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Chapter 1 Introduction to Decision Analysis

Section 1 Modeling Decisions

Chapter 2 Elements of Decision Procblems

Chapter 3 Structuring Decisions

Chapter 4 Making Choices

Chapter 5 Sensitivity Analysis

Chapter 6 Creativity and Decision Making

Section 2 Modeling Uncertainty

Chapter 7 Probability Basics

Chapter 8 Subjective Probability

Chapter 9 Theoretical Probability Models

Chapter 10 Using Data

Chapter 11 Monte Carlo Simulation

Chapter 12 Value of Information

Section 3 Modeling Preferences

Chapter 13 Risk Attitudes

Chapter 14 Utility Axioms, Paradoxes, and Implications

Chapter 15 Conflicting Objectives I: Fundamental Objectives
and the Additive Utility Function

Chapter 16 Conflicting Objectives Ⅱ: Multiattribute Utility Models
with Interactions

Chapter 17 Conclusion and Further Reading

Answers to Selected Exercises
Credits
Author Index
Subject Index

Preface 1. Introduction To Decision Analysis. Why Are Decisions Hard? Why Study Decision Analysis? Subjective Judgements And Decision Making. The Decision Analysis Process. Where Is Decision Analysis Used. Where Does The Software Fit In? Where Are We Going From Here? Summary. Questions And Problems. Case Studies. References. Epilogue. Section I: Modeling Decisions. 2. Elements Of Decision Problems. Values And Objectives. Making Money: A Special Objective. Values And The Current Decision Context. Decisions To Make. Sequential Decisions. Uncertain Events. Consequences. The Time Value Of Money: A Special Kind Of Trade-Off. Summary. Questions And Problems. Case Studies. References. Epilogue. 3. Structuring Decisions. Structuring Values. Fundamental And Means Objectives. Getting The Decision Complex Right. Structuring Designs: Influence Diagrams. Influence Diagrams And The Fundamental-Objectives Hierarchy. Using Arcs To Represent Relationships. Some Basic Influence Diagrams. Constructing An Influence Diagram (Optional). Structuring Decisions: Decision Trees. Decision Trees And Influence Diagrams Compared. Decision Details: Defining Details: Defining Elements Of The Decision. More Decision Details: Cash Flows And Probabilities. Using Precisiontree For Structuring Decisions. Summary. Exercises. Questions And Problems. Case Studies. References. Epilogue. 4. Making Choices. Decision Trees And Expected Monetary Value. Solving Influence Diagrams: Overview. Solving Influence Diagrams: The Details (Optional). Solving Influence Diagrams: An Algorithm (Optional). Risk Profiles. Dominance: An Alternative To Emv. Making Decisions With Multiple Objectives. Analysis: One Objective At A Time. Subjective Ratings For Constructed Attribute Scales. Assessing Trade-Off Weights. Analysis: Expected Values And Risk Profiles For Two Objectives. Decision Analysis Using Precisontree. Summary. Exercises. Questions And Problems. Case Studies. References. Epilogue. 5. Sensitivity Analysis. Sensitivity Analysis: A Modeling Approach. Problem Identification And Structure. One-Way Sensitivity Analysis. Tornado Diagrams. Dominance Considerations. Two-Way Sensitivity Analysis. Sensitivity To Probabilities. Two-Way Sensitivity Analysis For Three Alternatives (Optional). Sensitivity Analysis In Action. Sensitivity Analysis Using Toprank And Precisiontree. Sensitivity Analysis: A Built-In Irony. Summary. Exercises. Questions And Problems. Case Studies. References. Epilogue. 6. Creativity And Decision Making. What Is Creativity? Theories Of Creativity. Chains Of Thought. Phases Of The Creative Process. Blocks To Creativity. Cultural And Environmental Blocks. Value-Focused Thinking For Creating Alternatives. Other Creativity Techniques. Creating Decision Opportunities. Summary. Questions And Problems. Case Studies. References. Epilogue. Section II: Modeling Uncertainty. 7. Probability Basics. A Little Probability Theory. Venn Diagrams. More Probability Formulas. Uncertain Quantities. Examples. Decision-Analysis Software And Bayes'' Theorem. Summary. Exercises. Questions And Problems. Case Studies. References. Epilogue. 8. Subjective Probability. Probability: A Subjective Interpretation. Assessing Discrete Probabilities. Assessing Continuous Probabilities. Pitfalls: Heuristics And Biases. Decomposition And Probability Assessment. Experts And Probability Assessment: Pulling It All Together. Coherence And The Dutch Book (Optional). Constructing Distributions Using Riskview. Summary. Exercises. Questions And Problems. Case Studies. References. Epilogue. 9. Theoretical Probability Models. The Binomial Distribution. The Poisson Distribution. The Exponential Distribution. The Normal Distribution. The Beta Distribution. Viewing Theoretical Distributions With Riskview. Summary. Exercises. Questions And Problems. Case Studies. References. Epilogue. 10. Using Data. Using Data To Construct Probability Distributions. Using Data To Fit Theoretical Probability Models. Fitting Distributi