Foundations of Decision Analysis, Global Edition

Cover
Title
Copyright
Brief Contents
Contents
Chapter 1: Introduction to Quality Decision Making
1.1 Introduction
1.2 Normative Vs. Descriptive
1.3 Declaring a Decision
1.4 Thought Vs. Action
1.5 What is a Decision?
1.6 Decision Vs. Outcome
1.7 Clarity of Action
1.8 What is a Good Decision?
1.9 Summary
Key Terms
Problems
Chapter 2: Experiencing a Decision
2.1 Introduction
2.2 Analysis of a Decision: The Thumbtack and the Medallion Example
2.3 Lessons Learned from the Thumbtack and Medallion Example
2.4 Summary
Key Terms
Appendix A: Results of the Thumbtack Demonstration
Problems
Chapter 3: Clarifying Values
3.1 Introduction
3.2 Value in Use and Value in Exchange
3.3 Values Around a Cycle of Ownership
3.4 Summary
Key Terms
Problems
Chapter 4: Precise Decision Language
4.1 Introduction
4.2 Lego-Like Precision
4.3 Precise Decision Language
4.4 Experts and Distinctions
4.5 Mastery
4.6 Creating Your Own Distinctions
4.7 Footnote
4.8 Summary
Key Terms
Problems
Chapter 5: Possibilities
5.1 Overview
5.2 Creating Distinctions
5.3 The Possibility Tree
5.4 Measures
5.5 Sumary
Key Terms
Problems
Chapter 6: Handling Uncertainty
6.1 Introduction
6.2 Describing Degree of Belief by Probability
6.3 The Probability Tree
6.4 Several Degrees of Distinction
6.5 Multiple Degrees of Distinction
6.6 Probability Trees Using Multiple Distinctions
6.7 Adding Measures to the Probability Tree
6.8 Multiple Measures
6.9 Summary
Key Terms
Appendix A: The Chain Rule for Distinctions: Calculating Elemental Probabilities
Appendix B: Let’s Make a Deal Commentary
Appendix C: Further Discussion Related to the Example: At Least One Boy
Problems
Chapter 7: Relevance
7.1 Introduction
7.2 Relevance with Simple Distinctions
7.3 Is Relevance Mutual?
7.4 Relevance Diagrams
7.5 Alternate Asessment Orders
7.6 Relevance Depends on Knowledge
7.7 Distinctive Vs. Asociative Logic
7.8 The Third Factor
7.9 Multi-Degree Relevance
7.10 Summary
Key Terms
Appendix A: More on Relevance Diagrams and Arrow Reversals
Problems
Chapter 8: Rules of Actional Thought
8.1 Introduction
8.2 Using Rules for Decision Making
8.3 The Decision Situation
8.4 The Five Rules of Actional Thought
8.5 Summary
Key Terms
Problems
Chapter 9: The Party Problem
9.1 Introduction
9.2 The Party Problem
9.3 Simplifying the Rules: E-Value
9.4 Understanding the Value of the Party Problem
9.5 Summary
Key Terms
Appendix A
Problems
Chapter 10: Using a Value Measure
10.1 Introduction
10.2 Money as a Value Measure
10.3 u-curves
10.4 Valuing Clairvoyance
10.5 Jane’s Party Problem
10.6 Attitudes toward Risk
10.7 Mary’s Party Problem
10.8 Summary
Key Terms
Problems
Chapter 11: Risk Attitude
11.1 Introduction
11.2 Wealth Risk Attitude
11.3 Buying and Selling a Deal Around a Cycle of Ownership
11.4 The Delta Property
11.5 Risk Odds
11.6 Delta Property Simplifications
11.7 Other Forms of Exponential u-Curve
11.8 Direct Assessment of Risk Tolerance
11.9 Summary
Key Terms
Problems
Chapter 12: Sensitivity Analysis
12.1 Introduction
12.2 Kim’s Sensitivity to Probability of Sunshine
12.3 Certain Equivalent Sensitivity
12.4 Value of Clairvoyance Sensitivity to Probability of Sunshine
12.5 Jane’s Sensitivity to Probability of Sunshine
12.6 Comparison of Kim’s and Jane’s Value of Clairvoyance Sensitivities
12.7 Risk Sensitivity Profile
12.8 Summary
Key Terms
Problems
Chapter 13: Basic Information Gathering
13.1 Introduction
13.2 The Value of Information
13.3 The Acme Rain Detector
13.4 General Observations on Experiments
13.5 Asymmetric Experiments
13.6 Information Gathering Equivalents
13.7 Summary
Problems
Chapter 14: Decision Diagrams
14.1 Introduction
14.2 Nodes in the Decision Diagram
14.3 Arrows in Decision Diagrams
14.4 Value of Clairvoyance
