agents in the treatment and prevention of disease has increased pressure on pharmacy departments to provide therapeutic agents that are cost-effective. Decision analysis can be used to compare therapeutic agents, in terms of financial as well as clinical outcomes, in a structured fashion that all members of the health care team can understand. The application of Decision analysis is appropriate for many therapeutic agents, not just antibiotics.

The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks

OpenAIRE

Ratcliff, Roger; McKoon, Gail

2008-01-01

The diffusion decision model allows detailed explanations of behavior in two-choice discrimination tasks. In this article, the model is reviewed to show how it translates behavioral data—accuracy, mean response times, and response time distributions—into components of cognitive processing. Three experiments are used to illustrate experimental manipulations of three components: stimulus difficulty affects the quality of information on which a decision is based; instructions emphasizing either ...

Ranking of Business Process Simulation Software Tools with DEX/QQ Hierarchical Decision Model.

Science.gov (United States)

Damij, Nadja; Boškoski, Pavle; Bohanec, Marko; Mileva Boshkoska, Biljana

2016-01-01

The omnipresent need for optimisation requires constant improvements of companies' business processes (BPs). Minimising the risk of inappropriate BP being implemented is usually performed by simulating the newly developed BP under various initial conditions and "what-if" scenarios. An effectual business process simulations software (BPSS) is a prerequisite for accurate analysis of an BP. Characterisation of an BPSS tool is a challenging task due to the complex selection criteria that includes quality of visual aspects, simulation capabilities, statistical facilities, quality reporting etc. Under such circumstances, making an optimal decision is challenging. Therefore, various decision support models are employed aiding the BPSS tool selection. The currently established decision support models are either proprietary or comprise only a limited subset of criteria, which affects their accuracy. Addressing this issue, this paper proposes a new hierarchical decision support model for ranking of BPSS based on their technical characteristics by employing DEX and qualitative to quantitative (QQ) methodology. Consequently, the decision expert feeds the required information in a systematic and user friendly manner. There are three significant contributions of the proposed approach. Firstly, the proposed hierarchical model is easily extendible for adding new criteria in the hierarchical structure. Secondly, a fully operational decision support system (DSS) tool that implements the proposed hierarchical model is presented. Finally, the effectiveness of the proposed hierarchical model is assessed by comparing the resulting rankings of BPSS with respect to currently available results.

Financial Analysis, Budgeting, Decision and Control

Directory of Open Access Journals (Sweden)

Mariana Rodica TIRLEA

2013-12-01

Full Text Available The economic processes taking place in the economic environment are stochastic processes that involve and imply risks, arising from product diversification, competition, financial derivatives transactions: swaps, futures, options and from the large number of actors involved in the stock market with a higher or a smaller uncertainty degree. Competition and competitiveness, led to major and rapid change in the business environment, they determined actors participating in the economy to find solutions and methods of collecting and processing data, in such a way that, after being transformed into information they quickly help based on their analysis in decision making, planning and financial forecasting, having an effect on increasing their economic efficiency. In these circumstances the financial analysis, decision, forecasting and control, should be based on quality information that should be a value creation source. The active nature of the financial function implies the existence of a substantially large share of financial analysis, financial decision, forecasting and control.

An Assessment for A Filtered Containment Venting Strategy Using Decision Tree Models

International Nuclear Information System (INIS)

Shin, Hoyoung; Jae, Moosung

2016-01-01

In this study, a probabilistic assessment of the severe accident management strategy through a filtered containment venting system was performed by using decision tree models. In Korea, the filtered containment venting system has been installed for the first time in Wolsong unit 1 as a part of Fukushima follow-up steps, and it is planned to be applied gradually for all the remaining reactors. Filtered containment venting system, one of severe accident countermeasures, prevents a gradual pressurization of the containment building exhausting noncondensable gas and vapor to the outside of the containment building. In this study, a probabilistic assessment of the filtered containment venting strategy, one of the severe accident management strategies, was performed by using decision tree models. Containment failure frequencies of each decision were evaluated by the developed decision tree model. The optimum accident management strategies were evaluated by comparing the results. Various strategies in severe accident management guidelines (SAMG) could be improved by utilizing the methodology in this study and the offsite risk analysis methodology

An Assessment for A Filtered Containment Venting Strategy Using Decision Tree Models

Energy Technology Data Exchange (ETDEWEB)

Shin, Hoyoung; Jae, Moosung [Hanyang University, Seoul (Korea, Republic of)

2016-10-15

In this study, a probabilistic assessment of the severe accident management strategy through a filtered containment venting system was performed by using decision tree models. In Korea, the filtered containment venting system has been installed for the first time in Wolsong unit 1 as a part of Fukushima follow-up steps, and it is planned to be applied gradually for all the remaining reactors. Filtered containment venting system, one of severe accident countermeasures, prevents a gradual pressurization of the containment building exhausting noncondensable gas and vapor to the outside of the containment building. In this study, a probabilistic assessment of the filtered containment venting strategy, one of the severe accident management strategies, was performed by using decision tree models. Containment failure frequencies of each decision were evaluated by the developed decision tree model. The optimum accident management strategies were evaluated by comparing the results. Various strategies in severe accident management guidelines (SAMG) could be improved by utilizing the methodology in this study and the offsite risk analysis methodology.

Integration Of Externalized Decision Models In The Definition Of Workflows For Digital Pathology

Directory of Open Access Journals (Sweden)

J. van Leeuwen

2016-06-01

We proposed a workflow solution enabling the representation of decision models as externalized executable tasks in the process definition. Our approach separates the task implementations from the workflow model, ensuring scalability and allowing for the inclusion of complex decision logic in the workflow execution. In we depict a simplified model of a pathology diagnosis workflow (starting with the digitization of the slides, represented according to the BPMN modeling conventions. The example shows a workflow sequence that automatically orders a HER2 FISH when IHC is borderline according to defined customizable thresholds. The process model integrates an image analysis algorithm that scores images. Based on the score and the thresholds the decision model evaluates the condition and recommends the pre-ordering of an additional test when the score falls between the two thresholds. This leads to faster diagnosis and allows balancing the costs of an additional test versus the overhead of the pathologist by choosing the values of the thresholds.  

Seven business models for decision management

NARCIS (Netherlands)

dr. Martijn Zoet; Eline de Haan; Koen Smit

2016-01-01

Research, advisory companies, consultants and system integrators all predict that a lot of money will be earned with decision management (business rules, algorithms and analytics). But how can you actually make money with decision management or in other words: Which business models are exactly

A generic methodology for developing fuzzy decision models

NARCIS (Netherlands)

Bosma, R.H.; Berg, van den J.; Kaymak, Uzay; Udo, H.M.J.; Verreth, J.A.J.

2012-01-01

An important paradigm in decision-making models is utility-maximization where most models do not include actors’ motives. Fuzzy set theory on the other hand offers a method to simulate human decision-making. However, the literature describing expert-driven fuzzy logic models, rarely gives precise

Characterizing the Buyer Decision Process: the ZMOT model in Chile’s technology sector

Directory of Open Access Journals (Sweden)

Manuel Escobar Farfán

2017-06-01

Full Text Available This study presents an updated application model that identifies the intervening factors in the buyer decision process. In traditional research, the consumer is said to confront two moments of truth before making the decision to buy: first, when encountering the gondola, and then again while experiencing the product. Nevertheless, innovations in information technology have modified this traditional view to include the “Zero Moment of Truth”, known as ZMOT, a name popularized by Google. This analysis is based on the concept of the Zero Moment of Truth which relates to the process that consumers live prior to the purchase decision in which they gather information about the product or service. This study is justified by the absence of research on the ZMOT concept in Chile. To determine the factors involved in the purchase decision, a quantitative methodology was used, through the application of a survey that analyzed three perspectives: the influencing factors during the purchase decision, the activities carried out during the ZMOT, and, finally, the actions carried out after the completion of the purchase and information that will serve for future buyers is gathered. With the data obtained, an exploratory factor analysis was carried out, generating a preliminary multi-dimensional model to describe the factors in the buyer decision process during the information and experience gathering phase known as ZMOT.

Robustness of Multiple Objective Decision Analysis Preference Functions

Science.gov (United States)

2002-06-01

Bayesian Decision Theory and Utilitarian Ethics ,” American Economic Review Papers and Proceedings, 68: 223-228 (May 1978). Hartsough, Bruce R. “A...1983). Morrell, Darryl and Eric Driver. “ Bayesian Network Implementation of Levi’s Epistemic Utility Decision Theory ,” International Journal Of...elicitation efficiency for the decision maker. Subject Terms Decision Analysis, Utility Theory , Elicitation Error, Operations Research, Decision

A hierarchical Markov decision process modeling feeding and marketing decisions of growing pigs

DEFF Research Database (Denmark)

Pourmoayed, Reza; Nielsen, Lars Relund; Kristensen, Anders Ringgaard

2016-01-01

Feeding is the most important cost in the production of growing pigs and has a direct impact on the marketing decisions, growth and the final quality of the meat. In this paper, we address the sequential decision problem of when to change the feed-mix within a finisher pig pen and when to pick pigs...... for marketing. We formulate a hierarchical Markov decision process with three levels representing the decision process. The model considers decisions related to feeding and marketing and finds the optimal decision given the current state of the pen. The state of the system is based on information from on...

Dissolving decision making? : Models and their roles in decision-making processes and policy at large

NARCIS (Netherlands)

Zeiss, Ragna; van Egmond, S.

2014-01-01

This article studies the roles three science-based models play in Dutch policy and decision making processes. Key is the interaction between model construction and environment. Their political and scientific environments form contexts that shape the roles of models in policy decision making.

Evolution of quantum-like modeling in decision making processes

Energy Technology Data Exchange (ETDEWEB)

Khrennikova, Polina [School of Management, University of Leicester, University Road Leicester LE1 7RH (United Kingdom)

2012-12-18

The application of the mathematical formalism of quantum mechanics to model behavioral patterns in social science and economics is a novel and constantly emerging field. The aim of the so called 'quantum like' models is to model the decision making processes in a macroscopic setting, capturing the particular 'context' in which the decisions are taken. Several subsequent empirical findings proved that when making a decision people tend to violate the axioms of expected utility theory and Savage's Sure Thing principle, thus violating the law of total probability. A quantum probability formula was devised to describe more accurately the decision making processes. A next step in the development of QL-modeling in decision making was the application of Schroedinger equation to describe the evolution of people's mental states. A shortcoming of Schroedinger equation is its inability to capture dynamics of an open system; the brain of the decision maker can be regarded as such, actively interacting with the external environment. Recently the master equation, by which quantum physics describes the process of decoherence as the result of interaction of the mental state with the environmental 'bath', was introduced for modeling the human decision making. The external environment and memory can be referred to as a complex 'context' influencing the final decision outcomes. The master equation can be considered as a pioneering and promising apparatus for modeling the dynamics of decision making in different contexts.

Evolution of quantum-like modeling in decision making processes

Science.gov (United States)

Khrennikova, Polina

2012-12-01

The application of the mathematical formalism of quantum mechanics to model behavioral patterns in social science and economics is a novel and constantly emerging field. The aim of the so called 'quantum like' models is to model the decision making processes in a macroscopic setting, capturing the particular 'context' in which the decisions are taken. Several subsequent empirical findings proved that when making a decision people tend to violate the axioms of expected utility theory and Savage's Sure Thing principle, thus violating the law of total probability. A quantum probability formula was devised to describe more accurately the decision making processes. A next step in the development of QL-modeling in decision making was the application of Schrödinger equation to describe the evolution of people's mental states. A shortcoming of Schrödinger equation is its inability to capture dynamics of an open system; the brain of the decision maker can be regarded as such, actively interacting with the external environment. Recently the master equation, by which quantum physics describes the process of decoherence as the result of interaction of the mental state with the environmental 'bath', was introduced for modeling the human decision making. The external environment and memory can be referred to as a complex 'context' influencing the final decision outcomes. The master equation can be considered as a pioneering and promising apparatus for modeling the dynamics of decision making in different contexts.

Evolution of quantum-like modeling in decision making processes

International Nuclear Information System (INIS)

Khrennikova, Polina

2012-01-01

The application of the mathematical formalism of quantum mechanics to model behavioral patterns in social science and economics is a novel and constantly emerging field. The aim of the so called 'quantum like' models is to model the decision making processes in a macroscopic setting, capturing the particular 'context' in which the decisions are taken. Several subsequent empirical findings proved that when making a decision people tend to violate the axioms of expected utility theory and Savage's Sure Thing principle, thus violating the law of total probability. A quantum probability formula was devised to describe more accurately the decision making processes. A next step in the development of QL-modeling in decision making was the application of Schrödinger equation to describe the evolution of people's mental states. A shortcoming of Schrödinger equation is its inability to capture dynamics of an open system; the brain of the decision maker can be regarded as such, actively interacting with the external environment. Recently the master equation, by which quantum physics describes the process of decoherence as the result of interaction of the mental state with the environmental 'bath', was introduced for modeling the human decision making. The external environment and memory can be referred to as a complex 'context' influencing the final decision outcomes. The master equation can be considered as a pioneering and promising apparatus for modeling the dynamics of decision making in different contexts.

Multi-level decision making models, methods and applications

CERN Document Server

Zhang, Guangquan; Gao, Ya

2015-01-01

This monograph presents new developments in multi-level decision-making theory, technique and method in both modeling and solution issues. It especially presents how a decision support system can support managers in reaching a solution to a multi-level decision problem in practice. This monograph combines decision theories, methods, algorithms and applications effectively. It discusses in detail the models and solution algorithms of each issue of bi-level and tri-level decision-making, such as multi-leaders, multi-followers, multi-objectives, rule-set-based, and fuzzy parameters. Potential readers include organizational managers and practicing professionals, who can use the methods and software provided to solve their real decision problems; PhD students and researchers in the areas of bi-level and multi-level decision-making and decision support systems; students at an advanced undergraduate, master’s level in information systems, business administration, or the application of computer science.  

A Representation for Gaining Insight into Clinical Decision Models

Science.gov (United States)

Jimison, Holly B.

1988-01-01

For many medical domains uncertainty and patient preferences are important components of decision making. Decision theory is useful as a representation for such medical models in computer decision aids, but the methodology has typically had poor performance in the areas of explanation and user interface. The additional representation of probabilities and utilities as random variables serves to provide a framework for graphical and text insight into complicated decision models. The approach allows for efficient customization of a generic model that describes the general patient population of interest to a patient- specific model. Monte Carlo simulation is used to calculate the expected value of information and sensitivity for each model variable, thus providing a metric for deciding what to emphasize in the graphics and text summary. The computer-generated explanation includes variables that are sensitive with respect to the decision or that deviate significantly from what is typically observed. These techniques serve to keep the assessment and explanation of the patient's decision model concise, allowing the user to focus on the most important aspects for that patient.

Licensing method for new nuclear power plant: A study on decision making modeling

Energy Technology Data Exchange (ETDEWEB)

Ramli, N; Ohaga, E. O.; Jung, J. C. [KEPCO International Nuclear Graduate School, Ulsan (Korea, Republic of)

2012-10-15

This work provides a study on decision making modeling for a licensing method of a new nuclear power plant. SWOT analysis provides the licensing alternatives attributes, then the expectation from either COL or two step licensing method is decided by inputting the output from the Hurwitz mathematical model. From the analysis, COL shows the best candidate for both optimistic and pessimistic conditions.

Licensing method for new nuclear power plant: A study on decision making modeling

International Nuclear Information System (INIS)

Ramli, N; Ohaga, E. O.; Jung, J. C.

2012-01-01

This work provides a study on decision making modeling for a licensing method of a new nuclear power plant. SWOT analysis provides the licensing alternatives attributes, then the expectation from either COL or two step licensing method is decided by inputting the output from the Hurwitz mathematical model. From the analysis, COL shows the best candidate for both optimistic and pessimistic conditions

Combining morphological analysis and Bayesian networks for strategic decision support

Directory of Open Access Journals (Sweden)

A de Waal

2007-12-01

Full Text Available Morphological analysis (MA and Bayesian networks (BN are two closely related modelling methods, each of which has its advantages and disadvantages for strategic decision support modelling. MA is a method for defining, linking and evaluating problem spaces. BNs are graphical models which consist of a qualitative and quantitative part. The qualitative part is a cause-and-effect, or causal graph. The quantitative part depicts the strength of the causal relationships between variables. Combining MA and BN, as two phases in a modelling process, allows us to gain the benefits of both of these methods. The strength of MA lies in defining, linking and internally evaluating the parameters of problem spaces and BN modelling allows for the definition and quantification of causal relationships between variables. Short summaries of MA and BN are provided in this paper, followed by discussions how these two computer aided methods may be combined to better facilitate modelling procedures. A simple example is presented, concerning a recent application in the field of environmental decision support.

