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Breakthroughs in decision science and risk analysis /
Discover recent powerful advances in the theory, methods, and applications of decision and risk analysis Focusing on recent 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 le...
| Clasificación: | Libro Electrónico |
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| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
Wiley,
[2014]
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| Colección: | Wiley essentials in operations research and management science
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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 ConclusionsReferences; 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 ProblemOptimal 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.
- AcknowledgmentsReferences; 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 UncertaintyPoint 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.