Foundations of Decision Analysis, Global Edition

Ali E. Abbas / Ronald A. Howard  
Total pages
March 2015
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Foundations of Decision Analysis, Global Edition
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For courses in Decision Making and Engineering.


The Fundamentals of Analyzing and Making Decisions

Foundations of Decision Analysis is a groundbreaking text that explores the art of decision making, both in life and in professional settings. By exploring themes such as dealing with uncertainty and understanding the distinction between a decision and its outcome, the First Edition teaches students to achieve clarity of action in any situation.


The book treats decision making as an evolutionary process from a scientific standpoint. Strategic decision-making analysis is presented as a tool to help students understand, discuss, and settle on important life choices. Through this text, students will understand the specific thought process that occurs behind approaching any decision to make easier and better life choices for themselves.


Foundations of Decision Analysis contains the following features to facilitate learning:


An easy to read text accessible by all audiences

  • The book approaches the process of decision making from a mathematical standpoint, but many of its chapters steer clear of complex equations so basic fundamentals can be easily understood by a general audience.
    • Chapters 1-17 introduce foundations of decision analysis without mathematical or computational emphasis. Topics include characterizing a decision, the rules of actional thought, u-curves, sensitivity analysis, probability encoding, and framing.
    • Chapter 26 discusses multi-attribute decision problems with no uncertainty to prepare readers to approach these issues in real life when uncertainty is present.
    • Chapter 29 teachers readers to make decisions based on differing beliefs using rules of probability.
    • Chapter 33 explores decisions that involve a small probability of death.
    • Chapters and 37-39 applies the decision analysis approach to large group settings.
    • Chapter 40 discusses ethical consideration in decision making.
  • Readers with more mathematical and computational preparation can benefit from the latter half of the book after understanding basic fundamentals presented in chapters 1-17.
    • Chapters 18-25 discuss advanced information gathering from multiple sources, the concept of creating operations in our daily lives, u-curves that describe risk aversion, using approximate formulas for valuing deals, and the concept of probabilistic dominance relations to facilitate the best alternative.
    • Chapters 27 and 28 uses a value function for cash flows to determine and explain multi-attribute problems with uncertainty.
    • Chapter 30 teaches students to update probability after observing the results of an experiment.
    • Chapter 31 explores using the basic concepts for decision analysis to determine the best bid at the value of bidding opportunity at a variety of auction types.
    • Chapter 32 presents the concepts of risk scaling and sharing, exploring how decision makers can determine the best portion of an investment, how a partnership can share an investment, and how to establish the risk tolerance of a partnership.
    • Chapter 34 analyzes situations in which the decision maker is exposed to a high probability of death.
    • Chapters 35 and 36 teach students to solve decision making problems mathematically by using simulation and discretization.
    • Numerical problems are exposed in tabular format to help facilitate completion.

The “Decision Analysis Core Concepts Map” is a pedagogical feature that helps students understand major concepts

  • Provides a summary of major concepts that students can use as a reference of major points to grasp in each chapter.
  • Concepts are presented in chronological order to make for an easy flow of understanding key information.
  • Arrows are used between related concepts to show students what they must understand before approaching the next topic.

A text that teaches by real world example

  • Chapter 37 presents a case study that exemplifies the decision making tools presented throughout the book in a real life setting.

Table of Contents

Part 1 Defining a Good Decision

Chapter 1: Introduction to Quality Decision Making

Chapter 2: Experiencing a Decision


Part 2 Clear Thinking and Characterization

Chapter 3: Clarifying Values

Chapter 4: Precise Decision Language

Chapter 5: Possibilities

Chapter 6: Handling Uncertainty

Chapter 7: Relevance


Part 3 Making any Decision

Chapter 8: Rules of Actional Thought

Chapter 9: The Party Problem

Chapter 10: Using a Value Measure


Part 4 Building on the Rules

Chapter 11: Risk Attitude

Chapter 12: Sensitivity Analysis

Chapter 13: Basic Information Gathering

Chapter 14: Decision Diagrams


Part 5 Characterizing What you Know

Chapter 15: Encoding a Probability Distribution on a Measure

Chapter 16: From Phenomenon to Assessment


Part 6 Framing a Decision

Chapter 17: Framing a Decision


Part 7 Advanced Information Gathering

Chapter 18: Valuing Information from Multiple Sources

Chapter 19: Options

Chapter 20: Detectors with Multiple Indications

Chapter 21: Decisions with Influences


Part 8 Characterizing What You Want

Chapter 22: The Logarithmic u-Curve

Chapter 23: The Linear Risk Tolerance u-Curve

Chapter 24: Approximate Expressions for the Certain

Chapter 25: Deterministic and Probabilistic Dominance

Chapter 26: Decisions with Multiple Attributes (1)–Ordering

Chapter 27: Decisions with Multiple Attributes (2)–Value Functions

Chapter 28: Decisions with Multiple Attributes (3)–Preference Equivalent

Prospects with Preference and Value Functions

for Investment Cash Flows: Time Preference

Probabilities Over Value


Part 9 Some Practical Extensions

Chapter 29: Betting on Disparate Belief

Chapter 30: Learning from Experimentation

Chapter 31: Auctions and Bidding

Chapter 32: Evaluating, Scaling, and Sharing Uncertain Deals

Chapter 33: Making Risky Decisions

Chapter 34: Decisions with a High Probability of Death


Part 10 Computing Decision Problems

Chapter 35: Discretizing Continuous Probability Distributions

Chapter 36: Solving Decision Problems by Simulation


Part 11 Professional Decisions

Chapter 37: The Decision Analysis Cycle

Chapter 38: Topics in Organizational Decision Making

Chapter 39: Coordinating the Decision Making of Large


Part 12 Ethical Considerations

Chapter 40: Decisions and Ethics Groups