True pancreaticoduodenal artery aneurysms: A decision analysis

International Nuclear Information System (INIS)

Takao, Hidemasa; Nojo, Takeshi; Ohtomo, Kuni

2010-01-01

Purpose: True pancreaticoduodenal artery aneurysms are rare. No definitive study evaluating the natural history of these lesions or their preferred method of treatment has been published. The purpose of this study was to evaluate the outcome of preventive treatment of unruptured pancreaticoduodenal artery aneurysms using a Markov model. Materials and methods: With the use of a Markov model, we performed a decision analysis to evaluate the outcome of preventive treatment of unruptured pancreaticoduodenal artery aneurysms. The risk of rupture and the mortality of preventive treatment are unknown. Therefore, we performed sensitivity analysis using these parameters. Effectiveness was measured in life expectancy. Results: For 80-year-old patients, preventive treatment was dominated by no treatment if mortality rates of preventive treatment were greater than 1.4%, greater than 2.6%, greater than 3.8%, and greater than 4.8% at annual rupture rates of 1%, 2%, 3%, and 4%, respectively. For 50-year-old patients, preventive treatment was dominated by no treatment if mortality rates of preventive treatment were greater than 3.3%, greater than 5.9%, greater than 8.0%, and greater than 9.7% at annual rupture rates of 1%, 2%, 3%, and 4%, respectively. Conclusion: The effectiveness of preventive treatment of unruptured pancreaticoduodenal artery aneurysms depends on the aneurysm rupture rate, mortality rate of preventive treatment, and patient age. Taking into account the effects of these parameters is important in making treatment decisions.

Economic analysis for upgrade decision-making using a control system replacement example

International Nuclear Information System (INIS)

De Grosbois, J.; Wichman, R.; Hepburn, G.A.; Basso, R.; Kumar, V.; Deregowska, D.

2006-01-01

This paper (3rd in a CNS series) provides insight on how nuclear power plants can achieve better efficiencies and reduced operations and maintenance (O and M) costs by making well-informed equipment upgrade decisions. An investment decision in a plant system upgrade will have various technical options and associated performance outcomes. These can be modelled and evaluated using economic and financial analysis methods. The economic analysis usually involves a comparison of an investment scenario versus a no-investment scenario called difference case analysis. The investment may include several scenarios due to the existence of various options, different investment timings, or desired performance results. Classical approaches, using financial tools such as net present value and internal rate of return calculations, may be used to quantify the financial benefits of the difference cases when certainty about the outcomes is assumed. When making decisions under risk, the classical approaches may be augmented with methods that consider life-cycle costs and benefits, the cost consequences of and probability of equipment failure, the timing of the replacement, and the uncertainties in estimating costs and benefits. The use of expected value and Monte Carlo simulation, among others, allow the incorporation of financial and technical uncertainty into the analysis. Finally, sensitivity analysis enables better understanding of the problem and may improve the decision and clarify the level of confidence that should be put in the outcomes. This paper illustrates the use of financial decision analysis methods for equipment replacements using a control system upgrade example. These methods may easily be generalized for other types of plant upgrades. (author)

Which Cooperative Ownership Model Performs Better? A Financial-Decision Aid Approach

NARCIS (Netherlands)

Kalogeras, N.; Pennings, J.M.E.; Benos, T.; Doumpos, M.

2013-01-01

In this article the financial/ownership structures of agribusiness cooperatives are analyzed to examine whether new cooperative models perform better than the more traditional ones. The assessment procedure introduces a new financial decision-aid approach, which is based on data-analysis techniques

Integrating a Decision Management Tool with UML Modeling Tools

DEFF Research Database (Denmark)

Könemann, Patrick

by proposing potential subsequent design issues. In model-based software development, many decisions directly affect the structural and behavioral models used to describe and develop a software system and its architecture. However, these decisions are typically not connected to the models created during...... integration of formerly disconnected tools improves tool usability as well as decision maker productivity....

Generative Agents for Player Decision Modeling in Games

DEFF Research Database (Denmark)

Holmgård, Christoffer; Liapis, Antonios; Togelius, Julian

2014-01-01

This paper presents a method for modeling player decision making through the use of agents as AI-driven personas. The paper argues that artificial agents, as generative player models, have properties that allow them to be used as psyhometrically valid, abstract simulations of a human player......’s internal decision making processes. Such agents can then be used to interpret human decision making, as personas and playtesting tools in the game design process, as baselines for adapting agents to mimic classes of human players, or as believable, human-like opponents. This argument is explored...... in a crowdsourced decision making experiment, in which the decisions of human players are recorded in a small-scale dungeon themed puzzle game. Human decisions are compared to the decisions of a number of a priori defined “archetypical” agent-personas, and the humans are characterized by their likeness...

A Primer on Bayesian Decision Analysis With an Application to a Kidney Transplant Decision.

Science.gov (United States)

Neapolitan, Richard; Jiang, Xia; Ladner, Daniela P; Kaplan, Bruce

2016-03-01

A clinical decision support system (CDSS) is a computer program, which is designed to assist health care professionals with decision making tasks. A well-developed CDSS weighs the benefits of therapy versus the cost in terms of loss of quality of life and financial loss and recommends the decision that can be expected to provide maximum overall benefit. This article provides an introduction to developing CDSSs using Bayesian networks, such CDSS can help with the often complex decisions involving transplants. First, we review Bayes theorem in the context of medical decision making. Then, we introduce Bayesian networks, which can model probabilistic relationships among many related variables and are based on Bayes theorem. Next, we discuss influence diagrams, which are Bayesian networks augmented with decision and value nodes and which can be used to develop CDSSs that are able to recommend decisions that maximize the expected utility of the predicted outcomes to the patient. By way of comparison, we examine the benefit and challenges of using the Kidney Donor Risk Index as the sole decision tool. Finally, we develop a schema for an influence diagram that models generalized kidney transplant decisions and show how the influence diagram approach can provide the clinician and the potential transplant recipient with a valuable decision support tool.

Health versus money. Value judgments in the perspective of decision analysis.

Science.gov (United States)

Thompson, M S

1983-01-01

An important, but largely uninvestigated, value trade-off balances marginal nonhealth consumption against marginal medical care. Benefit-cost analysts have traditionally, if not fully satisfactorily, dealt with this issue by valuing health gains by their effects on productivity. Cost-effectiveness analysts compare monetary and health effects and leave their relative valuations to decision makers. A decision-analytic model using the satisfaction or utility gained from nonhealth consumption and the level of health enables one to calculate willingness to pay--a theoretically superior way of assigning monetary values to effects for benefit-cost analysis-and to determine minimally acceptable cost-effectiveness ratios. Examples show how a decision-analytic model of utility can differentiate medical actions so essential that failure to take them would be considered negligent from actions so expensive as to be unjustifiable, and can help to determine optimal legal arrangements for compensation for medical malpractice.

Markov decision processes: a tool for sequential decision making under uncertainty.

Science.gov (United States)

Alagoz, Oguzhan; Hsu, Heather; Schaefer, Andrew J; Roberts, Mark S

2010-01-01

We provide a tutorial on the construction and evaluation of Markov decision processes (MDPs), which are powerful analytical tools used for sequential decision making under uncertainty that have been widely used in many industrial and manufacturing applications but are underutilized in medical decision making (MDM). We demonstrate the use of an MDP to solve a sequential clinical treatment problem under uncertainty. Markov decision processes generalize standard Markov models in that a decision process is embedded in the model and multiple decisions are made over time. Furthermore, they have significant advantages over standard decision analysis. We compare MDPs to standard Markov-based simulation models by solving the problem of the optimal timing of living-donor liver transplantation using both methods. Both models result in the same optimal transplantation policy and the same total life expectancies for the same patient and living donor. The computation time for solving the MDP model is significantly smaller than that for solving the Markov model. We briefly describe the growing literature of MDPs applied to medical decisions.

A Layered Decision Model for Cost-Effective System Security

Energy Technology Data Exchange (ETDEWEB)

Wei, Huaqiang; Alves-Foss, James; Soule, Terry; Pforsich, Hugh; Zhang, Du; Frincke, Deborah A.

2008-10-01

System security involves decisions in at least three areas: identification of well-defined security policies, selection of cost-effective defence strategies, and implementation of real-time defence tactics. Although choices made in each of these areas affect the others, existing decision models typically handle these three decision areas in isolation. There is no comprehensive tool that can integrate them to provide a single efficient model for safeguarding a network. In addition, there is no clear way to determine which particular combinations of defence decisions result in cost-effective solutions. To address these problems, this paper introduces a Layered Decision Model (LDM) for use in deciding how to address defence decisions based on their cost-effectiveness. To validate the LDM and illustrate how it is used, we used simulation to test model rationality and applied the LDM to the design of system security for an e-commercial business case.

Multi-Criteria Decision Making For Determining A Simple Model of Supplier Selection

Science.gov (United States)

Harwati

2017-06-01

Supplier selection is a decision with many criteria. Supplier selection model usually involves more than five main criteria and more than 10 sub-criteria. In fact many model includes more than 20 criteria. Too many criteria involved in supplier selection models sometimes make it difficult to apply in many companies. This research focuses on designing supplier selection that easy and simple to be applied in the company. Analytical Hierarchy Process (AHP) is used to weighting criteria. The analysis results there are four criteria that are easy and simple can be used to select suppliers: Price (weight 0.4) shipment (weight 0.3), quality (weight 0.2) and services (weight 0.1). A real case simulation shows that simple model provides the same decision with a more complex model.

Municipal solid waste management system: decision support through systems analysis

OpenAIRE

Pires, Ana Lúcia Lourenço

2010-01-01

Thesis submitted to the Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia for the degree of Doctor of Philosophy in Environmental Engineering The present study intends to show the development of systems analysis model applied to solid waste management system, applied into AMARSUL, a solid waste management system responsible for the management of municipal solid waste produced in Setúbal peninsula, Portugal. The model developed intended to promote sustainable decision making, ...

Make or buy decision model with multi-stage manufacturing process and supplier imperfect quality

Science.gov (United States)

Pratama, Mega Aria; Rosyidi, Cucuk Nur

2017-11-01

This research develops an make or buy decision model considering supplier imperfect quality. This model can be used to help companies make the right decision in case of make or buy component with the best quality and the least cost in multistage manufacturing process. The imperfect quality is one of the cost component that must be minimizing in this model. Component with imperfect quality, not necessarily defective. It still can be rework and used for assembly. This research also provide a numerical example and sensitivity analysis to show how the model work. We use simulation and help by crystal ball to solve the numerical problem. The sensitivity analysis result show that percentage of imperfect generally not affect to the model significantly, and the model is not sensitive to changes in these parameters. This is because the imperfect cost are smaller than overall total cost components.

A Decision-Making Model of Social Shopping in Franchising: Assessing Collaboration Strategies

OpenAIRE

In Lee; Choong-Kwon Lee; Sangjin Yoo; Moo-Jin Choi

2015-01-01

Our paper develops a decision-making model of social shopping in franchising to understand impacts of various collaboration strategies on profits of a social intermediary, a franchisor, and a franchisee. Three decision variables are considered to make a daily deal promotion in a manner that results in optimal profits: the social intermediary's advertising expense, the franchisee's service quality expense, and the franchisor's financial assistance to the franchisee. The analysis shows that whi...

Decision Making under Uncertainty: A Neural Model based on Partially Observable Markov Decision Processes

Directory of Open Access Journals (Sweden)

Rajesh P N Rao

2010-11-01

Full Text Available A fundamental problem faced by animals is learning to select actions based on noisy sensory information and incomplete knowledge of the world. It has been suggested that the brain engages in Bayesian inference during perception but how such probabilistic representations are used to select actions has remained unclear. Here we propose a neural model of action selection and decision making based on the theory of partially observable Markov decision processes (POMDPs. Actions are selected based not on a single optimal estimate of state but on the posterior distribution over states (the belief state. We show how such a model provides a unified framework for explaining experimental results in decision making that involve both information gathering and overt actions. The model utilizes temporal difference (TD learning for maximizing expected reward. The resulting neural architecture posits an active role for the neocortex in belief computation while ascribing a role to the basal ganglia in belief representation, value computation, and action selection. When applied to the random dots motion discrimination task, model neurons representing belief exhibit responses similar to those of LIP neurons in primate neocortex. The appropriate threshold for switching from information gathering to overt actions emerges naturally during reward maximization. Additionally, the time course of reward prediction error in the model shares similarities with dopaminergic responses in the basal ganglia during the random dots task. For tasks with a deadline, the model learns a decision making strategy that changes with elapsed time, predicting a collapsing decision threshold consistent with some experimental studies. The model provides a new framework for understanding neural decision making and suggests an important role for interactions between the neocortex and the basal ganglia in learning the mapping between probabilistic sensory representations and actions that maximize

A Cercla-Based Decision Model to Support Remedy Selection for an Uncertain Volume of Contaminants at a DOE Facility

Energy Technology Data Exchange (ETDEWEB)

Christine E. Kerschus

1999-03-31

The Paducah Gaseous Diffusion Plant (PGDP) operated by the Department of Energy is challenged with selecting the appropriate remediation technology to cleanup contaminants at Waste Area Group (WAG) 6. This research utilizes value-focused thinking and multiattribute preference theory concepts to produce a decision analysis model designed to aid the decision makers in their selection process. The model is based on CERCLA's five primary balancing criteria, tailored specifically to WAG 6 and the contaminants of concern, utilizes expert opinion and the best available engineering, cost, and performance data, and accounts for uncertainty in contaminant volume. The model ranks 23 remediation technologies (trains) in their ability to achieve the CERCLA criteria at various contaminant volumes. A sensitivity analysis is performed to examine the effects of changes in expert opinion and uncertainty in volume. Further analysis reveals how volume uncertainty is expected to affect technology cost, time and ability to meet the CERCLA criteria. The model provides the decision makers with a CERCLA-based decision analysis methodology that is objective, traceable, and robust to support the WAG 6 Feasibility Study. In addition, the model can be adjusted to address other DOE contaminated sites.

A Cercla-Based Decision Model to Support Remedy Selection for an Uncertain Volume of Contaminants at a DOE Facility

International Nuclear Information System (INIS)

Christine E. Kerschus

1999-01-01

The Paducah Gaseous Diffusion Plant (PGDP) operated by the Department of Energy is challenged with selecting the appropriate remediation technology to cleanup contaminants at Waste Area Group (WAG) 6. This research utilizes value-focused thinking and multiattribute preference theory concepts to produce a decision analysis model designed to aid the decision makers in their selection process. The model is based on CERCLA's five primary balancing criteria, tailored specifically to WAG 6 and the contaminants of concern, utilizes expert opinion and the best available engineering, cost, and performance data, and accounts for uncertainty in contaminant volume. The model ranks 23 remediation technologies (trains) in their ability to achieve the CERCLA criteria at various contaminant volumes. A sensitivity analysis is performed to examine the effects of changes in expert opinion and uncertainty in volume. Further analysis reveals how volume uncertainty is expected to affect technology cost, time and ability to meet the CERCLA criteria. The model provides the decision makers with a CERCLA-based decision analysis methodology that is objective, traceable, and robust to support the WAG 6 Feasibility Study. In addition, the model can be adjusted to address other DOE contaminated sites

Decisions, decisions: analysis of age, cohort, and time of testing on framing of risky decision options.

Science.gov (United States)

Mayhorn, Christopher B; Fisk, Arthur D; Whittle, Justin D

2002-01-01

Decision making in uncertain environments is a daily challenge faced by adults of all ages. Framing decision options as either gains or losses is a common method of altering decision-making behavior. In the experiment reported here, benchmark decision-making data collected in the 1970s by Tversky and Kahneman (1981, 1988) were compared with data collected from current samples of young and older adults to determine whether behavior was consistent across time. Although differences did emerge between the benchmark and the present samples, the effect of framing on decision behavior was relatively stable. The present findings suggest that adults of all ages are susceptible to framing effects. Results also indicated that apparent age differences might be better explained by an analysis of cohort and time-of-testing effects. Actual or potential applications of this research include an understanding of how framing might influence the decision-making behavior of people of all ages in a number of applied contexts, such as product warning interactions and medical decision scenarios.

ANALYSIS AND COMPARISON OF EXISTING DECISION SUPPORT TECHNOLOGY

Directory of Open Access Journals (Sweden)

V. A. Rybak

2016-01-01

Full Text Available The article presents the results of an analytical review and comparison of the most common managerial decision support technologies: the analytic hierarchy method, neural networks, fuzzy set theory, genetic algorithms and neural-fuzzy modeling. The advantages and disadvantages of these approaches are shown. Determine the scope of their application. It is shown that the hierarchy analysis method works well with the full initial information, but due to the need for expert comparison of alternatives and the selection of evaluation criteria has a high proportion of subjectivity. For problems in the conditions of risk and uncertainty prediction seems reasonable use of the theory of fuzzy sets and neural networks. It is also considered technology collective decision applied both in the general election, and the group of experts. It reduces the time for conciliation meetings to reach a consensus by the preliminary analysis of all views submitted for the plane in the form of points. At the same time the consistency of opinion is determined by the distance between them.

Children's Lead Exposure: A Multimedia Modeling Analysis to Guide Public Health Decision-Making.

