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241008 ||| eng |
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|a 1118217160
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|a 1118938909
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|a 9781118938904
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|a T57.95
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|a Cox, Louis A.
|e editor
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|a Breakthroughs in decision science and risk analysis
|c edited by Louis Anthony Cox, Jr. ; contributors, Ali E. Abbas [and fourteen others]
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|a Hoboken, New Jersey
|b Wiley
|c 2015
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| 300 |
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|a 331 pages
|b illustrations (some color)
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|a Includes bibliographical references at the end of each chapters and index
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|a Medical Ethics and Autonomy -- Multiattribute Utility for Preferences of Life and Consumption Under Uncertainty -- Analysis Formulation -- The Decision Tree Framework -- Calculations -- Special Considerations and Limitations -- Case Example: Value to the Individual -- Societal Analysis -- Quality of Health Considerations -- Measuring Quality of Health -- Preference Models with Quality of Health -- Conclusion -- References -- Chapter 9 Electric Power Vulnerability Models: From Protection to Resilience -- Vulnerability-Analysis Methods -- Rating-Based Methods -- Risk-Based Methods -- Game-Theoretic and Quasi-Game-Theoretic Methods -- Modeling Cascading Failures in Electric Power Networks -- Deterministic Models of Cascading Failure -- Probabilistic Models of Cascading Failure -- Modeling Restoration Times -- Summary -- References -- Chapter 10 Outthinking the Terrorists -- Introduction -- Eliciting Attacker Actions from Experts -- Using Adaptive Decision and Game Theory -- Natural Language Processing to Determine Terrorist Intent -- Conclusions -- References -- Index -- EULA.
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|a Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- Contributors -- Chapter 1 Introduction: Five Breakthroughs in Decision and Risk Analysis -- Historical Development of Decision Analysis and Risk Analysis -- Overcoming Challenges for Applying Decision and Risk Analysis to Important, Difficult, Real-World Problems -- Chapter 2 The Ways We Decide: Reconciling Hearts and Minds -- Do we decide? -- Biology and Adaptation -- Seu and Game Theory -- Prospect Theory -- Behavioral Decision Theory -- Decisions with a Time Horizon -- Morals, Emotions, and Consumer Behavior -- Experimental Game Theory -- Behavior Modification and Conclusions -- References -- Chapter 3 Simulation Optimization: Improving Decisions under Uncertainty -- Introduction -- An Illustrative Example -- Optimization of Securities Portfolios -- Simulation -- A Simulation Optimization Solution Approach -- Simulation Optimization Applications in Other Real-World Settings -- Selecting the Best Configuration in a Hospital Emergency Room -- Selecting the Best Staffing Level for a Personal Claims Process at an Insurance Company -- Conclusions -- References -- Chapter 4 Optimal Learning in Business Decisions -- Introduction -- Optimal Learning in the Newsvendor Problem -- Optimal Learning in the Selection Problem -- Optimizing a Rule-Based Policy for Inventory Management -- Discussion -- References -- Chapter 5 Using Preference Orderings to Make Quantitative Trade-Offs -- Introduction -- Literature Review -- Estimating Attribute Weights from Ordinal Preference Rankings -- Conjoint Analysis: LINMAP -- Probabilistic Inversion -- Bayesian Density Estimation -- Relationship between LINMAP, PI, and BDE -- Illustrative Case Study -- Allowing for Negative Weights -- Reliability of Partial Rank Orderings -- Conclusions and Directions for Future Research -- Acknowledgments -- References
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|a Chapter 6 Causal Analysis and Modeling for Decision and Risk Analysis -- Introduction: The Challenge of Causal Inference in Risk Analysis -- How to do Better: More Objective Tests for Causal Impacts -- Predictive Models: Bayesian Network (BN) and Causal Graph Models -- Deciding What to do: Influence Diagrams (IDS) -- When is a BN or ID Causal? -- Conclusions: Improving Causal Analysis of Health Effects -- Acknowledgments -- References -- Chapter 7 Making Decisions without Trustworthy Risk Models -- Challenge: How to make Good Decisions without agreed-to, Trustworthy Risk Models? -- Principles and Challenges for Coping with Deep Uncertainty -- Point of Departure: Subjective Expected Utility Decision Theory -- Four Major Obstacles to Applying SEU to Risk Management with Model Uncertainty -- Ten Tools of Robust Risk Analysis for Coping with Deep Uncertainty -- Using Multiple Models and Relevant Data to Improve Decisions -- Robust Decisions with Model Ensembles -- Averaging Forecasts -- Resampling Data Allows Robust Statistical Inferences despite Model Uncertainty -- Adaptive Sampling and Modeling: Boosting -- BMA for Statistical Estimation with Relevant Data but Model Uncertainty -- Learning How to Make Low-Regret Decisions -- Reinforcement Learning (RL) of Low-Regret Risk Management Policies for Uncertain Dynamic Systems -- Applying the Tools: Accomplishments and Ongoing Challenges for Managing Risks with Deep Uncertainty -- Planning for Climate Change and Reducing Energy Waste -- Sustainably Managing Renewable Resources and Protecting Ecosystems -- Managing Disease Risks -- Maintaining Reliable Network Infrastructure Service Despite Disruptions -- Adversarial Risks and Risks from Intelligent Agents -- Conclusions -- Acknowledgments -- References -- Chapter 8 Medical Decision-Making: An Application to Sugar-Sweetened Beverages -- Introduction
