Foundations of Decision Analysis, Global Edition

Ali E. Abbas / Ronald A. Howard  
Total pages
March 2015
Related Titles

Product detail

Product Price CHF Available  
Foundations of Decision Analysis, Global Edition
115.10 not defined


You'll find the eBook here.:

Free evaluation copy for lecturers


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