Making Hard Decisions, With DecisionTools

MAKING HARD DECISIONS WITH DECISIONTOOLS(R) is a special version of Bob Clemen's best-selling text, MAKING HARD DECISIONS. This straight-forward book teaches the fundamental ideas of decision analysis, without an overly technical explanation of the mathematics used in management science. This new version incorporates and implements the powerful DecisionTools(R) by Palisade Corporation, the world's leading toolkit for risk and decision analysis. At the end of each chapter, topics are illustrated with step-by-step instructions for DecisionTools(R). This new version makes the text more useful and relevant to students to business and engineering. 

 

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 Distributions To Data. Using Data To Model Relationships. The Regression Approach. Natural Conjugate Distributions (Optional). A Bayesian Approach To Regression Analysis (Optional). Summary. Exercises. Questions And Problems. Case Studies. References. 11. Monte Carlo Simulation. Using Uniform Random Numbers As Building Blocks. General Uniform Distributions. Exponential Distributions. Discrete Distributions. Other Distributions. Simulating Spreadsheet Models Using @Risk. Simulation, Decision Trees, And Influence Diagrams. Summary. Exercises. Questions And Problems. Case Studies. References. 12. Value Of Information. Value Of Information: Some Basic Ideas. Expected Value Of Perfect Information. Expected Value Of Imperfect Information. Value Of Information In Complex Problems. Value Of Information, Sensitivity Analysis, And Structuring. Value Of Information And Nonmonetary Objectives. Value Of Information And Experts. Calculating Evpi And Evii With Precisiontree. Summary. Exercises. Questions And Problems. Case Studies. References. Section III: Modeling Preferences. 13. Risk Attitudes. Risk. Risk Attitudes. Investing In The Stock Market, Revisited. Expected Utility, Certainty Equivalents, And Risk Premiums. Keeping Terms Straight. Utility Function Assessment. Risk Tolerance And The Exponential Utility Function. Modeling Preferences Using Precisiontree. Decreasing And Constant Risk Aversion (Optional). Some Caveats. Summary. Exercises. Questions And Problems. Case Studies. References. Epilogue. 14. Utility Axioms, Paradoxes, And Implications. Axioms For Expected Utility. Paradoxes. Implications. A Final Perspective. Summary. Exercises. Questions And Problems. Case Studies. References. Epilogue. 15. Conflicting Objectives I: Fundamental Objectives And The Additive Utility Function Objectives And Attributes. Trading Off Conflicting Objectives: The Basics. The Additive Utility Function. Assessing Individual Utility Functions. Assessing Weights. Keeping Concepts Straight: Certainty Versus Uncertainty. An Example: Library Choices. Using Software For Multiple-Objective Decisions. Summary. Exercises. Questions And Problems. Case Studies. References. Epilogue. 16. Conflicting Objectives II: Multiattribute Utility Models With Interactions. Multiattribute Utility Functions: Direct Assessment. Independence Conditions. Determining Whether Independence Exists. Using Independence. Additive Independence. Substitutes And Complements. Assessing A Two-Attribute Utility Function. Three Or More Attributes (Optional). When Independence Fails. Multiattribute Utility In Action: Bc Hydro. Summary. Exercises. Questions And Problems. Case Studies. References. Epilogue. 17. Conclusion And Further Reading. A Decision-Analysis Reading List. Appendix A: Binomial Distribution: Individual Probabilities. Appendix B: Binomial Distribution: Cumulative Probabilities. Appendix C: Poisson Distribution: Individual Probabilities. Appendix D: Poisson Distribution: Cumulative Probabilities. Appendix E: Normal Distribution: Cumulative Probabilities. Appendix F: Beta Distribution: Cumulative Probabilities. Answers To Selected Exercises. Author Index. Subject Index.