Science.gov (United States)

Zartarian, Valerie; Xue, Jianping; Tornero-Velez, Rogelio; Brown, James

2017-09-12

Drinking water and other sources for lead are the subject of public health concerns around the Flint, Michigan, drinking water and East Chicago, Indiana, lead in soil crises. In 2015, the U.S. Environmental Protection Agency (EPA)'s National Drinking Water Advisory Council (NDWAC) recommended establishment of a "health-based, household action level" for lead in drinking water based on children's exposure. The primary objective was to develop a coupled exposure-dose modeling approach that can be used to determine what drinking water lead concentrations keep children's blood lead levels (BLLs) below specified values, considering exposures from water, soil, dust, food, and air. Related objectives were to evaluate the coupled model estimates using real-world blood lead data, to quantify relative contributions by the various media, and to identify key model inputs. A modeling approach using the EPA's Stochastic Human Exposure and Dose Simulation (SHEDS)-Multimedia and Integrated Exposure Uptake and Biokinetic (IEUBK) models was developed using available data. This analysis for the U.S. population of young children probabilistically simulated multimedia exposures and estimated relative contributions of media to BLLs across all population percentiles for several age groups. Modeled BLLs compared well with nationally representative BLLs (0-23% relative error). Analyses revealed relative importance of soil and dust ingestion exposure pathways and associated Pb intake rates; water ingestion was also a main pathway, especially for infants. This methodology advances scientific understanding of the relationship between lead concentrations in drinking water and BLLs in children. It can guide national health-based benchmarks for lead and related community public health decisions. https://doi.org/10.1289/EHP1605.

A prediction rule for the development of delirium among patients in medical wards: Chi-Square Automatic Interaction Detector (CHAID) decision tree analysis model.

Science.gov (United States)

Kobayashi, Daiki; Takahashi, Osamu; Arioka, Hiroko; Koga, Shinichiro; Fukui, Tsuguya

2013-10-01

To predict development of delirium among patients in medical wards by a Chi-Square Automatic Interaction Detector (CHAID) decision tree model. This was a retrospective cohort study of all adult patients admitted to medical wards at a large community hospital. The subject patients were randomly assigned to either a derivation or validation group (2:1) by computed random number generation. Baseline data and clinically relevant factors were collected from the electronic chart. Primary outcome was the development of delirium during hospitalization. All potential predictors were included in a forward stepwise logistic regression model. CHAID decision tree analysis was also performed to make another prediction model with the same group of patients. Receiver operating characteristic curves were drawn, and the area under the curves (AUCs) were calculated for both models. In the validation group, these receiver operating characteristic curves and AUCs were calculated based on the rules from derivation. A total of 3,570 patients were admitted: 2,400 patients assigned to the derivation group and 1,170 to the validation group. A total of 91 and 51 patients, respectively, developed delirium. Statistically significant predictors were delirium history, age, underlying malignancy, and activities of daily living impairment in CHAID decision tree model, resulting in six distinctive groups by the level of risk. AUC was 0.82 in derivation and 0.82 in validation with CHAID model and 0.78 in derivation and 0.79 in validation with logistic model. We propose a validated CHAID decision tree prediction model to predict the development of delirium among medical patients. Copyright © 2013 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.

Decision modeling and acceptance criteria

DEFF Research Database (Denmark)

Ditlevsen, Ove Dalager

2003-01-01

compensation value of a human life and a public money equivalent of a human life, where the last value usually is considerably larger than the first value, it is possible from the decision analysis to determine an upper limit that the public should impose on the ratio of the owner´s expected loss rate......) that combines wealth in terms of Gross Domestic Product per person, life expectancy at birth, and yearly work time into a single number. The philosophy behind the published evaluations is that the prevention of a loss of a life is counteracted by a cost such that the LQI remains unchanged (Skjong R, Ronold K......; Decision Analysis; Life quality index; Random interest rate; Risk aversion; Socio-economic value; Uncertainty aversion...

Using Decision Analysis to Understand the Indications for Unilateral Hand Transplantation

Science.gov (United States)

McClelland, Brett; Novak, Christine B.; Hanna, Steven; McCabe, Steven J.

2016-01-01

Background: Upper extremity transplantation has been performed to improve quality of life, the benefit which must be traded off for the risk created by life-long immunosuppression. We believe the process of decision analysis is well suited to improve our understanding of these trade-offs. Method: We created a decision tree to include a branch point to illustrate the expected recovery of useful function in the transplant, using the best estimates for utility and probability that exist. Results: Our model revealed that when the probability of achieving a good result, graded as Chen level one or two is greater than 73%, transplantation is preferred over no transplantation. The decision is sensitive to the probability of major complications and the utility of a transplanted limb with minimal function. Conclusions: The results of this analysis show that under some circumstances given a high probability of satisfactory functional recovery, unilateral hand transplantation can be justified. PMID:28149213

Effects of Methadone Maintenance Treatment on Decision-Making Processes in Heroin-Abusers: A Cognitive Modeling Analysis

Directory of Open Access Journals (Sweden)

Arash Khodadadi

2010-05-01

Full Text Available A B S T R A C TIntroduction: Although decision-making processes have become a principal target of study among addiction researchers, few researches are published according to effects of different treatment methods on the cognitive processes underlying decision making up to now. Utilizing cognitive modeling method, in this paper we examine the effects of Methadone maintenance treatment (MMT on cognitive processes underlying decision-making disorders in heroin-abusers. Methods: For this purpose, for the first time, we use the balloon analog risk task (BART to assess the decision-making ability of heroin-abusers before and after treatment and compare it to the non heroin-dependent subjects. Results: Results demonstrate that heroin-abusers show more risky behavior than other groups. But, there is no difference between the performance of heroin-abusers after 6 months of MMT and control group. Modeling subjects’ behavior in BART reveals that poor performance in heroin-abusers is due to reward-dependency and insensitivity to evaluation. Discussion: Results show that 6 months of MMT decreases reward-dependency and increases sensitivity to evaluation.

Analysis of Pedestrian Gap Acceptance and Crossing Decision in Kuala Lumpur

Directory of Open Access Journals (Sweden)

Mohamad Nor Siti Naquiyah

2017-01-01

Full Text Available Pedestrians are most vulnerable of all road users. This research aims to investigate and model pedestrian road crossing behaviour at crossing facilities. In particular, they have two aspects of pedestrians crossing behaviour are examined, namely the size of traffic gaps acceptance by pedestrians and the decision of pedestrians either to cross the road or not. A fields survey was carried out at six crossing facilities which from a zebra crossing at midblock. In this survey, the data were recorded in real traffic condition using video recorder. Determine the associations between characteristics of pedestrians, crossing facilities and vehicular traffic through on-site observations of pedestrian behaviour. This data will analysis using statistical analysis which is multiple regression and binary logit regression method. It is hope that through this research, the model of pedestrian gap acceptance and pedestrian crossing decision can be reached and what are the indicators that pedestrians look for when accepting gaps to cross the road.

Cost utility analysis of endoscopic biliary stent in unresectable hilar cholangiocarcinoma: decision analytic modeling approach.

Science.gov (United States)

Sangchan, Apichat; Chaiyakunapruk, Nathorn; Supakankunti, Siripen; Pugkhem, Ake; Mairiang, Pisaln

2014-01-01

Endoscopic biliary drainage using metal and plastic stent in unresectable hilar cholangiocarcinoma (HCA) is widely used but little is known about their cost-effectiveness. This study evaluated the cost-utility of endoscopic metal and plastic stent drainage in unresectable complex, Bismuth type II-IV, HCA patients. Decision analytic model, Markov model, was used to evaluate cost and quality-adjusted life year (QALY) of endoscopic biliary drainage in unresectable HCA. Costs of treatment and utilities of each Markov state were retrieved from hospital charges and unresectable HCA patients from tertiary care hospital in Thailand, respectively. Transition probabilities were derived from international literature. Base case analyses and sensitivity analyses were performed. Under the base-case analysis, metal stent is more effective but more expensive than plastic stent. An incremental cost per additional QALY gained is 192,650 baht (US$ 6,318). From probabilistic sensitivity analysis, at the willingness to pay threshold of one and three times GDP per capita or 158,000 baht (US$ 5,182) and 474,000 baht (US$ 15,546), the probability of metal stent being cost-effective is 26.4% and 99.8%, respectively. Based on the WHO recommendation regarding the cost-effectiveness threshold criteria, endoscopic metal stent drainage is cost-effective compared to plastic stent in unresectable complex HCA.

Enabling Real-time Water Decision Support Services Using Model as a Service

Science.gov (United States)

Zhao, T.; Minsker, B. S.; Lee, J. S.; Salas, F. R.; Maidment, D. R.; David, C. H.

2014-12-01

Through application of computational methods and an integrated information system, data and river modeling services can help researchers and decision makers more rapidly understand river conditions under alternative scenarios. To enable this capability, workflows (i.e., analysis and model steps) are created and published as Web services delivered through an internet browser, including model inputs, a published workflow service, and visualized outputs. The RAPID model, which is a river routing model developed at University of Texas Austin for parallel computation of river discharge, has been implemented as a workflow and published as a Web application. This allows non-technical users to remotely execute the model and visualize results as a service through a simple Web interface. The model service and Web application has been prototyped in the San Antonio and Guadalupe River Basin in Texas, with input from university and agency partners. In the future, optimization model workflows will be developed to link with the RAPID model workflow to provide real-time water allocation decision support services.

Ignorance- versus evidence-based decision making: a decision time analysis of the recognition heuristic.

Science.gov (United States)

Hilbig, Benjamin E; Pohl, Rüdiger F

2009-09-01

According to part of the adaptive toolbox notion of decision making known as the recognition heuristic (RH), the decision process in comparative judgments-and its duration-is determined by whether recognition discriminates between objects. By contrast, some recently proposed alternative models predict that choices largely depend on the amount of evidence speaking for each of the objects and that decision times thus depend on the evidential difference between objects, or the degree of conflict between options. This article presents 3 experiments that tested predictions derived from the RH against those from alternative models. All experiments used naturally recognized objects without teaching participants any information and thus provided optimal conditions for application of the RH. However, results supported the alternative, evidence-based models and often conflicted with the RH. Recognition was not the key determinant of decision times, whereas differences between objects with respect to (both positive and negative) evidence predicted effects well. In sum, alternative models that allow for the integration of different pieces of information may well provide a better account of comparative judgments. (c) 2009 APA, all rights reserved.

Regulator Loss Functions and Hierarchical Modeling for Safety Decision Making.

Science.gov (United States)

Hatfield, Laura A; Baugh, Christine M; Azzone, Vanessa; Normand, Sharon-Lise T

2017-07-01

Regulators must act to protect the public when evidence indicates safety problems with medical devices. This requires complex tradeoffs among risks and benefits, which conventional safety surveillance methods do not incorporate. To combine explicit regulator loss functions with statistical evidence on medical device safety signals to improve decision making. In the Hospital Cost and Utilization Project National Inpatient Sample, we select pediatric inpatient admissions and identify adverse medical device events (AMDEs). We fit hierarchical Bayesian models to the annual hospital-level AMDE rates, accounting for patient and hospital characteristics. These models produce expected AMDE rates (a safety target), against which we compare the observed rates in a test year to compute a safety signal. We specify a set of loss functions that quantify the costs and benefits of each action as a function of the safety signal. We integrate the loss functions over the posterior distribution of the safety signal to obtain the posterior (Bayes) risk; the preferred action has the smallest Bayes risk. Using simulation and an analysis of AMDE data, we compare our minimum-risk decisions to a conventional Z score approach for classifying safety signals. The 2 rules produced different actions for nearly half of hospitals (45%). In the simulation, decisions that minimize Bayes risk outperform Z score-based decisions, even when the loss functions or hierarchical models are misspecified. Our method is sensitive to the choice of loss functions; eliciting quantitative inputs to the loss functions from regulators is challenging. A decision-theoretic approach to acting on safety signals is potentially promising but requires careful specification of loss functions in consultation with subject matter experts.

Application of decision tree model for the ground subsidence hazard mapping near abandoned underground coal mines.

Science.gov (United States)

Lee, Saro; Park, Inhye

2013-09-30

Subsidence of ground caused by underground mines poses hazards to human life and property. This study analyzed the hazard to ground subsidence using factors that can affect ground subsidence and a decision tree approach in a geographic information system (GIS). The study area was Taebaek, Gangwon-do, Korea, where many abandoned underground coal mines exist. Spatial data, topography, geology, and various ground-engineering data for the subsidence area were collected and compiled in a database for mapping ground-subsidence hazard (GSH). The subsidence area was randomly split 50/50 for training and validation of the models. A data-mining classification technique was applied to the GSH mapping, and decision trees were constructed using the chi-squared automatic interaction detector (CHAID) and the quick, unbiased, and efficient statistical tree (QUEST) algorithms. The frequency ratio model was also applied to the GSH mapping for comparing with probabilistic model. The resulting GSH maps were validated using area-under-the-curve (AUC) analysis with the subsidence area data that had not been used for training the model. The highest accuracy was achieved by the decision tree model using CHAID algorithm (94.01%) comparing with QUEST algorithms (90.37%) and frequency ratio model (86.70%). These accuracies are higher than previously reported results for decision tree. Decision tree methods can therefore be used efficiently for GSH analysis and might be widely used for prediction of various spatial events. Copyright © 2013. Published by Elsevier Ltd.

Microsimulation Modeling for Health Decision Sciences Using R: A Tutorial.

Science.gov (United States)

Krijkamp, Eline M; Alarid-Escudero, Fernando; Enns, Eva A; Jalal, Hawre J; Hunink, M G Myriam; Pechlivanoglou, Petros

2018-04-01

Microsimulation models are becoming increasingly common in the field of decision modeling for health. Because microsimulation models are computationally more demanding than traditional Markov cohort models, the use of computer programming languages in their development has become more common. R is a programming language that has gained recognition within the field of decision modeling. It has the capacity to perform microsimulation models more efficiently than software commonly used for decision modeling, incorporate statistical analyses within decision models, and produce more transparent models and reproducible results. However, no clear guidance for the implementation of microsimulation models in R exists. In this tutorial, we provide a step-by-step guide to build microsimulation models in R and illustrate the use of this guide on a simple, but transferable, hypothetical decision problem. We guide the reader through the necessary steps and provide generic R code that is flexible and can be adapted for other models. We also show how this code can be extended to address more complex model structures and provide an efficient microsimulation approach that relies on vectorization solutions.

PRAGMATIST MODEL OF DECISION-MAKING IN LAW: FROM THE INSTRUMENTAL MENTALISM TO THE COMMUNICATIVE INTERSUBJECTIVITY

Directory of Open Access Journals (Sweden)

Mário Cesar da Silva Andrade

2015-12-01

Full Text Available This paper aimed to evaluate the method of making rational decision derived from the philosophy of Kant as a foundation paradigma of public decisions and, more specifically, of legal decisions. Based on the communicative action theory of Jürgen Habermas, the question is  if  the  transcendental  model  of  decision-making  meets  the  democratic  demands. Methodologically, the qualitative research was based on doctrinal sources about the theme, promoting a legal and critical analysis. Habermas' communicative bias raises the hypothesis that Kant's transcendental method, which influenced so much the theory of justice and Law, entails the adoption of an objective posture by the decision maker, something incompatible with the need for broad participation and the intersubjectivity prescribed by democracy . It was concluded that the public decision-making process must overcome the transcendental, decisionistic  and  instrumental  models,  adopting  pragmatic  model,  which  is  more intersubjective and communicative, therefore more consistente with the participatory bias of democracy.

Fuzzy rationality and parameter elicitation in decision analysis

Science.gov (United States)

Nikolova, Natalia D.; Tenekedjiev, Kiril I.

2010-07-01

It is widely recognised by decision analysts that real decision-makers always make estimates in an interval form. An overview of techniques to find an optimal alternative among such with imprecise and interval probabilities is presented. Scalarisation methods are outlined as most appropriate. A proper continuation of such techniques is fuzzy rational (FR) decision analysis. A detailed representation of the elicitation process influenced by fuzzy rationality is given. The interval character of probabilities leads to the introduction of ribbon functions, whose general form and special cases are compared with the p-boxes. As demonstrated, approximation of utilities in FR decision analysis does not depend on the probabilities, but the approximation of probabilities is dependent on preferences.

