Book contents
- Frontmatter
- Contents
- Editor's Statement
- Foreword
- Preface
- Measurement Theory
- Introduction
- Chapter 1 Relations
- Chapter 2 Fundamental Measurement, Derived Measurement, and the Uniqueness Problem
- Chapter 3 Three Representation Problems: Ordinal, Extensive, and Difference Measurement
- Chapter 4 Applications to Psychophysical Scaling
- Chapter 5 Product Structures
- Chapter 6 Nontransitive Indifference, Probabilistic Consistency, and Measurement without Numbers
- Chapter 7 Decisionmaking under Risk or Uncertainty
- Chapter 8 Subjective Probability
- Author Index
- Subject Index
Chapter 7 - Decisionmaking under Risk or Uncertainty
Published online by Cambridge University Press: 04 August 2010
- Frontmatter
- Contents
- Editor's Statement
- Foreword
- Preface
- Measurement Theory
- Introduction
- Chapter 1 Relations
- Chapter 2 Fundamental Measurement, Derived Measurement, and the Uniqueness Problem
- Chapter 3 Three Representation Problems: Ordinal, Extensive, and Difference Measurement
- Chapter 4 Applications to Psychophysical Scaling
- Chapter 5 Product Structures
- Chapter 6 Nontransitive Indifference, Probabilistic Consistency, and Measurement without Numbers
- Chapter 7 Decisionmaking under Risk or Uncertainty
- Chapter 8 Subjective Probability
- Author Index
- Subject Index
Summary
The Expected Utility Rule and the Expected Utility Hypothesis
In this chapter, we consider for the first time a situation of decisionmaking under uncertainty. We allow for the possibility that one of a set of uncertain events may occur, each with a certain probability, and each with a known consequence. We face such decisionmaking problems often in our lives. For example, when we consider whether or not to buy insurance before we are 30 years old, we consider the possible but uncertain event of death. A doctor often faces a choice among alternative treatments, with different uncertain side effects. A government must spend money on one of several technologies designed to solve a problem, each of which has only a certain probability of providing the desired results. (For example: What design should we use for a rapid transit system? Should we invest large amounts of money on breeder reactor research, rather than on solar power? And so on.)
To motivate our discussion of such decisionmaking problems, let us consider a simple gambling situation. Suppose you are attending a business meeting and have a choice between parking your car at a meter or putting it in a parking lot. The meter can take coins for up to 2 hours–the maximum would require $1. The meter is monitored almost constantly, and the fine for overtime parking is $25. The lot would be $5, a flat fee. You think that the chance the meeting will be over within 2 hours is 80%.
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- Measurement TheoryWith Applications to Decisionmaking, Utility, and the Social Sciences, pp. 305 - 368Publisher: Cambridge University PressPrint publication year: 1984