14.5 Imperfect Information
14.6 Decision Tree Order
14.7 Detector Use Decision
14.8 Summary
Key Terms
Problems
Chapter 15: Encoding a Probability Distribution on a Measure
15.1 Introduction
15.2 Probability Encoding
15.3 Fractiles of a Probability Distribution
15.4 Summary
Key Terms
Problems
Answers to Problem 2
Chapter 16: From Phenomenon to Asesment
16.1 Introduction
16.2 Information Transmission
16.3 Perception
16.4 Cognition
16.5 Motivation
16.6 Summary
Key Terms
Chapter 17: Framing a Decision
17.1 Introduction
17.2 Making a Decision
17.3 Selecting a Frame
17.4 Summary
Key Terms
Problems
Chapter 18: Valuing Information from Multiple Sources
18.1 Introduction
18.2 The Beta Rain Detector
18.3 Clarifying the Value of Joint Clairvoyance on Two Distinctions
18.4 Value of Information for Multiple Uncertainties
18.5 Approaching Clairvoyance with Multiple Acme Detectors
18.6 Valuing Individually Immaterial Multiple Detectors
18.7 Summary
Key Terms
Problems
Chapter 19: Options
19.1 Introduction
19.2 Contractual and Non-Contractual Options
19.3 Option Price, Exercise Price, and Option Value
19.4 Simple Option Analysis
19.5 Consequences of Failure to Recognize Options
19.6 Jane’s Party Revisited
19.7 Value of Clairvoyance as an Option
19.8 Sequential Information Options
19.9 Sequential Detector Options
19.10 Creating Options
19.11 Summary
Key Terms
Problems
Chapter 20: Detectors with Multiple Indications
20.1 Introduction
20.2 Detector with 100 Indications
20.3 The Continuous Beta Detector
20.4 Summary
Key Terms
Problems
Chapter 21: Decisions with Influences
21.1 Introduction
21.2 Shirley’s Problem
21.3 Summary
Key Terms
Problems
Chapter 22: The Logarithmic u-Curve
22.1 Introduction
22.2 The Logarithmic u-Curve
22.3 Deals with Large Monetary Prospects for a DeltaPerson
22.4 Properties of the Logarithmic u-Curve
22.5 Certain Equivalent of Two Mutually Irrelevant Deal
22.6 The St. Petersburg Paradox
22.7 Summary
Key Terms
Appendix A: The Logarithmic Function and Its Properties
Appendix B: The Risk-Aversion Function
Appendix C: A Student’s Question Following an Economist Article
Problems
Chapter 23: The Linear Risk Tolerance u-Curve
23.1 Introduction
23.2 Linear Risk Tolerance
23.3 Summary
Key Terms
Appendix A: Derivation of Linear Risk Tolerance u-Curve
Appendix B: Student’s Problem Using Linear Risk Tolerance u-Curve
Problems
Chapter 24: Aproximate Expresions for the Certain Equivalent
24.1 Introduction
24.2 Moments of a Measure
24.3 Central Moments of a Measure
24.4 Approximating the Certain Equivalent Using First and Second Central Moments
24.5 Approximating the Certain Equivalent Using Higher Order Moments
24.6 Cumulants
24.7 Summary
Key Terms
Problems
Chapter 25: Deterministic and Probabilistic Dominance
25.1 Introduction
25.2 Deterministic Dominance
25.3 First-Order Probabilistic Dominance
25.4 Second-Order Probabilistic Dominance
25.5 Dominance for Alternatives in the Party Problem
25.6 Summary
Key Terms
Problems
Chapter 26: Decisions with Multiple Attributes (1)–Ordering Prospects with Preference and Value Functions
26.1 Introduction
26.2 Step 1: Direct Vs. Indirect Values
26.3 Step 2: Ordering Prospects Characterized by Multiple “Direct Value” Attributes
26.4 Summary
Key Terms
Appendix A: Deriving the Relation Between Increments in x and y as a Function of η in the Preference Function
Problems
Chapter 27: Decisions with Multiple Attributes (2)–Value Functions for Investment Cash Flows: Time Preference
27.1 Introduction
27.2 Rules for Evaluating Investment Cash Flows
27.3 Methods Not Equivalent to the Present Equivalent
27.4 Cash Flows: A Single Measure
27.5 Summary
Key Terms
Problems
Chapter 28: Decisions With Multiple Attributes (3)–Preference Probabilities Over Value