Systematic approaches to data analysis from the Critical Decision Method

Directory of Open Access Journals (Sweden)

Martin Sedlár

2015-01-01

Full Text Available The aim of the present paper is to introduce how to analyse the qualitative data from the Critical Decision Method. At first, characterizing the method provides the meaningful introduction into the issue. This method used in naturalistic decision making research is one of the cognitive task analysis methods, it is based on the retrospective semistructured interview about critical incident from the work and it may be applied in various domains such as emergency services, military, transport, sport or industry. Researchers can make two types of methodological adaptation. Within-method adaptations modify the way of conducting the interviews and cross-method adaptations combine this method with other related methods. There are many decsriptions of conducting the interview, but the descriptions how the data should be analysed are rare. Some researchers use conventional approaches like content analysis, grounded theory or individual procedures with reference to the objectives of research project. Wong (2004 describes two approaches to data analysis proposed for this method of data collection, which are described and reviewed in the details. They enable systematic work with a large amount of data. The structured approach organizes the data according to an a priori analysis framework and it is suitable for clearly defined object of research. Each incident is studied separately. At first, the decision chart showing the main decision points and then the incident summary are made. These decision points are used to identify the relevant statements from the transcript, which are analysed in terms of the Recognition-Primed Decision Model. Finally, the results from all the analysed incidents are integrated. The limitation of the structured approach is it may not reveal some interesting concepts. The emergent themes approach helps to identify these concepts while maintaining a systematic framework for analysis and it is used for exploratory research design. It

Integrating Design Decision Management with Model-based Software Development

DEFF Research Database (Denmark)

Könemann, Patrick

Design decisions are continuously made during the development of software systems and are important artifacts for design documentation. Dedicated decision management systems are often used to capture such design knowledge. Most such systems are, however, separated from the design artifacts...... of the system. In model-based software development, where design models are used to develop a software system, outcomes of many design decisions have big impact on design models. The realization of design decisions is often manual and tedious work on design models. Moreover, keeping design models consistent......, or by ignoring the causes. This substitutes manual reviews to some extent. The concepts, implemented in a tool, have been validated with design patterns, refactorings, and domain level tests that comprise a replay of a real project. This proves the applicability of the solution to realistic examples...

Decision-making in healthcare: a practical application of partial least square path modelling to coverage of newborn screening programmes.

Science.gov (United States)

Fischer, Katharina E

2012-08-02

Decision-making in healthcare is complex. Research on coverage decision-making has focused on comparative studies for several countries, statistical analyses for single decision-makers, the decision outcome and appraisal criteria. Accounting for decision processes extends the complexity, as they are multidimensional and process elements need to be regarded as latent constructs (composites) that are not observed directly. The objective of this study was to present a practical application of partial least square path modelling (PLS-PM) to evaluate how it offers a method for empirical analysis of decision-making in healthcare. Empirical approaches that applied PLS-PM to decision-making in healthcare were identified through a systematic literature search. PLS-PM was used as an estimation technique for a structural equation model that specified hypotheses between the components of decision processes and the reasonableness of decision-making in terms of medical, economic and other ethical criteria. The model was estimated for a sample of 55 coverage decisions on the extension of newborn screening programmes in Europe. Results were evaluated by standard reliability and validity measures for PLS-PM. After modification by dropping two indicators that showed poor measures in the measurement models' quality assessment and were not meaningful for newborn screening, the structural equation model estimation produced plausible results. The presence of three influences was supported: the links between both stakeholder participation or transparency and the reasonableness of decision-making; and the effect of transparency on the degree of scientific rigour of assessment. Reliable and valid measurement models were obtained to describe the composites of 'transparency', 'participation', 'scientific rigour' and 'reasonableness'. The structural equation model was among the first applications of PLS-PM to coverage decision-making. It allowed testing of hypotheses in situations where there

Decision Support Model for Optimal Management of Coastal Gate

Science.gov (United States)

Ditthakit, Pakorn; Chittaladakorn, Suwatana

2010-05-01

The coastal areas are intensely settled by human beings owing to their fertility of natural resources. However, at present those areas are facing with water scarcity problems: inadequate water and poor water quality as a result of saltwater intrusion and inappropriate land-use management. To solve these problems, several measures have been exploited. The coastal gate construction is a structural measure widely performed in several countries. This manner requires the plan for suitably operating coastal gates. Coastal gate operation is a complicated task and usually concerns with the management of multiple purposes, which are generally conflicted one another. This paper delineates the methodology and used theories for developing decision support modeling for coastal gate operation scheduling. The developed model was based on coupling simulation and optimization model. The weighting optimization technique based on Differential Evolution (DE) was selected herein for solving multiple objective problems. The hydrodynamic and water quality models were repeatedly invoked during searching the optimal gate operations. In addition, two forecasting models:- Auto Regressive model (AR model) and Harmonic Analysis model (HA model) were applied for forecasting water levels and tide levels, respectively. To demonstrate the applicability of the developed model, it was applied to plan the operations for hypothetical system of Pak Phanang coastal gate system, located in Nakhon Si Thammarat province, southern part of Thailand. It was found that the proposed model could satisfyingly assist decision-makers for operating coastal gates under various environmental, ecological and hydraulic conditions.

A multicriteria decision making model for assessment and selection of an ERP in a logistics context

Science.gov (United States)

Pereira, Teresa; Ferreira, Fernanda A.

2017-07-01

The aim of this work is to apply a methodology of decision support based on a multicriteria decision analyses (MCDA) model that allows the assessment and selection of an Enterprise Resource Planning (ERP) in a Portuguese logistics company by Group Decision Maker (GDM). A Decision Support system (DSS) that implements a MCDA - Multicriteria Methodology for the Assessment and Selection of Information Systems / Information Technologies (MMASSI / IT) is used based on its features and facility to change and adapt the model to a given scope. Using this DSS it was obtained the information system that best suited to the decisional context, being this result evaluated through a sensitivity and robustness analysis.

Decision analysis and rational countermeasures in radiation protection

International Nuclear Information System (INIS)

Sinkko, K.

1991-09-01

During the past few years several international organizations (ICRP, IAEA, OECD/NEA), in revising their radiation protection principles, have emphasized the importance of the rationalization and planning of intervention after a nuclear accident. An accident itself and the introduction of protective action entails risks to the people affected, monetary costs and social disruption. Thus protective actions, often including objectives which are difficult to control simultaneously, cannot be undertaken without careful contemplation and consideration of the essential consequences of decisions. Often during an accident there is not enough time for careful consideration. Decision analysis is an analyzing and thought guiding method for the definition of objectives and comparison of options. It is an appropriate methodology assisting in rendering explicit and apparent all factors involved and evaluating their relative importance. The planning of intervention with the help of decision analysis is portion of the preparation for accident situations. In this report one of the techniques of decision analysis, multi-attribute utility analysis, is presented, as concerns its application in planning protective actions in the event of radiation accidents. (orig.)

Ranking environmental projects model based on multicriteria decision-making and the weight sensitivity analysis

Institute of Scientific and Technical Information of China (English)

无

2007-01-01

With the fast growth of Chinese economic,more and more capital will be invested in environmental projects.How to select the environmental investment projects(alternatives)for obtaining the best environmental quality and economic benefits is an important problem for the decision makers.The purpose of this paper is to develop a decision-making model to rank a finite number of alternatives with several and sometimes conflicting criteria.A model for ranking the projects of municipal sewage treatment plants is proposed by using exports' information and the data of the real projects.And,the ranking result is given based on the PROMETHEE method. Furthermore,by means of the concept of the weight stability intervals(WSI),the sensitivity of the ranking results to the size of criteria values and the change of weights value of criteria are discussed.The result shows that some criteria,such as"proportion of benefit to projoct cost",will influence the ranking result of alternatives very strong while others not.The influence are not only from the value of criterion but also from the changing the weight of criterion.So,some criteria such as"proportion of benefit to projoct cost" are key critera for ranking the projects. Decision makers must be cautious to them.

A new fit-for-purpose model testing framework: Decision Crash Tests

Science.gov (United States)

Tolson, Bryan; Craig, James

2016-04-01

Decision-makers in water resources are often burdened with selecting appropriate multi-million dollar strategies to mitigate the impacts of climate or land use change. Unfortunately, the suitability of existing hydrologic simulation models to accurately inform decision-making is in doubt because the testing procedures used to evaluate model utility (i.e., model validation) are insufficient. For example, many authors have identified that a good standard framework for model testing called the Klemes Crash Tests (KCTs), which are the classic model validation procedures from Klemeš (1986) that Andréassian et al. (2009) rename as KCTs, have yet to become common practice in hydrology. Furthermore, Andréassian et al. (2009) claim that the progression of hydrological science requires widespread use of KCT and the development of new crash tests. Existing simulation (not forecasting) model testing procedures such as KCTs look backwards (checking for consistency between simulations and past observations) rather than forwards (explicitly assessing if the model is likely to support future decisions). We propose a fundamentally different, forward-looking, decision-oriented hydrologic model testing framework based upon the concept of fit-for-purpose model testing that we call Decision Crash Tests or DCTs. Key DCT elements are i) the model purpose (i.e., decision the model is meant to support) must be identified so that model outputs can be mapped to management decisions ii) the framework evaluates not just the selected hydrologic model but the entire suite of model-building decisions associated with model discretization, calibration etc. The framework is constructed to directly and quantitatively evaluate model suitability. The DCT framework is applied to a model building case study on the Grand River in Ontario, Canada. A hypothetical binary decision scenario is analysed (upgrade or not upgrade the existing flood control structure) under two different sets of model building

Ethical analysis to improve decision-making on health technologies.

Science.gov (United States)

Saarni, Samuli I; Hofmann, Bjørn; Lampe, Kristian; Lühmann, Dagmar; Mäkelä, Marjukka; Velasco-Garrido, Marcial; Autti-Rämö, Ilona

2008-08-01

Health technology assessment (HTA) is the multidisciplinary study of the implications of the development, diffusion and use of health technologies. It supports health-policy decisions by providing a joint knowledge base for decision-makers. To increase its policy relevance, HTA tries to extend beyond effectiveness and costs to also considering the social, organizational and ethical implications of technologies. However, a commonly accepted method for analysing the ethical aspects of health technologies is lacking. This paper describes a model for ethical analysis of health technology that is easy and flexible to use in different organizational settings and cultures. The model is part of the EUnetHTA project, which focuses on the transferability of HTAs between countries. The EUnetHTA ethics model is based on the insight that the whole HTA process is value laden. It is not sufficient to only analyse the ethical consequences of a technology, but also the ethical issues of the whole HTA process must be considered. Selection of assessment topics, methods and outcomes is essentially a value-laden decision. Health technologies may challenge moral or cultural values and beliefs, and their implementation may also have significant impact on people other than the patient. These are essential considerations for health policy. The ethics model is structured around key ethical questions rather than philosophical theories, to be applicable to different cultures and usable by non-philosophers. Integrating ethical considerations into HTA can improve the relevance of technology assessments for health care and health policy in both developed and developing countries.

A decision-making model for engineering designers

DEFF Research Database (Denmark)

Ahmed, S.; Hansen, Claus Thorp

2002-01-01

This paper describes research that combines the generic decision-making model of Hansen, together with design strategies employed by experienced engineering designers. The relationship between the six decision-making sub-activities and the eight design strategies are examined. By combining...

Solid Waste Management Holistic Decision Modeling

OpenAIRE

World Bank

2008-01-01

This study provides support to the Bank's ability to conduct client dialogue on solid waste management technology selection, and will contribute to client decision-making. The goal of the study was to fully explore the use of the United States Environmental Protection Agency and the Research Triangle Institute (EPA/RTI) holistic decision model to study alternative solid waste systems in a ...

Decision Making Analysis: Critical Factors-Based Methodology

Science.gov (United States)

2010-04-01

the pitfalls associated with current wargaming methods such as assuming a western view of rational values in decision - making regardless of the cultures...Utilization theory slightly expands the rational decision making model as it states that “actors try to maximize their expected utility by weighing the...items to categorize the decision - making behavior of political leaders which tend to demonstrate either a rational or cognitive leaning. Leaders

Multivariate analysis: models and method

International Nuclear Information System (INIS)

Sanz Perucha, J.

1990-01-01

Data treatment techniques are increasingly used since computer methods result of wider access. Multivariate analysis consists of a group of statistic methods that are applied to study objects or samples characterized by multiple values. A final goal is decision making. The paper describes the models and methods of multivariate analysis

A Geospatial Cyberinfrastructure for Urban Economic Analysis and Spatial Decision-Making

Directory of Open Access Journals (Sweden)

Michael F. Goodchild

2013-05-01

Full Text Available Urban economic modeling and effective spatial planning are critical tools towards achieving urban sustainability. However, in practice, many technical obstacles, such as information islands, poor documentation of data and lack of software platforms to facilitate virtual collaboration, are challenging the effectiveness of decision-making processes. In this paper, we report on our efforts to design and develop a geospatial cyberinfrastructure (GCI for urban economic analysis and simulation. This GCI provides an operational graphic user interface, built upon a service-oriented architecture to allow (1 widespread sharing and seamless integration of distributed geospatial data; (2 an effective way to address the uncertainty and positional errors encountered in fusing data from diverse sources; (3 the decomposition of complex planning questions into atomic spatial analysis tasks and the generation of a web service chain to tackle such complex problems; and (4 capturing and representing provenance of geospatial data to trace its flow in the modeling task. The Greater Los Angeles Region serves as the test bed. We expect this work to contribute to effective spatial policy analysis and decision-making through the adoption of advanced GCI and to broaden the application coverage of GCI to include urban economic simulations.

Dual processing model of medical decision-making

OpenAIRE

Djulbegovic, Benjamin; Hozo, Iztok; Beckstead, Jason; Tsalatsanis, Athanasios; Pauker, Stephen G

2012-01-01

Abstract Background Dual processing theory of human cognition postulates that reasoning and decision-making can be described as a function of both an intuitive, experiential, affective system (system I) and/or an analytical, deliberative (system II) processing system. To date no formal descriptive model of medical decision-making based on dual processing theory has been developed. Here we postulate such a model and apply it to a common clinical situation: whether treatment should be administe...

Integrating technical analysis and public values in risk-based decision making

International Nuclear Information System (INIS)

Bohnenblust, Hans; Slovic, Paul

1998-01-01

Simple technical analysis cannot capture the complex scope of preferences or values of society and individuals. However, decision making needs to be sustained by formal analysis. The paper describes a policy framework which incorporates both technical analysis and aspects of public values. The framework can be used as a decision supporting tool and helps decision makers to make more informed and more transparent decisions about safety issues

Risk analysis for CHP decision making within the conditions of an open electricity market

International Nuclear Information System (INIS)

Al-Mansour, Fouad; Kozuh, Mitja

2007-01-01

Decision making under uncertainty is a difficult task in most areas. Investment decisions for combined heat and power production (CHP) are certainly one of the areas where it is difficult to find an optimal solution since the payback period is several years and parameters change due to different perturbing factors of economic and mostly political nature. CHP is one of the most effective measures for saving primary energy and reduction of greenhouse gas emissions. The implementation of EU directives on the promotion of cogeneration based on useful heat demand in the internal energy market will accelerate CHP installation. The expected number of small CHP installations will be very high in the near future. A quick, reliable and simple tool for economic evaluation of small CHP systems is required. Since evaluation is normally made by sophisticated economic computer models which are rather expensive, a simple point estimate economic model was developed which was later upgraded by risk methodology to give more informative results for better decision making. This paper presents a reliable computer model entitled 'Computer program for economic evaluation analysis of CHP' as a tool for analysis and economic evaluation of small CHP systems with the aim of helping the decision maker. The paper describes two methods for calculation of the sensitivity of the economic results to changes of input parameters and the uncertainty of the results: the classic/static method and the risk method. The computer program uses risk methodology by applying RISK software on an existing conventional economic model. The use of risk methodology for economic evaluation can improve decisions by incorporating all possible information (knowledge), which cannot be done in the conventional economic model due to its limitations. The methodology was tested on the case of a CHP used in a smaller hospital

Decision analysis for conservation breeding: Maximizing production for reintroduction of whooping cranes

Science.gov (United States)

Smith, Des H.V.; Converse, Sarah J.; Gibson, Keith; Moehrenschlager, Axel; Link, William A.; Olsen, Glenn H.; Maguire, Kelly

2011-01-01

Captive breeding is key to management of severely endangered species, but maximizing captive production can be challenging because of poor knowledge of species breeding biology and the complexity of evaluating different management options. In the face of uncertainty and complexity, decision-analytic approaches can be used to identify optimal management options for maximizing captive production. Building decision-analytic models requires iterations of model conception, data analysis, model building and evaluation, identification of remaining uncertainty, further research and monitoring to reduce uncertainty, and integration of new data into the model. We initiated such a process to maximize captive production of the whooping crane (Grus americana), the world's most endangered crane, which is managed through captive breeding and reintroduction. We collected 15 years of captive breeding data from 3 institutions and used Bayesian analysis and model selection to identify predictors of whooping crane hatching success. The strongest predictor, and that with clear management relevance, was incubation environment. The incubation period of whooping crane eggs is split across two environments: crane nests and artificial incubators. Although artificial incubators are useful for allowing breeding pairs to produce multiple clutches, our results indicate that crane incubation is most effective at promoting hatching success. Hatching probability increased the longer an egg spent in a crane nest, from 40% hatching probability for eggs receiving 1 day of crane incubation to 95% for those receiving 30 days (time incubated in each environment varied independently of total incubation period). Because birds will lay fewer eggs when they are incubating longer, a tradeoff exists between the number of clutches produced and egg hatching probability. We developed a decision-analytic model that estimated 16 to be the optimal number of days of crane incubation needed to maximize the number of

Multi-criteria decision analysis for use in transport decision making

DEFF Research Database (Denmark)

the recent years that besides the social costs and benefits associated with transport other impacts that are more difficult to monetise should also have influence on the decision making process. This is in many developed countries realised in the transport planning, which takes into account a wide range......, however, commonly agreed that the final decision making concerning transport infrastructure projects in many cases will depend on other aspects besides the monetary ones assessed in a socio-economic analysis. Nevertheless, an assessment framework such as the Danish one (DMT, 2003) does not provide any...... specific guidelines on how to include the strategic impacts; it merely suggests describing the impacts verbally and keeping them in mind during the decision process. A coherent, well-structured, flexible, straight forward evaluation method, taking into account all the requirements of a transport...

