Breakthroughs in Decision Science and Risk Analysis.

Intro -- 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 -- 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 -- 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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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.