Decision analysis provides both an overall paradigm and a set of tools with which a decision maker can construct and analyze a model of a decision situation. The objective of decision analysis is to help a decision maker think hard about the specific problem at hand, including the overall structure of the problem as well as his or her preferences and beliefs. Be assured, however, that the material can be thoroughly understood, and the problems can be worked, without any knowledge of calculus. Calculus concepts are used in a few instances as an explanatory tool.
Algebra is used liberally throughout the book. Chapter 10 provides a decision-analysis view of data analysis, including regression, and familiarity with such statistical procedures would be an advantage when covering this topic. In particular, some familiarity with probability and probability distributions would be helpful in Chapters 7 through 12. At some points in the book, the student should be familiar with basic statistical concepts normally covered in an undergraduate applied statistics course. The inspiration to write it has come from many sources, but perhaps most important was a desire to give students access to upto-date information on modern decision analysis techniques at a level that could be easily understood by those without a strong mathematical background.
This book provides a one-semester overview of decision analysis for advanced undergraduate and master's degree students. Normal Distribution: Cumulative Probabilitiesīeta Distribution: Cumulative ProbabilitiesĪnswers to Selected Exercises 719 Credits 721 Author Index 722 Subject Index 725 Poisson Distribution: Cumulative Probabilities When Independence Fails 660 Multiattribute Utility in Action: BC Hydro STRATEGIC DECISIONS AT BC HYDROĮxercises 667 Questions and Problems 668 Case Studies: A Mining-Investment Decision References EpilogueĬhapter 17 Conclusion and Further Reading A Decision-Analysis Reading Listīinomial Distribution: Individual Probabilitiesīinomial Distribution: Cumulative ProbabilitiesĬ Poisson Distribution: Individual Probabilities Substitutes and Complements 654 Assessing a Two-Attribute Utility Function 654 655 Three or More Attributes (Optional) RiskAttitudes 527 Utility Axioms, Paradoxes, and Implications 571 Conflicting Objectives I: Fundamental Objectives and the Additive Utility Function 598 Conflicting Objectives II: Multiattribute Utility Models with Ip.teractions 644 Conclusion and Further Reading 67 5 Appendixes 679 Answers to Selected Exercises Credit~ 721 Author Index 722 Subject Index 725Ĭhapter 16 Conflicting Objectives II: Multiattribute Utility Models with Interactions 644 Multiattribute Utility Functions: Direct Assessment 645 Independence Conditions 647 Preferential Independence 647ĭetermining Whether Independence Exists Using Independence Section 3 Modeling Preferences 13 14 15 16 17 Terence Reilly Gillette Professor Babson CollegeĮlements of Decision Problems 21 Structuring Decisi~ns 43 Making Choices 111 Sensitivity Analysis 174 Creativity and Decision Making 217ħ Probability Basics, ~49 8 Subjective Probabi~ity 295 9 Theoretical Prob'l.bility Models 10 Using Data 398 11 Monte Carlo Simulation 459 12
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