Characteristic times in the English Channel from numerical modelling: supporting decision-making

Energy Technology Data Exchange (ETDEWEB)

Perianez, R [Departamento de Fisica Aplicada 1, Universidad de Sevilla, EUITA, Carretera Utrera km 1, 41013 Sevilla (Spain); Miro, C [Departamento de Fisica Aplicada, Facultad de Veterinaria, Universidad de Extremadura, Avenida de la Universidad s/n, 10071 Caceres (Spain)], E-mail: rperianez@us.es, E-mail: cmiro@unex.es

2009-06-15

A numerical model that simulates the dispersion of radionuclides in the English Channel has been applied to study the dispersion of conservative and non-conservative radionuclides released from the La Hague nuclear fuel reprocessing plant. The model is based upon previous work and now is able to simulate dispersion over long timescales (decades), explicitly including transport by instantaneous tidal currents and variable wind conditions. Wind conditions are obtained from meteorological statistics using a stochastic method. Outputs from the model are treated using time-series analysis techniques. These techniques allow the determination of characteristic times of the system, transport velocities and dispersion factors. This information may be very useful to support the decision-making process after an emergency situation. Thus, we are proposing that time-series analysis can be integrated with numerical modelling for helping decision-making in response to an accident. The model is first validated through its application to actual releases of {sup 99}Tc and {sup 125}Sb, comparing measured and computed concentrations, and characteristic times for three radionuclides are given next: a perfectly conservative one, a very reactive one ({sup 239,240}Pu) and {sup 137}Cs, which has an intermediate behaviour. Characteristic transport velocities and dispersion factors have been calculated as well. Model results are supported by experimental evidence.

A Signal Detection Model of Compound Decision Tasks

Science.gov (United States)

2006-12-01

strict isolation (for many examples of such models see Egan, 1975; Macmillan & Creelman , 1991). The result has been twofold: A rich corpus of decision...Macmillan & Creelman , 1991). It is important to point out that SDT models are primarily decision models. They specify the rules and procedures for how...Broadbent, 1958; Macmillan & Creelman , 1991; Nolte & Jaarsma, 1967; Swensson & Judy, 1981; Tanner & Norman, 1954). To better understand how these two

Benefit-Risk Analysis for Decision-Making: An Approach.

Science.gov (United States)

Raju, G K; Gurumurthi, K; Domike, R

2016-12-01

The analysis of benefit and risk is an important aspect of decision-making throughout the drug lifecycle. In this work, the use of a benefit-risk analysis approach to support decision-making was explored. The proposed approach builds on the qualitative US Food and Drug Administration (FDA) approach to include a more explicit analysis based on international standards and guidance that enables aggregation and comparison of benefit and risk on a common basis and a lifecycle focus. The approach is demonstrated on six decisions over the lifecycle (e.g., accelerated approval, withdrawal, and traditional approval) using two case studies: natalizumab for multiple sclerosis (MS) and bedaquiline for multidrug-resistant tuberculosis (MDR-TB). © 2016 American Society for Clinical Pharmacology and Therapeutics.

Effects of stress on decisions under uncertainty: A meta-analysis.

Science.gov (United States)

Starcke, Katrin; Brand, Matthias

2016-09-01

[Correction Notice: An Erratum for this article was reported in Vol 142(9) of Psychological Bulletin (see record 2016-39486-001). It should have been reported that the inverted u-shaped relationship between cortisol stress responses and decision-making performance was only observed in female, but not in male participants as suggested by the study by van den Bos, Harteveld, and Stoop (2009). Corrected versions of the affected sentences are provided.] The purpose of the present meta-analysis was to quantify the effects that stress has on decisions made under uncertainty. We hypothesized that stress increases reward seeking and risk taking through alterations of dopamine firing rates and reduces executive control by hindering optimal prefrontal cortex functioning. In certain decision situations, increased reward seeking and risk taking is dysfunctional, whereas in others, this is not the case. We also assumed that the type of stressor plays a role. In addition, moderating variables are analyzed, such as the hormonal stress response, the time between stress onset and decisions, and the participants' age and gender. We included studies in the meta-analysis that investigated decision making after a laboratory stress-induction versus a control condition (k = 32 datasets, N = 1829 participants). A random-effects model revealed that overall, stress conditions lead to decisions that can be described as more disadvantageous, more reward seeking, and more risk taking than nonstress conditions (d = .17). In those situations in which increased reward seeking and risk taking is disadvantageous, stress had significant effects (d = .26), whereas in other situations, no effects were observed (d = .01). Effects were observed under processive stressors (d = .19), but not under systemic ones (d = .09). Moderation analyses did not reveal any significant results. We concluded that stress deteriorates overall decision-making performance through the mechanisms proposed. The effects differ

Simple model for multiple-choice collective decision making.

Science.gov (United States)

Lee, Ching Hua; Lucas, Andrew

2014-11-01

We describe a simple model of heterogeneous, interacting agents making decisions between n≥2 discrete choices. For a special class of interactions, our model is the mean field description of random field Potts-like models and is effectively solved by finding the extrema of the average energy E per agent. In these cases, by studying the propagation of decision changes via avalanches, we argue that macroscopic dynamics is well captured by a gradient flow along E. We focus on the permutation symmetric case, where all n choices are (on average) the same, and spontaneous symmetry breaking (SSB) arises purely from cooperative social interactions. As examples, we show that bimodal heterogeneity naturally provides a mechanism for the spontaneous formation of hierarchies between decisions and that SSB is a preferred instability to discontinuous phase transitions between two symmetric points. Beyond the mean field limit, exponentially many stable equilibria emerge when we place this model on a graph of finite mean degree. We conclude with speculation on decision making with persistent collective oscillations. Throughout the paper, we emphasize analogies between methods of solution to our model and common intuition from diverse areas of physics, including statistical physics and electromagnetism.

A Heuristic Model for Supporting Users’ Decision-Making in Privacy Disclosure for Recommendation

Directory of Open Access Journals (Sweden)

Hongchen Wu

2018-01-01

Full Text Available Privacy issues have become a major concern in the web of resource sharing, and users often have difficulty managing their information disclosure in the context of high-quality experiences from social media and Internet of Things. Recent studies have shown that users’ disclosure decisions may be influenced by heuristics from the crowds, leading to inconsistency in the disclosure volumes and reduction of the prediction accuracy. Therefore, an analysis of why this influence occurs and how to optimize the user experience is highly important. We propose a novel heuristic model that defines the data structures of items and participants in social media, utilizes a modified decision-tree classifier that can predict participants’ disclosures, and puts forward a correlation analysis for detecting disclosure inconsistences. The heuristic model is applied to real-time dataset to evaluate the behavioral effects. Decision-tree classifier and correlation analysis indeed prove that some participants’ behaviors in information disclosures became decreasingly correlated during item requesting. Participants can be “persuaded” to change their disclosure behaviors, and the users’ answers to the mildly sensitive items tend to be more variable and less predictable. Using this approach, recommender systems in social media can thus know the users better and provide service with higher prediction accuracy.

Functional Freedom: A Psychological Model of Freedom in Decision-Making.

Science.gov (United States)

Lau, Stephan; Hiemisch, Anette

2017-07-05

The freedom of a decision is not yet sufficiently described as a psychological variable. We present a model of functional decision freedom that aims to fill that role. The model conceptualizes functional freedom as a capacity of people that varies depending on certain conditions of a decision episode. It denotes an inner capability to consciously shape complex decisions according to one's own values and needs. Functional freedom depends on three compensatory dimensions: it is greatest when the decision-maker is highly rational, when the structure of the decision is highly underdetermined, and when the decision process is strongly based on conscious thought and reflection. We outline possible research questions, argue for psychological benefits of functional decision freedom, and explicate the model's implications on current knowledge and research. In conclusion, we show that functional freedom is a scientific variable, permitting an additional psychological foothold in research on freedom, and that is compatible with a deterministic worldview.

Bayesian risk-based decision method for model validation under uncertainty

International Nuclear Information System (INIS)

Jiang Xiaomo; Mahadevan, Sankaran

2007-01-01

This paper develops a decision-making methodology for computational model validation, considering the risk of using the current model, data support for the current model, and cost of acquiring new information to improve the model. A Bayesian decision theory-based method is developed for this purpose, using a likelihood ratio as the validation metric for model assessment. An expected risk or cost function is defined as a function of the decision costs, and the likelihood and prior of each hypothesis. The risk is minimized through correctly assigning experimental data to two decision regions based on the comparison of the likelihood ratio with a decision threshold. A Bayesian validation metric is derived based on the risk minimization criterion. Two types of validation tests are considered: pass/fail tests and system response value measurement tests. The methodology is illustrated for the validation of reliability prediction models in a tension bar and an engine blade subjected to high cycle fatigue. The proposed method can effectively integrate optimal experimental design into model validation to simultaneously reduce the cost and improve the accuracy of reliability model assessment

A decision support model for reducing electric energy consumption in elementary school facilities

International Nuclear Information System (INIS)

Hong, Taehoon; Koo, Choongwan; Jeong, Kwangbok

2012-01-01

Highlights: ► Decision support model is developed to reduce CO 2 emission in elementary schools. ► The model can select the school to be the most effective in energy savings. ► Decision tree improved the prediction accuracy by 1.83–3.88%. ► Using the model, decision-maker can save the electric-energy consumption by 16.58%. ► The model can make the educational-facility improvement program more effective. -- Abstract: The South Korean government has been actively promoting an educational-facility improvement program as part of its energy-saving efforts. This research seeks to develop a decision support model for selecting the facility expected to be effective in generating energy savings and making the facility improvement program more effective. In this research, project characteristics and electric-energy consumption data for the year 2009 were collected from 6282 elementary schools located in seven metropolitan cities in South Korea. In this research, the following were carried out: (i) a group of educational facilities was established based on electric-energy consumption, using a decision tree; (ii) a number of similar projects were retrieved from the same group of facilities, using case-based reasoning; and (iii) the accuracy of prediction was improved, using the combination of genetic algorithms, the artificial neural network, and multiple regression analysis. The results of this research can be useful for the following purposes: (i) preliminary research on the systematic and continuous management of educational facilities’ electric-energy consumption; (ii) basic research on electric-energy consumption prediction based on the project characteristics; and (iii) practical research for selecting an optimum facility that can more effectively apply an educational-facility improvement program as a decision support model.

Models and theories of prescribing decisions: A review and suggested a new model.

Science.gov (United States)

Murshid, Mohsen Ali; Mohaidin, Zurina

2017-01-01

To date, research on the prescribing decisions of physician lacks sound theoretical foundations. In fact, drug prescribing by doctors is a complex phenomenon influenced by various factors. Most of the existing studies in the area of drug prescription explain the process of decision-making by physicians via the exploratory approach rather than theoretical. Therefore, this review is an attempt to suggest a value conceptual model that explains the theoretical linkages existing between marketing efforts, patient and pharmacist and physician decision to prescribe the drugs. The paper follows an inclusive review approach and applies the previous theoretical models of prescribing behaviour to identify the relational factors. More specifically, the report identifies and uses several valuable perspectives such as the 'persuasion theory - elaboration likelihood model', the stimuli-response marketing model', the 'agency theory', the theory of planned behaviour,' and 'social power theory,' in developing an innovative conceptual paradigm. Based on the combination of existing methods and previous models, this paper suggests a new conceptual model of the physician decision-making process. This unique model has the potential for use in further research.

Verification of Decision-Analytic Models for Health Economic Evaluations: An Overview.

Science.gov (United States)

Dasbach, Erik J; Elbasha, Elamin H

2017-07-01

Decision-analytic models for cost-effectiveness analysis are developed in a variety of software packages where the accuracy of the computer code is seldom verified. Although modeling guidelines recommend using state-of-the-art quality assurance and control methods for software engineering to verify models, the fields of pharmacoeconomics and health technology assessment (HTA) have yet to establish and adopt guidance on how to verify health and economic models. The objective of this paper is to introduce to our field the variety of methods the software engineering field uses to verify that software performs as expected. We identify how many of these methods can be incorporated in the development process of decision-analytic models in order to reduce errors and increase transparency. Given the breadth of methods used in software engineering, we recommend a more in-depth initiative to be undertaken (e.g., by an ISPOR-SMDM Task Force) to define the best practices for model verification in our field and to accelerate adoption. Establishing a general guidance for verifying models will benefit the pharmacoeconomics and HTA communities by increasing accuracy of computer programming, transparency, accessibility, sharing, understandability, and trust of models.

Toward an operational model of decision making, emotional regulation, and mental health impact.

Science.gov (United States)

Collura, Thomas Francis; Zalaquett, Ronald P; Bonnstetter, Carlos Joyce; Chatters, Seria J

2014-01-01

Current brain research increasingly reveals the underlying mechanisms and processes of human behavior, cognition, and emotion. In addition to being of interest to a wide range of scientists, educators, and professionals, as well as laypeople, brain-based models are of particular value in a clinical setting. Psychiatrists, psychologists, counselors, and other mental health professionals are in need of operational models that integrate recent findings in the physical, cognitive, and emotional domains, and offer a common language for interdisciplinary understanding and communication. Based on individual traits, predispositions, and responses to stimuli, we can begin to identify emotional and behavioral pathways and mental processing patterns. The purpose of this article is to present a brain-path activation model to understand individual differences in decision making and psychopathology. The first section discusses the role of frontal lobe electroencephalography (EEG) asymmetry, summarizes state- and trait-based models of decision making, and provides a more complex analysis that supplements the traditional simple left-right brain model. Key components of the new model are the introduction of right hemisphere parallel and left hemisphere serial scanning in rendering decisions, and the proposition of pathways that incorporate both past experiences as well as future implications into the decision process. Main attributes of each decision-making mechanism are provided. The second section applies the model within the realm of clinical mental health as a tool to understand specific human behavior and pathology. Applications include general and chronic anxiety, depression, paranoia, risk taking, and the pathways employed when well-functioning operational integration is observed. Finally, specific applications such as meditation and mindfulness are offered to facilitate positive functioning.

An empirical analysis of the corporate call decision

International Nuclear Information System (INIS)

Carlson, M.D.

1998-01-01

An economic study of the the behaviour of financial managers of utility companies was presented. The study examined whether or not an option pricing based model of the call decision does a better job of explaining callable preferred share prices and call decisions compared to other models. In this study, the Rust (1987) empirical technique was extended to include the use of information from preferred share prices in addition to the call decisions. Reasonable estimates were obtained from data of shares of the Pacific Gas and Electric Company (PGE) for the transaction costs associated with a call. It was concluded that the managers of the PGE clearly take into account the value of the option to delay the call when making their call decisions

Stochastic Watershed Models for Risk Based Decision Making

Science.gov (United States)

Vogel, R. M.

