Preface | p. xxi |

Introduction to Decision Analysis | p. 1 |

Gypsy Moths and the Oda | p. 1 |

Why Are Decisions Hard? | p. 2 |

Why Study Decision Analysis? | p. 3 |

Subjective Judgments and Decision Making | p. 5 |

The Decision-Analysis Process | p. 5 |

Where Is Decision Analysis Used? | p. 8 |

Where Does the Software Fit In? | p. 9 |

Where Are We Going from Here? | p. 11 |

Modeling Decisions | p. 19 |

Elements of Decision Problems | p. 21 |

Values and Objectives | p. 21 |

Making Money: A Special Objective | p. 22 |

Values and the Current Decision Context | p. 23 |

Boeing's Supercomputer | p. 24 |

Decisions to Make | p. 25 |

Sequential Decisions | p. 26 |

Uncertain Events | p. 27 |

Consequences | p. 29 |

The Time Value of Money: A Special Kind of Trade-Off | p. 30 |

Larkin Oil | p. 33 |

Structuring Decisions | p. 43 |

Structuring Values | p. 44 |

Hiring a Summer Intern | p. 44 |

Fundamental and Means Objectives | p. 46 |

Getting the Decision Context Right | p. 51 |

Structuring Decisions: Influence Diagrams | p. 52 |

Influence Diagrams and the Fundamental-Objectives Hierarchy | p. 54 |

Using Arcs to Represent Relationships | p. 55 |

Some Basic Influence Diagrams | p. 57 |

Constructing an Influence Diagram (Optional) | p. 65 |

Toxic Chemicals and the Epa | p. 65 |

Structuring Decisions: Decision Trees | p. 69 |

Decision Trees and the Objectives Hierarchy | p. 71 |

Some Basic Decision Trees | p. 72 |

Decision Trees and Influence Diagrams Compared | p. 76 |

Decision Details: Defining Elements of the Decision | p. 76 |

More Decision Details: Cash Flows and Probabilities | p. 78 |

Defining Measurement Scales for Fundamental Objectives | p. 79 |

Using PrecisionTree for Structuring Decisions | p. 83 |

Making Choices | p. 111 |

Texaco Versus Pennzoil | p. 111 |

Decision Trees and Expected Monetary Value | p. 115 |

Solving Influence Diagrams: Overview | p. 119 |

Solving Influence Diagrams: The Details (Optional) | p. 121 |

Solving Influence Diagrams: An Algorithm (Optional) | p. 127 |

Risk Profiles | p. 128 |

Dominance: An Alternative to EMV | p. 133 |

Making Decisions with Multiple Objectives | p. 137 |

The Summer Job | p. 138 |

Analysis: One Objective at a Time | p. 140 |

Subjective Ratings for Constructed Attribute Scales | p. 140 |

Assessing Trade-Off Weights | p. 142 |

Analysis: Expected Values and Risk Profiles for Two Objectives | p. 143 |

Decision Analysis Using PrecisionTree | p. 146 |

Sensitivity Analysis | p. 174 |

Eagle Airlines | p. 174 |

Sensitivity Analysis: A Modeling Approach | p. 175 |

Problem Identification and Structure | p. 176 |

One-Way Sensitivity Analysis | p. 179 |

Tornado Diagrams | p. 180 |

Dominance Considerations | p. 181 |

Two-Way Sensitivity Analysis | p. 183 |

Sensitivity to Probabilities | p. 184 |

Two-Way Sensitivity Analysis for Three Alternatives (Optional) | p. 188 |

Investing in the Stock Market | p. 189 |

Sensitivity Analysis in Action | p. 192 |

Heart Disease in Infants | p. 192 |

Sensitivity Analysis Using TopRank and PrecisionTree | p. 193 |

Sensitivity Analysis: A Built-In Irony | p. 206 |

Creativity and Decision Making | p. 217 |

What Is Creativity? | p. 218 |

Theories of Creativity | p. 219 |

Chains of Thought | p. 219 |

Phases of the Creative Process | p. 220 |

Blocks to Creativity | p. 222 |

The Monk and the Mountain | p. 222 |

Making Cigars | p. 223 |

Ping-Pong Ball in a Pipe | p. 227 |

Value-Focused Thinking for Creating Alternatives | p. 230 |

Transportation of Nuclear Waste | p. 231 |

Other Creativity Techniques | p. 233 |

Creating Decision Opportunities | p. 239 |

Modeling Uncertainty | p. 247 |

Probability Basics | p. 249 |

A Little Probability Theory | p. 250 |

Venn Diagrams | p. 250 |

More Probability Formulas | p. 251 |

Uncertain Quantities | p. 256 |

Examples | p. 272 |

Oil Wildcatting | p. 272 |

John Hinckley's Trial | p. 278 |

Decision-Analysis Software and Bayes' Theorem | p. 280 |

Subjective Probability | p. 295 |

Uncertainty and Public Policy | p. 295 |

Probability: A Subjective Interpretation | p. 297 |

Accounting for Contingent Losses | p. 298 |

Assessing Discrete Probabilities | p. 299 |

Assessing Continuous Probabilities | p. 303 |

Pitfalls: Heuristics and Biases | p. 311 |

Tom W. | p. 311 |

Decomposition and Probability Assessment | p. 315 |

Experts and Probability Assessment: Pulling It All Together | p. 321 |