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|a decision making / aat
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|a BUSINESS & ECONOMICS / Organizational Behavior / bisacsh
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|a BUSINESS & ECONOMICS / Management / bisacsh
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|a risk assessment / aat
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|a Risk assessment / http://id.loc.gov/authorities/subjects/sh87002638
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|a Decision making / http://id.loc.gov/authorities/subjects/sh85036199
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|a BUSINESS & ECONOMICS / Industrial Management / bisacsh
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|a Risk assessment / fast
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|a Decision making / fast
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|a Prise de décision
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|a Évaluation du risque
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|a BUSINESS & ECONOMICS / Management Science / bisacsh
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| 700 |
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|a Abbas, Ali E.
|e contributor
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|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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|a Operations researdhf and management science
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|u https://learning.oreilly.com/library/view/~/9781118938898/?ar
|x Verlag
|3 Volltext
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|a 658.4/03
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|a 302.3
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|a Discover recent powerful advances in the theory, methods, and applications of decision and risk analysis Focusing on modern advances and innovations in the field of decision analysis (DA), Breakthroughs in Decision Science and Risk Analysis presents theories and methods for making, improving, and learning from significant practical decisions. The book explains these new methods and important applications in an accessible and stimulating style for readers from multiple backgrounds, including psychology, economics, statistics, engineering, risk analysis, operations research, and management science. Highlighting topics not conventionally found in DA textbooks, the book illustrates genuine advances in practical decision science, including developments and trends that depart from, or break with, the standard axiomatic DA paradigm in fundamental and useful ways. The book features methods for coping with realistic decision-making challenges such as online adaptive learning algorithms, innovations in robust decision-making, and the use of a variety of models to explain available data and recommend actions. In addition, the book illustrates how these techniques can be applied to dramatically improve risk management decisions. Breakthroughs in Decision Science and Risk Analysis also includes: An emphasis on new approaches rather than only classical and traditional ideas Discussions of how decision and risk analysis can be applied to improve high-stakes policy and management decisions Coverage of the potential value and realism of decision science within applications in financial, health, safety, environmental, business, engineering, and security risk management Innovative methods for deciding what actions to take when decision problems are not completely known or described or when useful probabilities cannot be specified Recent breakthroughs in the
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|a Psychology and brain science of risky decisions, mathematical foundations and techniques, and integration with learning and pattern recognition methods from computational intelligence Breakthroughs in Decision Science and Risk Analysis is an ideal reference for researchers, consultants, and practitioners in the fields of decision science, operations research, business, management science, engineering, statistics, and mathematics. The book is also an appropriate guide for managers, analysts, and decision and policy makers in the areas of finance, health and safety, environment, business, engineering, and security risk management
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