2017-12-01

Over half a century ago, the Harvard Water Program introduced the field of operational or synthetic hydrology providing stochastic streamflow models (SSMs), which could generate ensembles of synthetic streamflow traces useful for hydrologic risk management. The application of SSMs, based on streamflow observations alone, revolutionized water resources planning activities, yet has fallen out of favor due, in part, to their inability to account for the now nearly ubiquitous anthropogenic influences on streamflow. This commentary advances the modern equivalent of SSMs, termed `stochastic watershed models' (SWMs) useful as input to nearly all modern risk based water resource decision making approaches. SWMs are deterministic watershed models implemented using stochastic meteorological series, model parameters and model errors, to generate ensembles of streamflow traces that represent the variability in possible future streamflows. SWMs combine deterministic watershed models, which are ideally suited to accounting for anthropogenic influences, with recent developments in uncertainty analysis and principles of stochastic simulation

From spatially variable streamflow to distributed hydrological models: Analysis of key modeling decisions

Science.gov (United States)

Fenicia, Fabrizio; Kavetski, Dmitri; Savenije, Hubert H. G.; Pfister, Laurent

2016-02-01

This paper explores the development and application of distributed hydrological models, focusing on the key decisions of how to discretize the landscape, which model structures to use in each landscape element, and how to link model parameters across multiple landscape elements. The case study considers the Attert catchment in Luxembourg—a 300 km2 mesoscale catchment with 10 nested subcatchments that exhibit clearly different streamflow dynamics. The research questions are investigated using conceptual models applied at hydrologic response unit (HRU) scales (1-4 HRUs) on 6 hourly time steps. Multiple model structures are hypothesized and implemented using the SUPERFLEX framework. Following calibration, space/time model transferability is tested using a split-sample approach, with evaluation criteria including streamflow prediction error metrics and hydrological signatures. Our results suggest that: (1) models using geology-based HRUs are more robust and capture the spatial variability of streamflow time series and signatures better than models using topography-based HRUs; this finding supports the hypothesis that, in the Attert, geology exerts a stronger control than topography on streamflow generation, (2) streamflow dynamics of different HRUs can be represented using distinct and remarkably simple model structures, which can be interpreted in terms of the perceived dominant hydrologic processes in each geology type, and (3) the same maximum root zone storage can be used across the three dominant geological units with no loss in model transferability; this finding suggests that the partitioning of water between streamflow and evaporation in the study area is largely independent of geology and can be used to improve model parsimony. The modeling methodology introduced in this study is general and can be used to advance our broader understanding and prediction of hydrological behavior, including the landscape characteristics that control hydrologic response, the

Finding the Most Preferred Decision-Making Unit in Data Envelopment Analysis

Directory of Open Access Journals (Sweden)

Shirin Mohammadi

2016-01-01

Full Text Available Data envelopment analysis (DEA evaluates the efficiency of the transformation of a decision-making unit’s (DMU’s inputs into its outputs. Finding the benchmarks of a DMU is one of the important purposes of DEA. The benchmarks of a DMU in DEA are obtained by solving some linear programming models. Currently, the obtained benchmarks are just found by using the information of the data of inputs and outputs without considering the decision-maker’s preferences. If the preferences of the decision-maker are available, it is very important to obtain the most preferred DMU as a benchmark of the under-assessment DMU. In this regard, we present an algorithm to find the most preferred DMU based on the utility function of decision-maker’s preferences by exploring some properties on that. The proposed method is constructed based on the projection of the gradient of the utility function on the production possibility set’s frontier.

What role can simulation model predictions play in environmental decisions: carbon dioxide as an example

International Nuclear Information System (INIS)

Emanuel, W.R.

1979-01-01

Frequently, when an environmental issue requiring quantitative analysis surfaces, the development of a model synthesizing all aspects of the problem and applicable at each stage of the decision process is proposed. A more desirable alternative is to generate models specifically designed to meet the requirements of each level in decision making and which can be adapted in response to the changing status of the environmental issue. Various models of the global carbon cycle constructed to predict levels of CO 2 in the atmosphere as a result of man's activities are described to illustrate this point. In summary, the progression of models developed to analyze the global carbon cycle in resolving the CO 2 /climate issue indicates the changing character of models depending on the immediate role they play in environmental decision making. The dominant and successful role served by models in the carbon cycle problem points to the desirability of this flexible approach

"Know What to Do If You Encounter a Flash Flood": Mental Models Analysis for Improving Flash Flood Risk Communication and Public Decision Making.

Science.gov (United States)

Lazrus, Heather; Morss, Rebecca E; Demuth, Julie L; Lazo, Jeffrey K; Bostrom, Ann

2016-02-01

Understanding how people view flash flood risks can help improve risk communication, ultimately improving outcomes. This article analyzes data from 26 mental models interviews about flash floods with members of the public in Boulder, Colorado, to understand their perspectives on flash flood risks and mitigation. The analysis includes a comparison between public and professional perspectives by referencing a companion mental models study of Boulder-area professionals. A mental models approach can help to diagnose what people already know about flash flood risks and responses, as well as any critical gaps in their knowledge that might be addressed through improved risk communication. A few public interviewees mentioned most of the key concepts discussed by professionals as important for flash flood warning decision making. However, most interviewees exhibited some incomplete understandings and misconceptions about aspects of flash flood development and exposure, effects, or mitigation that may lead to ineffective warning decisions when a flash flood threatens. These include important misunderstandings about the rapid evolution of flash floods, the speed of water in flash floods, the locations and times that pose the greatest flash flood risk in Boulder, the value of situational awareness and environmental cues, and the most appropriate responses when a flash flood threatens. The findings point to recommendations for ways to improve risk communication, over the long term and when an event threatens, to help people quickly recognize and understand threats, obtain needed information, and make informed decisions in complex, rapidly evolving extreme weather events such as flash floods. © 2015 Society for Risk Analysis.

Decision Making Model for Business Process Outsourcing of Enterprise Content Management

Directory of Open Access Journals (Sweden)

Zhuojun Yi

2013-03-01

Full Text Available Business process outsourcing (BPO in enterprise content management (ECM is a growing though immature market. BPO in ECM focuses on pursuing market transactions in the process of managing all types of content being used in organizations. However, inadequate sourcing decisions lead to organizational sensitive content exposure, high transaction cost, poor outsourcer performance, low flexibility. ECM BPO in general is rarely discussed in the literature and no discussion was found on decision making strategies in ECM BPO. In this paper, we present a decision making model for ECM BPO that will fill the literature gap and guide industry practitioners with ECM sourcing decision making strategies. Our proposed decision making model includes two parts. Part one is an ECM functional framework that shows what functionality component or functionality combinations can be outsourced. Part two is a decision making model that provides guidance for decision making in ECM BPO. We apply the model in two case studies, and the results indicate that the model can guide the sourcing decision making process for organizations, and determine the factors when considering sourcing alternatives in ECM.

Using decision trees and their ensembles for analysis of NIR spectroscopic data

DEFF Research Database (Denmark)

Kucheryavskiy, Sergey V.

and interpretation of the models. In this presentation, we are going to discuss an applicability of decision trees based methods (including gradient boosting) for solving classification and regression tasks with NIR spectra as predictors. We will cover such aspects as evaluation, optimization and validation......Advanced machine learning methods, like convolutional neural networks and decision trees, became extremely popular in the last decade. This, first of all, is directly related to the current boom in Big data analysis, where traditional statistical methods are not efficient. According to the kaggle.......com — the most popular online resource for Big data problems and solutions — methods based on decision trees and their ensembles are most widely used for solving the problems. It can be noted that the decision trees and convolutional neural networks are not very popular in Chemometrics. One of the reasons...

A decision model for planetary missions

Science.gov (United States)

Hazelrigg, G. A., Jr.; Brigadier, W. L.

1976-01-01

Many techniques developed for the solution of problems in economics and operations research are directly applicable to problems involving engineering trade-offs. This paper investigates the use of utility theory for decision making in planetary exploration space missions. A decision model is derived that accounts for the objectives of the mission - science - the cost of flying the mission and the risk of mission failure. A simulation methodology for obtaining the probability distribution of science value and costs as a function spacecraft and mission design is presented and an example application of the decision methodology is given for various potential alternatives in a comet Encke mission.

Assumptions and Policy Decisions for Vital Area Identification Analysis

Energy Technology Data Exchange (ETDEWEB)

Kim, Myungsu; Bae, Yeon-Kyoung; Lee, Youngseung [KHNP CRI, Daejeon (Korea, Republic of)

2016-10-15

U.S. Nuclear Regulatory Commission and IAEA guidance indicate that certain assumptions and policy questions should be addressed to a Vital Area Identification (VAI) process. Korea Hydro and Nuclear Power conducted a VAI based on current Design Basis Threat and engineering judgement to identify APR1400 vital areas. Some of the assumptions were inherited from Probabilistic Safety Assessment (PSA) as a sabotage logic model was based on PSA logic tree and equipment location data. This paper illustrates some important assumptions and policy decisions for APR1400 VAI analysis. Assumptions and policy decisions could be overlooked at the beginning stage of VAI, however they should be carefully reviewed and discussed among engineers, plant operators, and regulators. Through APR1400 VAI process, some of the policy concerns and assumptions for analysis were applied based on document research and expert panel discussions. It was also found that there are more assumptions to define for further studies for other types of nuclear power plants. One of the assumptions is mission time, which was inherited from PSA.

PIEteR : a field specific bio-economic production model for decision support in sugar beet growing

NARCIS (Netherlands)

Smit, A.B.

1996-01-01

To support decisions in sugar beet growing, a model, PIEteR, was developed. It simulates growth and production of the crop in a field specific way, making a tailor-made approach in decision taking possible.

PIEteR is based on causal regression analysis of Dutch data of mostly

DECISION ANALYSIS OF INCINERATION COSTS IN SUPERFUND SITE REMEDIATION

Science.gov (United States)

This study examines the decision-making process of the remedial design (RD) phase of on-site incineration projects conducted at Superfund sites. Decisions made during RD affect the cost and schedule of remedial action (RA). Decision analysis techniques are used to determine the...

A decision analysis of an exploratory studies facility

International Nuclear Information System (INIS)

Merkhofer, M.W.; Gnirk, P.

1991-01-01

An Exploratory Studies Facility (ESF) is planned to support the characterization of a potential site for a high-level nuclear waste repository at Yucca Mountain, NV. The selection of a design for the ESF is a critical decision, because the ESF design may affect the accuracy of characterization testing and subsequent repository design. The assist the design process, a comparative evaluation was conducted to rank 34 alternative relied on techniques from formal decision analysis, including decision trees and multiattribute utility analysis (MUA). The results helped to identify favorable design features and convinced the Department of Energy to adopt the top-ranked option as the preferred ESF design

Economic decision making and the application of nonparametric prediction models

Science.gov (United States)

Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.

2008-01-01

Sustained increases in energy prices have focused attention on gas resources in low-permeability shale or in coals that were previously considered economically marginal. Daily well deliverability is often relatively small, although the estimates of the total volumes of recoverable resources in these settings are often large. Planning and development decisions for extraction of such resources must be areawide because profitable extraction requires optimization of scale economies to minimize costs and reduce risk. For an individual firm, the decision to enter such plays depends on reconnaissance-level estimates of regional recoverable resources and on cost estimates to develop untested areas. This paper shows how simple nonparametric local regression models, used to predict technically recoverable resources at untested sites, can be combined with economic models to compute regional-scale cost functions. The context of the worked example is the Devonian Antrim-shale gas play in the Michigan basin. One finding relates to selection of the resource prediction model to be used with economic models. Models chosen because they can best predict aggregate volume over larger areas (many hundreds of sites) smooth out granularity in the distribution of predicted volumes at individual sites. This loss of detail affects the representation of economic cost functions and may affect economic decisions. Second, because some analysts consider unconventional resources to be ubiquitous, the selection and order of specific drilling sites may, in practice, be determined arbitrarily by extraneous factors. The analysis shows a 15-20% gain in gas volume when these simple models are applied to order drilling prospects strategically rather than to choose drilling locations randomly. Copyright ?? 2008 Society of Petroleum Engineers.

Stock Selection for Portfolios Using Expected Utility-Entropy Decision Model

Directory of Open Access Journals (Sweden)

Jiping Yang

2017-09-01

Full Text Available Yang and Qiu proposed and then recently improved an expected utility-entropy (EU-E measure of risk and decision model. When segregation holds, Luce et al. derived an expected utility term, plus a constant multiplies the Shannon entropy as the representation of risky choices, further demonstrating the reasonability of the EU-E decision model. In this paper, we apply the EU-E decision model to selecting the set of stocks to be included in the portfolios. We first select 7 and 10 stocks from the 30 component stocks of Dow Jones Industrial Average index, and then derive and compare the efficient portfolios in the mean-variance framework. The conclusions imply that efficient portfolios composed of 7(10 stocks selected using the EU-E model with intermediate intervals of the tradeoff coefficients are more efficient than that composed of the sets of stocks selected using the expected utility model. Furthermore, the efficient portfolio of 7(10 stocks selected by the EU-E decision model have almost the same efficient frontier as that of the sample of all stocks. This suggests the necessity of incorporating both the expected utility and Shannon entropy together when taking risky decisions, further demonstrating the importance of Shannon entropy as the measure of uncertainty, as well as the applicability of the EU-E model as a decision-making model.

A model-referenced procedure to support adversarial decision processes; Application to electricity planning

Energy Technology Data Exchange (ETDEWEB)

Bunn, D.W.; Vlahos, K. (London Business School (United Kingdom))

1992-10-01

In public enquiries concerning major facilities, such as the construction of a new electric power plant, it is observed that a useable decision model should be made commonly available alongside the open provision of data and assumptions. The protagonist, eg the electric utility, generally makes use of a complex, proprietary model for detailed evaluation of options. A simple emulator of this, based upon a regression analysis of numerous scenarios, and validated by further simulations is shown to be feasible and potentially attractive. It would be in the interests of the utility to make such a model-referenced decision support method generally available. The approach is considered in relation to the recent Hinkley Point C public enquiry for a new nuclear power plant in the UK. (Author).

Making decision process knowledge explicit using the product data model

NARCIS (Netherlands)

Petrusel, R.; Vanderfeesten, I.T.P.; Dolean, Cristina; Mican, D.

2011-01-01

In this paper, we present a new knowledge acquisition and formalization method: the decision mining approach. Basically, we aim to produce a model of the workflow of mental actions performed by decision makers during the decision process. We show that through the use of a Product Data Model (PDM) we

Traffic analysis toolbox volume XI : weather and traffic analysis, modeling and simulation.

Science.gov (United States)

2010-12-01

This document presents a weather module for the traffic analysis tools program. It provides traffic engineers, transportation modelers and decisions makers with a guide that can incorporate weather impacts into transportation system analysis and mode...

Environmental Modeling and Bayesian Analysis for Assessing Human Health Impacts from Radioactive Waste Disposal

Science.gov (United States)

Stockton, T.; Black, P.; Tauxe, J.; Catlett, K.

2004-12-01

Bayesian decision analysis provides a unified framework for coherent decision-making. Two key components of Bayesian decision analysis are probability distributions and utility functions. Calculating posterior distributions and performing decision analysis can be computationally challenging, especially for complex environmental models. In addition, probability distributions and utility functions for environmental models must be specified through expert elicitation, stakeholder consensus, or data collection, all of which have their own set of technical and political challenges. Nevertheless, a grand appeal of the Bayesian approach for environmental decision- making is the explicit treatment of uncertainty, including expert judgment. The impact of expert judgment on the environmental decision process, though integral, goes largely unassessed. Regulations and orders of the Environmental Protection Agency, Department Of Energy, and Nuclear Regulatory Agency orders require assessing the impact on human health of radioactive waste contamination over periods of up to ten thousand years. Towards this end complex environmental simulation models are used to assess "risk" to human and ecological health from migration of radioactive waste. As the computational burden of environmental modeling is continually reduced probabilistic process modeling using Monte Carlo simulation is becoming routinely used to propagate uncertainty from model inputs through model predictions. The utility of a Bayesian approach to environmental decision-making is discussed within the context of a buried radioactive waste example. This example highlights the desirability and difficulties of merging the cost of monitoring, the cost of the decision analysis, the cost and viability of clean up, and the probability of human health impacts within a rigorous decision framework.

Real-Time Decision Making and Aggressive Behavior in Youth: A Heuristic Model of Response Evaluation and Decision (RED).

Science.gov (United States)

Fontaine, Reid Griffith; Dodge, Kenneth A

2006-11-01

Considerable scientific and intervention attention has been paid to judgment and decision-making systems associated with aggressive behavior in youth. However, most empirical studies have investigated social-cognitive correlates of stable child and adolescent aggressiveness, and less is known about real-time decision making to engage in aggressive behavior. A model of real-time decision making must incorporate both impulsive actions and rational thought. The present paper advances a process model (response evaluation and decision; RED) of real-time behavioral judgments and decision making in aggressive youths with mathematic representations that may be used to quantify response strength. These components are a heuristic to describe decision making, though it is doubtful that individuals always mentally complete these steps. RED represents an organization of social-cognitive operations believed to be active during the response decision step of social information processing. The model posits that RED processes can be circumvented through impulsive responding. This article provides a description and integration of thoughtful, rational decision making and nonrational impulsivity in aggressive behavioral interactions.

An Overview of R in Health Decision Sciences.

Science.gov (United States)

Jalal, Hawre; Pechlivanoglou, Petros; Krijkamp, Eline; Alarid-Escudero, Fernando; Enns, Eva; Hunink, M G Myriam

2017-10-01

As the complexity of health decision science applications increases, high-level programming languages are increasingly adopted for statistical analyses and numerical computations. These programming languages facilitate sophisticated modeling, model documentation, and analysis reproducibility. Among the high-level programming languages, the statistical programming framework R is gaining increased recognition. R is freely available, cross-platform compatible, and open source. A large community of users who have generated an extensive collection of well-documented packages and functions supports it. These functions facilitate applications of health decision science methodology as well as the visualization and communication of results. Although R's popularity is increasing among health decision scientists, methodological extensions of R in the field of decision analysis remain isolated. The purpose of this article is to provide an overview of existing R functionality that is applicable to the various stages of decision analysis, including model design, input parameter estimation, and analysis of model outputs.

Robustness of Multiple Objective Decision Analysis Preference Functions

National Research Council Canada - National Science Library

Klimack, William

2002-01-01

.... The impact of these differences was examined to improve implementation efficiency. The robustness of the decision model was examined with respect to the preference functions to reduce the time burden imposed on the decision maker...