28.1 Introduction
28.2 Stating Preference Probabilities with Two Attributes
28.3 Stating Preference Probabilities with a Value Function
28.4 Stating a u-Curve Over the Value Function
28.5 The Value Certain Equivalent
28.6 Other u-Function Approaches
28.7 Stating a u-Curve Over an Individual Attribute within the Value Function
28.8 Valuing Uncertain Cash Flows
28.9 Discussion
28.10 Summary
Key Terms
Problems
Chapter 29: Betting on Disparate Belief
29.1 Introduction
29.2 Betting on Disparate Probabilities
29.3 Practical Use
29.4 Summary
Key Terms
Problems
Chapter 30: Learning From Experimentation
30.1 Introduction
30.2 Assigning Probability of Head and Tail for the Thumbtack
30.3 Probability of Heads on Next Two Tosses
30.4 Probability of Any Number of Heads and Tails
30.5 Learning from Observation
30.6 Conjugate Distributions
30.7 Does Observing a Head Make the Probability of a Head on the Next Toss More Likely?
30.8 Another Thumbtack Demonstration
30.9 Summary
Key Terms
Problems
Chapter 31: Auctions and Biding
31.1 Introduction
31.2 Another Thumbtack Demonstration
31.3 Auctions 1 and 3 for a Deltaperson
31.4 Non-Deltaperson Analysis
31.5 The Value of the Bidding Opportunity for Auction 2
31.6 The Winner’s Curse
31.7 Summary
Key Terms
Problems
Chapter 32: Evaluating, Scaling, and Sharing Uncertain Deals
32.1 Introduction
32.2 Scaling and Sharing Risk
32.3 Scaling an Uncertain Deal
32.4 Risk Sharing of Uncertain Deals
32.5 Optimal Investment in a Portfolio
32.6 Summary
Key Terms
Appendix A: Covariance and Correlation
Appendix B: Scalar (Dot) Product of Vectors
Appendix C: 2 × 2 and 3 × 3 Matrix Multiplications and Matrix Inversion
Problems
Chapter 33: Making Risky Decisions
33.1 Introduction
33.2 A Painful Dilemma
33.3 Small Probabilities
33.4 Using Micromort Values
33.5 Applications
33.6 Facing Larger Probabilities of Death
33.7 Summary
Key Terms
Problems
Chapter 34: Decisions with a High Probability of Death
34.1 Introduction
34.2 Value Function for Remaining Life Years and Consumption
34.3 Assigning a u-Curve Over the Value Function
34.4 Determining Micromort Values
34.5 Equivalent Perfect Life Probability (EPlP)
34.6 Summary
Key Terms
Appendix A: Mortality Table for 30-Year-Old Male
Appendix B: Example of a Black Pill Calculation, x = 10,000
Appendix C: Example of a White Pill Calculation, x = 10,000
Problems
Chapter 35: Discretizing Continuous Probability Distributions
35.1 Introduction
35.2 Equal Areas Method
35.3 Caution with Discretization
35.4 Accuracy of 10–50–90 Approximate Method for Equal Areas
35.5 Moments of Discrete and Continuous Measures
35.6 Moment Matching Method
35.7 Summary
Key Terms
Appendix A: Rationale for Equal Areas Method
Problems
Chapter 36: Solving Decision Problems by Simulation
36.1 Introduction
36.2 Using Simulation for Solving Problems
36.3 Simulating Decisions Having a Single Discrete Distinction
36.4 Decisions with Multiple Discrete Distinctions
36.5 Simulating a Measure with a Continuous Distribution
36.6 Simulating Mutually Irrelevant Distinctions
36.7 Value of Information with Simulation
36.8 Simulating Multiple Distinctions with Relevance
36.9 Summary
Key Terms
Problems
Chapter 37: The Decision Analysis Cycle
37.1 Introduction
37.2 The Decision Analysis Cycle
37.3 The Model Sequence
37.4 Summary
Key Terms
Appendix A: Open Loop and Closed Loop Sensitivity for the Bidding Decision
Chapter 38: Topics in Organizational Decision Making
38.1 Introduction
38.2 Operating to Maximize Value
38.3 Issues When Operating with Budgets
38.4 Issues with Incentive Structures
38.5 A Common Issue: Multiple Specifications Vs. Tradeoffs
38.6 Need for a Corporate Risk Tolerance
38.7 Common Motivational Biases in Organizations
38.8 Summary
Key Terms
Problems
Chapter 39: Coordinating the Decision Making of Large Groups
39.1 Introduction
39.2 Issues Contributing to Poor Group Decision Making
39.3 Classifying Decision Problems
39.4 Structuring Decision Problems within Organizations
39.5 Example: The Fifth Generation Corvette
39.6 Summary
Key Terms
Chapter 40: Decisions and Ethics
40.1 Introduction
40.2 The Role of Ethics in Decision Making
40.3 Ethical Distinctions
40.4 Harming, Stealing, and Truth Telling
40.5 Ethical Codes
40.6 Ethical Situations
40.7 Summary
Key Terms
Problems
Index
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