Foundations of Decision Analysis

This book is described by the authors as emerging from what they have learned by "teaching decision analysis to thousands of people in the United States and around the world in university classes and special professional educational programs".

The early chapters and certain later chapters are written to be accessible to a general audience. Chapters 1 through 17 introduce the foundations of decision analysis without requiring significant mathematical sophistication. Chapter 26 discusses multi-attribute decision problems with no uncertainty. Chapter 33 analyzes decisions that involve a small probability of death, such as skiing or driving a car. Chapters 37 through 39 explain how to use the decision analysis approach when there are large groups involved. Chapter 40 discusses ethical considerations in decision making.

Readers with more mathematical and computational preparation can benefit from the remainder of the book after understanding the fundamentals. These chapters expose readers to problems that require a higher level of analysis, such as problems that may appear in organizations. Chapters 18 through 25 discuss advanced information gathering from multiple sources, risk aversion, and probabilistic dominance. Chapters 27 and 28 explain how to handle multiattribute decision problems with uncertainty. Chapter 30 shows how to update probability after observing the results of an experiment. Chapter 32 presents the concepts of risk scaling and sharing. Chapter 34 analyzes situations where a person is exposed to a large probability of death, such as may be faced in medical decisions. Chapters 35 and 36 illustrate how to solve decision problems numerically by simulation and discretization.

Chapters include:

• Ch 1: Introduction to Quality Decision Making
• Ch 2: Experiencing a Decision
• Ch 3: Clarifying Values
• Ch 4: Precise Decision Language
• Ch 5: Possibilities
• Ch 6: Handling Uncertainty
• Ch 7: Relevance
• Ch 8: Rules of Actional Thought
• Ch 9: The Party Problem
• Ch 10: Using a Value Measure
• Ch 11: Risk Attitude
• Ch 12: Sensitivity Analysis
• Ch 13: Basic Information Gathering
• Ch 14: Decision Diagrams
• Ch 15: Encoding Probability Distribution on a Measure
• Ch 16: From Phenomenon to Assessment
• Ch 17: Framing a Decision
• Ch 18: Valuing Information from Multiple Sources
• Ch 19: Options
• Ch 20: Detectors with Multiple Indications
• Ch 21: Decisions with Influences
• Ch 22: The Logarithmic U-Curve
• Ch 23: The Linear Risk Tolerance U-Curve
• Ch 24: Approximate Expressions for Certain Equivalent
• Ch 25: Deterministic and Probabilistic Dominance
• Ch 26: Decisions with Multiple Attributes 1
• Ch 27: Decisions with Multiple Attributes 2
• Ch 28: Decisions with Multiple Attributes 3
• Ch 29: Betting on Disparate Belief
• Ch 30: Learning from Experimentation
• Ch 31: Auctions and Bidding
• Ch 32: Evaluating, Scaling, and Sharing Uncertain Deals
• Ch 33: Making Risky Decisions
• Ch 34: Decisions with a High Probability of Death
• Ch 35: Discretizing Continuous Probability Distributions
• Ch 36: Solving Decision Problems by Simulation
• Ch 37: The Decision Analysis Cycle
• Ch 38: Topics in Organizational Decision Making
• Ch 39: Coordinating Decision Making Large Groups
• Ch 40: Decisions and Ethics