An Analysis of Design Decision-Making in Industrial Practice

DEFF Research Database (Denmark)

Ahmed, Saeema; Hansen, Claus Thorp

2002-01-01

This paper describes research that confronts a generic decision-making model with design strategies employed by experienced designers. The relationship between the decision-making activities proposed by the model and the eight design strategies identified by an empirical study of design work is e...

Decision-making in healthcare: a practical application of partial least square path modelling to coverage of newborn screening programmes

Directory of Open Access Journals (Sweden)

Fischer Katharina E

2012-08-01

Full Text Available Abstract Background Decision-making in healthcare is complex. Research on coverage decision-making has focused on comparative studies for several countries, statistical analyses for single decision-makers, the decision outcome and appraisal criteria. Accounting for decision processes extends the complexity, as they are multidimensional and process elements need to be regarded as latent constructs (composites that are not observed directly. The objective of this study was to present a practical application of partial least square path modelling (PLS-PM to evaluate how it offers a method for empirical analysis of decision-making in healthcare. Methods Empirical approaches that applied PLS-PM to decision-making in healthcare were identified through a systematic literature search. PLS-PM was used as an estimation technique for a structural equation model that specified hypotheses between the components of decision processes and the reasonableness of decision-making in terms of medical, economic and other ethical criteria. The model was estimated for a sample of 55 coverage decisions on the extension of newborn screening programmes in Europe. Results were evaluated by standard reliability and validity measures for PLS-PM. Results After modification by dropping two indicators that showed poor measures in the measurement models’ quality assessment and were not meaningful for newborn screening, the structural equation model estimation produced plausible results. The presence of three influences was supported: the links between both stakeholder participation or transparency and the reasonableness of decision-making; and the effect of transparency on the degree of scientific rigour of assessment. Reliable and valid measurement models were obtained to describe the composites of ‘transparency’, ‘participation’, ‘scientific rigour’ and ‘reasonableness’. Conclusions The structural equation model was among the first applications of PLS-PM to

Regulatory decision making by decision analyses

International Nuclear Information System (INIS)

Holmberg, J.; Pulkkinen, U.

1993-11-01

The Technical Research Centre of Finland (VTT) has studied with the Finnish Centre for Radiation and Nuclear Safety (STUK) the applicability of decision analytic approach to the treatment of nuclear safety related problems at the regulatory body. The role of probabilistic safety assessment (PSA) in decision making has also been discussed. In the study, inspectors from STUK exercised with a decision analytic approach by reoperationalizing two occurred and solved problems. The research scientist from VTT acted as systems analysts guiding the analysis process. The first case was related to a common cause failure phenomenon in solenoid valves controlling pneumatic valves important to safety of the plant. The problem of the regulatory body was to judge whether to allow continued operation or to require more detailed inspections and in which time chedule the inspections should be done. The latter problem was to evaluate design changes of external electrical grid connections after a fire incident had revealed weakness in the separation of electrical system. In both cases, the decision analysis was carried out several sessions in which decision makers, technical experts as well as experts of decision analysis participated. A multi-attribute value function was applied as a decision model so that attributes had to be defined to quantify the levels of achievements of the objectives. The attributes included both indicators related to the level of operational safety of the plant such as core damage frequency given by PSA, and indicators related to the safety culture, i.e., how well the chosen option fits on the regulatory policy. (24 refs., 6 figs., 9 tabs.)

The Limitations of Applying Rational Decision-Making Models

African Journals Online (AJOL)

decision-making models as applied to child spacing and more. specificaDy to the use .... also assumes that the individual operates as a rational decision- making organism in ..... work involves: Motivation; Counselling; Distribution ofIEC mate-.

Spatially explicit multi-criteria decision analysis for managing vector-borne diseases

Science.gov (United States)

2011-01-01

The complex epidemiology of vector-borne diseases creates significant challenges in the design and delivery of prevention and control strategies, especially in light of rapid social and environmental changes. Spatial models for predicting disease risk based on environmental factors such as climate and landscape have been developed for a number of important vector-borne diseases. The resulting risk maps have proven value for highlighting areas for targeting public health programs. However, these methods generally only offer technical information on the spatial distribution of disease risk itself, which may be incomplete for making decisions in a complex situation. In prioritizing surveillance and intervention strategies, decision-makers often also need to consider spatially explicit information on other important dimensions, such as the regional specificity of public acceptance, population vulnerability, resource availability, intervention effectiveness, and land use. There is a need for a unified strategy for supporting public health decision making that integrates available data for assessing spatially explicit disease risk, with other criteria, to implement effective prevention and control strategies. Multi-criteria decision analysis (MCDA) is a decision support tool that allows for the consideration of diverse quantitative and qualitative criteria using both data-driven and qualitative indicators for evaluating alternative strategies with transparency and stakeholder participation. Here we propose a MCDA-based approach to the development of geospatial models and spatially explicit decision support tools for the management of vector-borne diseases. We describe the conceptual framework that MCDA offers as well as technical considerations, approaches to implementation and expected outcomes. We conclude that MCDA is a powerful tool that offers tremendous potential for use in public health decision-making in general and vector-borne disease management in particular

Spatially explicit multi-criteria decision analysis for managing vector-borne diseases

Directory of Open Access Journals (Sweden)

Hongoh Valerie

2011-12-01

Full Text Available Abstract The complex epidemiology of vector-borne diseases creates significant challenges in the design and delivery of prevention and control strategies, especially in light of rapid social and environmental changes. Spatial models for predicting disease risk based on environmental factors such as climate and landscape have been developed for a number of important vector-borne diseases. The resulting risk maps have proven value for highlighting areas for targeting public health programs. However, these methods generally only offer technical information on the spatial distribution of disease risk itself, which may be incomplete for making decisions in a complex situation. In prioritizing surveillance and intervention strategies, decision-makers often also need to consider spatially explicit information on other important dimensions, such as the regional specificity of public acceptance, population vulnerability, resource availability, intervention effectiveness, and land use. There is a need for a unified strategy for supporting public health decision making that integrates available data for assessing spatially explicit disease risk, with other criteria, to implement effective prevention and control strategies. Multi-criteria decision analysis (MCDA is a decision support tool that allows for the consideration of diverse quantitative and qualitative criteria using both data-driven and qualitative indicators for evaluating alternative strategies with transparency and stakeholder participation. Here we propose a MCDA-based approach to the development of geospatial models and spatially explicit decision support tools for the management of vector-borne diseases. We describe the conceptual framework that MCDA offers as well as technical considerations, approaches to implementation and expected outcomes. We conclude that MCDA is a powerful tool that offers tremendous potential for use in public health decision-making in general and vector

Exploratory Use of Decision Tree Analysis in Classification of Outcome in Hypoxic–Ischemic Brain Injury

Directory of Open Access Journals (Sweden)

Thanh G. Phan

2018-03-01

Full Text Available BackgroundPrognostication following hypoxic ischemic encephalopathy (brain injury is important for clinical management. The aim of this exploratory study is to use a decision tree model to find clinical and MRI associates of severe disability and death in this condition. We evaluate clinical model and then the added value of MRI data.MethodThe inclusion criteria were as follows: age ≥17 years, cardio-respiratory arrest, and coma on admission (2003–2011. Decision tree analysis was used to find clinical [Glasgow Coma Score (GCS, features about cardiac arrest, therapeutic hypothermia, age, and sex] and MRI (infarct volume associates of severe disability and death. We used the area under the ROC (auROC to determine accuracy of model. There were 41 (63.7% males patients having MRI imaging with the average age 51.5 ± 18.9 years old. The decision trees showed that infarct volume and age were important factors for discrimination between mild to moderate disability and severe disability and death at day 0 and day 2. The auROC for this model was 0.94 (95% CI 0.82–1.00. At day 7, GCS value was the only predictor; the auROC was 0.96 (95% CI 0.86–1.00.ConclusionOur findings provide proof of concept for further exploration of the role of MR imaging and decision tree analysis in the early prognostication of hypoxic ischemic brain injury.

Exploratory Use of Decision Tree Analysis in Classification of Outcome in Hypoxic-Ischemic Brain Injury.

Science.gov (United States)

Phan, Thanh G; Chen, Jian; Singhal, Shaloo; Ma, Henry; Clissold, Benjamin B; Ly, John; Beare, Richard

2018-01-01

Prognostication following hypoxic ischemic encephalopathy (brain injury) is important for clinical management. The aim of this exploratory study is to use a decision tree model to find clinical and MRI associates of severe disability and death in this condition. We evaluate clinical model and then the added value of MRI data. The inclusion criteria were as follows: age ≥17 years, cardio-respiratory arrest, and coma on admission (2003-2011). Decision tree analysis was used to find clinical [Glasgow Coma Score (GCS), features about cardiac arrest, therapeutic hypothermia, age, and sex] and MRI (infarct volume) associates of severe disability and death. We used the area under the ROC (auROC) to determine accuracy of model. There were 41 (63.7% males) patients having MRI imaging with the average age 51.5 ± 18.9 years old. The decision trees showed that infarct volume and age were important factors for discrimination between mild to moderate disability and severe disability and death at day 0 and day 2. The auROC for this model was 0.94 (95% CI 0.82-1.00). At day 7, GCS value was the only predictor; the auROC was 0.96 (95% CI 0.86-1.00). Our findings provide proof of concept for further exploration of the role of MR imaging and decision tree analysis in the early prognostication of hypoxic ischemic brain injury.

A decision analysis of an exploratory studies facility

International Nuclear Information System (INIS)

Merkhofer, M.W.; Gnirk, P.

1992-01-01

This paper reports that an Exploratory Studied Facility (ESF) is planned to support the characterization of a potential site for a high-level nuclear waste repository at Yucca Mountain, NV. The selection of a design for the ESF is a critical characterization decision because the ESF design may affect the accuracy of characterization testing an constrains subsequent repository design. To assist the design process, a comparative evaluation was conducted to rank 34 alternative ESF-repository designs. The evaluation relied on techniques from formal decision analysis, including decision trees and multiattribute utility analysis (MUA). The results helped to identify favorable design features and enabled the Department of Energy to adopt an improved ESF design

Risk analysis for decision support in electricity distribution system asset management: methods and frameworks for analysing intangible risks

Energy Technology Data Exchange (ETDEWEB)

Nordgaard, Dag Eirik

2010-04-15

During the last 10 to 15 years electricity distribution companies throughout the world have been ever more focused on asset management as the guiding principle for their activities. Within asset management, risk is a key issue for distribution companies, together with handling of cost and performance. There is now an increased awareness of the need to include risk analyses into the companies' decision making processes. Much of the work on risk in electricity distribution systems has focused on aspects of reliability. This is understandable, since it is surely an important feature of the product delivered by the electricity distribution infrastructure, and it is high on the agenda for regulatory authorities in many countries. However, electricity distribution companies are also concerned with other risks relevant for their decision making. This typically involves intangible risks, such as safety, environmental impacts and company reputation. In contrast to the numerous methodologies developed for reliability risk analysis, there are relatively few applications of structured analyses to support decisions concerning intangible risks, even though they represent an important motivation for decisions taken in electricity distribution companies. The overall objective of this PhD work has been to explore risk analysis methods that can be used to improve and support decision making in electricity distribution system asset management, with an emphasis on the analysis of intangible risks. The main contributions of this thesis can be summarised as: An exploration and testing of quantitative risk analysis (QRA) methods to support decisions concerning intangible risks; The development of a procedure for using life curve models to provide input to QRA models; The development of a framework for risk-informed decision making where QRA are used to analyse selected problems; In addition, the results contribute to clarify the basic concepts of risk, and highlight challenges

Characterising bias in regulatory risk and decision analysis: An analysis of heuristics applied in health technology appraisal, chemicals regulation, and climate change governance.

Science.gov (United States)

MacGillivray, Brian H

2017-08-01

In many environmental and public health domains, heuristic methods of risk and decision analysis must be relied upon, either because problem structures are ambiguous, reliable data is lacking, or decisions are urgent. This introduces an additional source of uncertainty beyond model and measurement error - uncertainty stemming from relying on inexact inference rules. Here we identify and analyse heuristics used to prioritise risk objects, to discriminate between signal and noise, to weight evidence, to construct models, to extrapolate beyond datasets, and to make policy. Some of these heuristics are based on causal generalisations, yet can misfire when these relationships are presumed rather than tested (e.g. surrogates in clinical trials). Others are conventions designed to confer stability to decision analysis, yet which may introduce serious error when applied ritualistically (e.g. significance testing). Some heuristics can be traced back to formal justifications, but only subject to strong assumptions that are often violated in practical applications. Heuristic decision rules (e.g. feasibility rules) in principle act as surrogates for utility maximisation or distributional concerns, yet in practice may neglect costs and benefits, be based on arbitrary thresholds, and be prone to gaming. We highlight the problem of rule-entrenchment, where analytical choices that are in principle contestable are arbitrarily fixed in practice, masking uncertainty and potentially introducing bias. Strategies for making risk and decision analysis more rigorous include: formalising the assumptions and scope conditions under which heuristics should be applied; testing rather than presuming their underlying empirical or theoretical justifications; using sensitivity analysis, simulations, multiple bias analysis, and deductive systems of inference (e.g. directed acyclic graphs) to characterise rule uncertainty and refine heuristics; adopting "recovery schemes" to correct for known biases

A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis.

Science.gov (United States)

Allyn, Jérôme; Allou, Nicolas; Augustin, Pascal; Philip, Ivan; Martinet, Olivier; Belghiti, Myriem; Provenchere, Sophie; Montravers, Philippe; Ferdynus, Cyril

2017-01-01

The benefits of cardiac surgery are sometimes difficult to predict and the decision to operate on a given individual is complex. Machine Learning and Decision Curve Analysis (DCA) are recent methods developed to create and evaluate prediction models. We conducted a retrospective cohort study using a prospective collected database from December 2005 to December 2012, from a cardiac surgical center at University Hospital. The different models of prediction of mortality in-hospital after elective cardiac surgery, including EuroSCORE II, a logistic regression model and a machine learning model, were compared by ROC and DCA. Of the 6,520 patients having elective cardiac surgery with cardiopulmonary bypass, 6.3% died. Mean age was 63.4 years old (standard deviation 14.4), and mean EuroSCORE II was 3.7 (4.8) %. The area under ROC curve (IC95%) for the machine learning model (0.795 (0.755-0.834)) was significantly higher than EuroSCORE II or the logistic regression model (respectively, 0.737 (0.691-0.783) and 0.742 (0.698-0.785), p machine learning model, in this monocentric study, has a greater benefit whatever the probability threshold. According to ROC and DCA, machine learning model is more accurate in predicting mortality after elective cardiac surgery than EuroSCORE II. These results confirm the use of machine learning methods in the field of medical prediction.

Diffusion Modelling Reveals the Decision Making Processes Underlying Negative Judgement Bias in Rats.

Directory of Open Access Journals (Sweden)

Claire A Hales

Full Text Available Human decision making is modified by emotional state. Rodents exhibit similar biases during interpretation of ambiguous cues that can be altered by affective state manipulations. In this study, the impact of negative affective state on judgement bias in rats was measured using an ambiguous-cue interpretation task. Acute treatment with an anxiogenic drug (FG7142, and chronic restraint stress and social isolation both induced a bias towards more negative interpretation of the ambiguous cue. The diffusion model was fit to behavioural data to allow further analysis of the underlying decision making processes. To uncover the way in which parameters vary together in relation to affective state manipulations, independent component analysis was conducted on rate of information accumulation and distances to decision threshold parameters for control data. Results from this analysis were applied to parameters from negative affective state manipulations. These projected components were compared to control components to reveal the changes in decision making processes that are due to affective state manipulations. Negative affective bias in rodents induced by either FG7142 or chronic stress is due to a combination of more negative interpretation of the ambiguous cue, reduced anticipation of the high reward and increased anticipation of the low reward.

Diffusion Modelling Reveals the Decision Making Processes Underlying Negative Judgement Bias in Rats.

Science.gov (United States)

Hales, Claire A; Robinson, Emma S J; Houghton, Conor J

2016-01-01

Human decision making is modified by emotional state. Rodents exhibit similar biases during interpretation of ambiguous cues that can be altered by affective state manipulations. In this study, the impact of negative affective state on judgement bias in rats was measured using an ambiguous-cue interpretation task. Acute treatment with an anxiogenic drug (FG7142), and chronic restraint stress and social isolation both induced a bias towards more negative interpretation of the ambiguous cue. The diffusion model was fit to behavioural data to allow further analysis of the underlying decision making processes. To uncover the way in which parameters vary together in relation to affective state manipulations, independent component analysis was conducted on rate of information accumulation and distances to decision threshold parameters for control data. Results from this analysis were applied to parameters from negative affective state manipulations. These projected components were compared to control components to reveal the changes in decision making processes that are due to affective state manipulations. Negative affective bias in rodents induced by either FG7142 or chronic stress is due to a combination of more negative interpretation of the ambiguous cue, reduced anticipation of the high reward and increased anticipation of the low reward.

The application of the heuristic-systematic processing model to treatment decision making about prostate cancer.