Climate Change at Yucca Mountain, Nevada | p. 324 |

Coherence and the Dutch Book (Optional) | p. 326 |

Constructing Distributions Using RISKview | p. 328 |

Theoretical Probability Models | p. 352 |

Theoretical Models Applied | p. 353 |

The Binomial Distribution | p. 354 |

The Poisson Distribution | p. 358 |

The Exponential Distribution | p. 361 |

The Normal Distribution | p. 363 |

The Beta Distribution | p. 369 |

Viewing Theoretical Distributions with RISKview | p. 373 |

Using Data | p. 398 |

Using Data to Construct Probability Distributions | p. 398 |

Halfway Houses | p. 400 |

Using Data to Fit Theoretical Probability Models | p. 404 |

Fitting Distributions to Data | p. 405 |

Using Data to Model Relationships | p. 412 |

The Regression Approach | p. 414 |

Natural Conjugate Distributions (Optional) | p. 436 |

A Bayesian Approach to Regression Analysis (Optional) | p. 445 |

Monte Carlo Simulation | p. 459 |

Fashions | p. 460 |

Using Uniform Random Numbers as Building Blocks | p. 463 |

General Uniform Distributions | p. 464 |

Exponential Distributions | p. 465 |

Discrete Distributions | p. 466 |

Other Distributions | p. 466 |

Simulating Spreadsheet Models Using @RISK | p. 466 |

Simulation, Decision Trees, and Influence Diagrams | p. 486 |

Value of Information | p. 496 |

Investing in the Stock Market | p. 496 |

Value of Information: Some Basic Ideas | p. 497 |

Expected Value of Perfect Information | p. 500 |

Expected Value of Imperfect Information | p. 502 |

Value of Information in Complex Problems | p. 508 |

Value of Information, Sensitivity Analysis, and Structuring | p. 509 |

Seeding Hurricanes | p. 510 |

Value of Information and Nonmonetary Objectives | p. 511 |

Value of Information and Experts | p. 512 |

Calculating EVPI and EVII with PrecisionTree | p. 512 |

Modeling Preferences | p. 525 |

Risk Attitudes | p. 527 |

E. H. Harriman Fights for the Northern Pacific Railroad | p. 528 |

Risk | p. 529 |

Risk Attitudes | p. 531 |

Investing in the Stock Market, Revisited | p. 533 |

Expected Utility, Certainty Equivalents, and Risk Premiums | p. 535 |

Keeping Terms Straight | p. 539 |

Utility Function Assessment | p. 539 |

Risk Tolerance and the Exponential Utility Function | p. 543 |

Modeling Preferences Using PrecisionTree | p. 546 |

Decreasing and Constant Risk Aversion (Optional) | p. 551 |

Some Caveats | p. 556 |

Utility Axioms, Paradoxes, and Implications | p. 571 |

Preparing for an Influenza Outbreak | p. 571 |

Axioms for Expected Utility | p. 572 |

Paradoxes | p. 578 |

Implications | p. 582 |

A Final Perspective | p. 586 |

Conflicting Objectives I: Fundamental Objectives and the Additive Utility Function | p. 598 |

Objectives and Attributes | p. 600 |

Trading Off Conflicting Objectives: The Basics | p. 602 |

The Additive Utility Function | p. 604 |

Assessing Individual Utility Functions | p. 610 |

Assessing Weights | p. 614 |

Keeping Concepts Straight: Certainty versus Uncertainty | p. 620 |

An Example: Library Choices | p. 621 |

The Eugene Public Library | p. 621 |

Using Software for Multiple-Objective Decisions | p. 628 |

Conflicting Objectives II: Multiattribute Utility Models with Interactions | p. 644 |

Multiattribute Utility Functions: Direct Assessment | p. 645 |

Independence Conditions | p. 647 |

Determining Whether Independence Exists | p. 648 |

Using Independence | p. 650 |

Additive Independence | p. 651 |

Substitutes and Complements | p. 654 |

Assessing a Two-Attribute Utility Function | p. 654 |

The Blood Bank | p. 655 |

Three or More Attributes (Optional) | p. 659 |

When Independence Fails | p. 660 |

Multiattribute Utility in Action: BC Hydro | p. 661 |

Strategic Decisions at BC Hydro | p. 661 |

Conclusion and Further Reading | p. 675 |

A Decision-Analysis Reading List | p. 676 |

Appendixes | p. 679 |

Binomial Distribution: Individual Probabilities | p. 680 |

Binomial Distribution: Cumulative Probabilities | p. 688 |

Poisson Distribution: Individual Probabilities | p. 696 |

Poisson Distribution: Cumulative Probabilities | p. 701 |

Normal Distribution: Cumulative Probabilities | p. 706 |

Beta Distribution: Cumulative Probabilities | p. 710 |

Answers to Selected Exercises | p. 719 |

Credits | p. 721 |

Author Index | p. 722 |

Subject Index | p. 725 |

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