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Breakthroughs in Decision Science and Risk Analysis

Book Description

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 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.

    Table of Contents

    1. Cover
    2. Title page
    3. Foreword
    4. Preface
    5. Contributors
    6. Chapter 1: Introduction: Five Breakthroughs in Decision and Risk Analysis
      1. Historical Development of Decision Analysis and Risk Analysis
      2. Overcoming Challenges for Applying Decision and Risk Analysis to Important, Difficult, Real-World Problems
    7. Chapter 2: The Ways We Decide: Reconciling Hearts and Minds
      1. Do we decide?
      2. Biology and Adaptation
      3. Seu and Game Theory
      4. Prospect Theory
      5. Behavioral Decision Theory
      6. Decisions with a Time Horizon
      7. Morals, Emotions, and Consumer Behavior
      8. Experimental Game Theory
      9. Behavior Modification and Conclusions
      10. References
    8. Chapter 3: Simulation Optimization: Improving Decisions under Uncertainty
      1. Introduction
      2. An Illustrative Example
      3. Optimization of Securities Portfolios
      4. Simulation
      5. A Simulation Optimization Solution Approach
      6. Simulation Optimization Applications in Other Real-World Settings
      7. Conclusions
      8. References
    9. Chapter 4: Optimal Learning in Business Decisions
      1. Introduction
      2. Optimal Learning in the Newsvendor Problem
      3. Optimal Learning in the Selection Problem
      4. Optimizing a Rule-Based Policy for Inventory Management
      5. Discussion
      6. References
    10. Chapter 5: Using Preference Orderings to Make Quantitative Trade-Offs
      1. Introduction
      2. Literature Review
      3. Estimating Attribute Weights from Ordinal Preference Rankings
      4. Illustrative Case Study
      5. Allowing for Negative Weights
      6. Reliability of Partial Rank Orderings
      7. Conclusions and Directions for Future Research
      8. Acknowledgments
      9. References
    11. Chapter 6: Causal Analysis and Modeling for Decision and Risk Analysis
      1. Introduction: The Challenge of Causal Inference in Risk Analysis
      2. How to do Better: More Objective Tests for Causal Impacts
      3. Predictive Models: Bayesian Network (BN) and Causal Graph Models
      4. Deciding What to do: Influence Diagrams (IDS)
      5. When is a BN or ID Causal?
      6. Conclusions: Improving Causal Analysis of Health Effects
      7. Acknowledgments
      8. References
    12. Chapter 7: Making Decisions without Trustworthy Risk Models
      1. Challenge: How to make Good Decisions without agreed-to, Trustworthy Risk Models?
      2. Principles and Challenges for Coping with Deep Uncertainty
      3. Ten Tools of Robust Risk Analysis for Coping with Deep Uncertainty
      4. Applying the Tools: Accomplishments and Ongoing Challenges for Managing Risks with Deep Uncertainty
      5. Conclusions
      6. Acknowledgments
      7. References
    13. Chapter 8: Medical Decision-Making: An Application to Sugar-Sweetened Beverages
      1. Introduction
      2. Medical Ethics and Autonomy
      3. Multiattribute Utility for Preferences of Life and Consumption Under Uncertainty
      4. Analysis Formulation
      5. Case Example: Value ‚ÄČto the Individual
      6. Societal Analysis
      7. Quality of Health Considerations
      8. Conclusion
      9. References
    14. Chapter 9: Electric Power Vulnerability Models: From Protection to Resilience
      1. Vulnerability-Analysis Methods
      2. Modeling Cascading Failures in Electric Power Networks
      3. Modeling Restoration Times
      4. Summary
      5. References
    15. Chapter 10: Outthinking the Terrorists
      1. Introduction
      2. Eliciting Attacker Actions from Experts
      3. Using Adaptive Decision and Game Theory
      4. Natural Language Processingto Determine Terrorist Intent
      5. Conclusions
      6. References
    16. Index
    17. End User License Agreement