Science.gov (United States)

Steginga, Suzanne K; Occhipinti, Stefano

2004-01-01

The study investigated the utility of the Heuristic-Systematic Processing Model as a framework for the investigation of patient decision making. A total of 111 men recently diagnosed with localized prostate cancer were assessed using Verbal Protocol Analysis and self-report measures. Study variables included men's use of nonsystematic and systematic information processing, desire for involvement in decision making, and the individual differences of health locus of control, tolerance of ambiguity, and decision-related uncertainty. Most men (68%) preferred that decision making be shared equally between them and their doctor. Men's use of the expert opinion heuristic was related to men's verbal reports of decisional uncertainty and having a positive orientation to their doctor and medical care; a desire for greater involvement in decision making was predicted by a high internal locus of health control. Trends were observed for systematic information processing to increase when the heuristic strategy used was negatively affect laden and when men were uncertain about the probabilities for cure and side effects. There was a trend for decreased systematic processing when the expert opinion heuristic was used. Findings were consistent with the Heuristic-Systematic Processing Model and suggest that this model has utility for future research in applied decision making about health.

an analysis of perceived prominent decision making areas in ...

African Journals Online (AJOL)

p2333147

Keywords: Game ranch management, decision making, risk perception, springbuck. ABSTRACT ..... environment, herd management (herd structure) and marketing and client satisfaction .... Prospect theory: An analysis of decision under risk.

LEADERSHIP MODELS AND EFFICIENCY IN DECISION CRISIS SITUATIONS, DURING DISASTERS

Directory of Open Access Journals (Sweden)

JAIME RIQUELME CASTAÑEDA

2017-09-01

Full Text Available This article explains how an effective leadership is made on a team during an emergency, during a decision crisis in the context of a disaster. From the approach of the process, we analyze some variables such as flexibility, value congruence, rationality, politicization, and quality of design. To achieve that, we made a fi eld work with the information obtained from the three Emergency headquarters deployed by the Chilean Armed Forces, due to the effects of the 8.8 earthquake on February 27th 2010. The data is analyzed through econometric technics. The results suggested that the original ideas and the rigorous analysis are the keys to secure the quality of the decision. It also, made possible to unveil the fact, that to have efficiency in operations in a disaster, it requires a big presence of a vision, mission, and inspiration about a solid and pre-existing base of goals and motivations. Finally, we can fi nd the support to the relationship between kinds of leadership and efficiency on crisis decision-making process of the disaster and opens a space to build a decision making theoretic model.

Relevance of a Managerial Decision-Model to Educational Administration.

Science.gov (United States)

Lundin, Edward.; Welty, Gordon

The rational model of classical economic theory assumes that the decision maker has complete information on alternatives and consequences, and that he chooses the alternative that maximizes expected utility. This model does not allow for constraints placed on the decision maker resulting from lack of information, organizational pressures,…

Risk-based emergency decision support

International Nuclear Information System (INIS)

Koerte, Jens

2003-01-01

In the present paper we discuss how to assist critical decisions taken under complex, contingent circumstances, with a high degree of uncertainty and short time frames. In such sharp-end decision regimes, standard rule-based decision support systems do not capture the complexity of the situation. At the same time, traditional risk analysis is of little use due to variability in the specific circumstances. How then, can an organisation provide assistance to, e.g. pilots in dealing with such emergencies? A method called 'contingent risk and decision analysis' is presented, to provide decision support for decisions under variable circumstances and short available time scales. The method consists of nine steps of definition, modelling, analysis and criteria definition to be performed 'off-line' by analysts, and procedure generation to transform the analysis result into an operational decision aid. Examples of pilots' decisions in response to sudden vibration in offshore helicopter transport method are used to illustrate the approach

Modeling Common-Sense Decisions in Artificial Intelligence

Science.gov (United States)

Zak, Michail

2010-01-01

A methodology has been conceived for efficient synthesis of dynamical models that simulate common-sense decision- making processes. This methodology is intended to contribute to the design of artificial-intelligence systems that could imitate human common-sense decision making or assist humans in making correct decisions in unanticipated circumstances. This methodology is a product of continuing research on mathematical models of the behaviors of single- and multi-agent systems known in biology, economics, and sociology, ranging from a single-cell organism at one extreme to the whole of human society at the other extreme. Earlier results of this research were reported in several prior NASA Tech Briefs articles, the three most recent and relevant being Characteristics of Dynamics of Intelligent Systems (NPO -21037), NASA Tech Briefs, Vol. 26, No. 12 (December 2002), page 48; Self-Supervised Dynamical Systems (NPO-30634), NASA Tech Briefs, Vol. 27, No. 3 (March 2003), page 72; and Complexity for Survival of Living Systems (NPO- 43302), NASA Tech Briefs, Vol. 33, No. 7 (July 2009), page 62. The methodology involves the concepts reported previously, albeit viewed from a different perspective. One of the main underlying ideas is to extend the application of physical first principles to the behaviors of living systems. Models of motor dynamics are used to simulate the observable behaviors of systems or objects of interest, and models of mental dynamics are used to represent the evolution of the corresponding knowledge bases. For a given system, the knowledge base is modeled in the form of probability distributions and the mental dynamics is represented by models of the evolution of the probability densities or, equivalently, models of flows of information. Autonomy is imparted to the decisionmaking process by feedback from mental to motor dynamics. This feedback replaces unavailable external information by information stored in the internal knowledge base. Representation

Dual Processes in Decision Making and Developmental Neuroscience: A Fuzzy-Trace Model.

Science.gov (United States)

Reyna, Valerie F; Brainerd, Charles J

2011-09-01

From Piaget to the present, traditional and dual-process theories have predicted improvement in reasoning from childhood to adulthood, and improvement has been observed. However, developmental reversals-that reasoning biases emerge with development -have also been observed in a growing list of paradigms. We explain how fuzzy-trace theory predicts both improvement and developmental reversals in reasoning and decision making. Drawing on research on logical and quantitative reasoning, as well as on risky decision making in the laboratory and in life, we illustrate how the same small set of theoretical principles apply to typical neurodevelopment, encompassing childhood, adolescence, and adulthood, and to neurological conditions such as autism and Alzheimer's disease. For example, framing effects-that risk preferences shift when the same decisions are phrases in terms of gains versus losses-emerge in early adolescence as gist-based intuition develops. In autistic individuals, who rely less on gist-based intuition and more on verbatim-based analysis, framing biases are attenuated (i.e., they outperform typically developing control subjects). In adults, simple manipulations based on fuzzy-trace theory can make framing effects appear and disappear depending on whether gist-based intuition or verbatim-based analysis is induced. These theoretical principles are summarized and integrated in a new mathematical model that specifies how dual modes of reasoning combine to produce predictable variability in performance. In particular, we show how the most popular and extensively studied model of decision making-prospect theory-can be derived from fuzzy-trace theory by combining analytical (verbatim-based) and intuitive (gist-based) processes.

Dual Processes in Decision Making and Developmental Neuroscience: A Fuzzy-Trace Model

Science.gov (United States)

Reyna, Valerie F.; Brainerd, Charles J.

2011-01-01

From Piaget to the present, traditional and dual-process theories have predicted improvement in reasoning from childhood to adulthood, and improvement has been observed. However, developmental reversals—that reasoning biases emerge with development —have also been observed in a growing list of paradigms. We explain how fuzzy-trace theory predicts both improvement and developmental reversals in reasoning and decision making. Drawing on research on logical and quantitative reasoning, as well as on risky decision making in the laboratory and in life, we illustrate how the same small set of theoretical principles apply to typical neurodevelopment, encompassing childhood, adolescence, and adulthood, and to neurological conditions such as autism and Alzheimer's disease. For example, framing effects—that risk preferences shift when the same decisions are phrases in terms of gains versus losses—emerge in early adolescence as gist-based intuition develops. In autistic individuals, who rely less on gist-based intuition and more on verbatim-based analysis, framing biases are attenuated (i.e., they outperform typically developing control subjects). In adults, simple manipulations based on fuzzy-trace theory can make framing effects appear and disappear depending on whether gist-based intuition or verbatim-based analysis is induced. These theoretical principles are summarized and integrated in a new mathematical model that specifies how dual modes of reasoning combine to produce predictable variability in performance. In particular, we show how the most popular and extensively studied model of decision making—prospect theory—can be derived from fuzzy-trace theory by combining analytical (verbatim-based) and intuitive (gist-based) processes. PMID:22096268

Data Decision Analysis: Project Shoal

Energy Technology Data Exchange (ETDEWEB)

Forsgren, Frank; Pohll, Greg; Tracy, John

1999-01-01

The purpose of this study was to determine the most appropriate field activities in terms of reducing the uncertainty in the groundwater flow and transport model at the Project Shoal area. The data decision analysis relied on well-known tools of statistics and uncertainty analysis. This procedure identified nine parameters that were deemed uncertain. These included effective porosity, hydraulic head, surface recharge, hydraulic conductivity, fracture correlation scale, fracture orientation, dip angle, dissolution rate of radionuclides from the puddle glass, and the retardation coefficient, which describes the sorption characteristics. The parameter uncertainty was described by assigning prior distributions for each of these parameters. Next, the various field activities were identified that would provide additional information on these parameters. Each of the field activities was evaluated by an expert panel to estimate posterior distribution of the parameters assuming a field activity was performed. The posterior distributions describe the ability of the field activity to estimate the true value of the nine parameters. Monte Carlo techniques were used to determine the current uncertainty, the reduction of uncertainty if a single parameter was known with certainty, and the reduction of uncertainty expected from each field activity on the model predictions. The mean breakthrough time to the downgradient land withdrawal boundary and the peak concentration at the control boundary were used to evaluate the uncertainty reduction. The radionuclide 137Cs was used as the reference solute, as its migration is dependent on all of the parameters. The results indicate that the current uncertainty of the model yields a 95 percent confidence interval between 42 and 1,412 years for the mean breakthrough time and an 18 order-of-magnitude range in peak concentration. The uncertainty in effective porosity and recharge dominates the uncertainty in the model predictions, while the

Personalization of models with many model parameters : an efficient sensitivity analysis approach

NARCIS (Netherlands)

Donders, W.P.; Huberts, W.; van de Vosse, F.N.; Delhaas, T.

2015-01-01

Uncertainty quantification and global sensitivity analysis are indispensable for patient-specific applications of models that enhance diagnosis or aid decision-making. Variance-based sensitivity analysis methods, which apportion each fraction of the output uncertainty (variance) to the effects of

Investment timing decisions in a stochastic duopoly model

Energy Technology Data Exchange (ETDEWEB)

Marseguerra, Giovanni [Istituto di Econometria e CRANEC, Universita Cattolica del Sacro Cuore di Milan (Italy)]. E-mail: giovanni.marseguerra@unicatt.it; Cortelezzi, Flavia [Dipartimento di Diritto ed Economia delle Persone e delle Imprese, Universita dell' Insubria (Italy)]. E-mail: flavia.cortelezzi@uninsubria.it; Dominioni, Armando [CORE-Catholique de Louvain la Neuve (Belgium)]. E-mail: dominioni@core.ucl.ac.be

2006-08-15

We investigate the role of strategic considerations on the optimal timing of investment when firms compete for a new market (e.g., the provision of an innovative product) under demand uncertainty. Within a continuous time model of stochastic oligopoly, we show that strategic considerations are likely to be of limited impact when the new product is radically innovative whilst the fear of a rival's entry may deeply affect firms' decisions whenever innovation is to some extent limited. The welfare analysis shows surprisingly that the desirability of the different market structures considered does not depend on the fixed entry cost.

Investment timing decisions in a stochastic duopoly model

International Nuclear Information System (INIS)

Marseguerra, Giovanni; Cortelezzi, Flavia; Dominioni, Armando

2006-01-01

We investigate the role of strategic considerations on the optimal timing of investment when firms compete for a new market (e.g., the provision of an innovative product) under demand uncertainty. Within a continuous time model of stochastic oligopoly, we show that strategic considerations are likely to be of limited impact when the new product is radically innovative whilst the fear of a rival's entry may deeply affect firms' decisions whenever innovation is to some extent limited. The welfare analysis shows surprisingly that the desirability of the different market structures considered does not depend on the fixed entry cost

A benefit–risk assessment model for statins using multicriteria decision analysis based on a discrete choice experiment in Korean patients

Directory of Open Access Journals (Sweden)

Byun JH

2016-06-01

Full Text Available Ji-Hye Byun,1 Sun-Hong Kwon,1 Ji-Hye Ha,2 Eui-Kyung Lee1 1School of Pharmacy, Sungkyunkwan University, Suwon-si, Gyeonggi-do, 2Ministry of Food and Drug Safety, Cheongju-si, Chungcheongbuk-do, South Korea Purpose: The benefit–risk balance for drugs can alter post approval owing to additional data on efficacy or adverse events. This study developed a quantitative benefit–risk assessment (BRA model for statins using multicriteria decision analysis with discrete choice experiments and compared a recent BRA with that at the time of approval. Patients and methods: Following a systematic review of the literature, the benefit criteria within the statin BRA model were defined as a reduction in the plasma low-density lipoprotein cholesterol level and a reduction in myocardial infarction incidence; the risk criteria were hepatotoxicity (Liv and fatal rhabdomyolysis (Rha. The scores for these criteria were estimated using mixed treatment comparison methods. Weighting was calculated from a discrete choice experiment involving 203 Korean patients. The scores and weights were integrated to produce an overall value representing the benefit–risk balance, and sensitivity analyses were conducted. Results: In this BRA model, low-density lipoprotein (relative importance [RI]: 37.50% was found to be a more important benefit criterion than myocardial infarction (RI: 35.43%, and Liv (RI: 16.28% was a more important risk criterion than Rha (RI: 10.79%. Patients preferred atorvastatin, and the preference ranking of cerivastatin and simvastatin was switched post approval because of the emergence of additional risk information related to cerivastatin. Conclusion: A quantitative statin BRA model confirmed that the preference ranking of statins changed post approval because of the identification of additional benefits or risks. Keywords: multicriteria decision analysis, statin, quantitative benefit–risk assessment, discrete choice experiment

Translational Models of Gambling-Related Decision-Making.

Science.gov (United States)

Winstanley, Catharine A; Clark, Luke

Gambling is a harmless, recreational pastime that is ubiquitous across cultures. However, for some, gambling becomes a maladaptive and compulsive, and this syndrome is conceptualized as a behavioural addiction. Laboratory models that capture the key cognitive processes involved in gambling behaviour, and that can be translated across species, have the potential to make an important contribution to both decision neuroscience and the study of addictive disorders. The Iowa gambling task has been widely used to assess human decision-making under uncertainty, and this paradigm can be successfully modelled in rodents. Similar neurobiological processes underpin choice behaviour in humans and rats, and thus, a preference for the disadvantageous "high-risk, high-reward" options may reflect meaningful vulnerability for mental health problems. However, the choice behaviour operationalized by these tasks does not necessarily approximate the vulnerability to gambling disorder (GD) per se. We consider a number of psychological challenges that apply to modelling gambling in a translational way, and evaluate the success of the existing models. Heterogeneity in the structure of gambling games, as well as in the motivations of individuals with GD, is highlighted. The potential issues with extrapolating too directly from established animal models of drug dependency are discussed, as are the inherent difficulties in validating animal models of GD in the absence of any approved treatments for GD. Further advances in modelling the cognitive biases endemic in human decision-making, which appear to be exacerbated in GD, may be a promising line of research.

A modeling framework for optimal long-term care insurance purchase decisions in retirement planning.

Science.gov (United States)

Gupta, Aparna; Li, Lepeng

2004-05-01

The level of need and costs of obtaining long-term care (LTC) during retired life require that planning for it is an integral part of retirement planning. In this paper, we divide retirement planning into two phases, pre-retirement and post-retirement. On the basis of four interrelated models for health evolution, wealth evolution, LTC insurance premium and coverage, and LTC cost structure, a framework for optimal LTC insurance purchase decisions in the pre-retirement phase is developed. Optimal decisions are obtained by developing a trade-off between post-retirement LTC costs and LTC insurance premiums and coverage. Two-way branching models are used to model stochastic health events and asset returns. The resulting optimization problem is formulated as a dynamic programming problem. We compare the optimal decision under two insurance purchase scenarios: one assumes that insurance is purchased for good and other assumes it may be purchased, relinquished and re-purchased. Sensitivity analysis is performed for the retirement age.

A methodological model to assist in the optimization and risk management of mining investment decisions

International Nuclear Information System (INIS)

Botin, Jose A; Guzman, Ronald R; Smith, Martin L

2011-01-01

Identifying, quantifying, and minimizing technical risks associated with investment decisions is a key challenge for mineral industry decision makers and investors. However, risk analysis in most bankable mine feasibility studies are based on the stochastic modeling of project N et Present Value (NPV)which, in most cases, fails to provide decision makers with a truly comprehensive analysis of risks associated with technical and management uncertainty and, as a result, are of little use for risk management and project optimization. This paper presents a value-chain risk management approach where project risk is evaluated for each step of the project life cycle, from exploration to mine closure, and risk management is performed as a part of a stepwise value-added optimization process.