Book contents
- Frontmatter
- Contents
- List of contributors
- Preface
- 1 Introduction: Bayesian decision theory – foundations and problems
- Part I Foundations of Bayesian decision theory
- Part II Conceptualization of probability and utility
- Part III Questionable rules of rationality
- Part IV Unreliable probabilities
- 13 Risk, ambiguity, and the Savage axioms
- 14 Self-knowledge, uncertainty, and choice
- 15 On indeterminate probabilities
- 16 Unreliable probabilities, risk taking, and decision making
- Part V Causal decision theory
- References
- Name index
- Subject index
13 - Risk, ambiguity, and the Savage axioms
Published online by Cambridge University Press: 05 June 2012
- Frontmatter
- Contents
- List of contributors
- Preface
- 1 Introduction: Bayesian decision theory – foundations and problems
- Part I Foundations of Bayesian decision theory
- Part II Conceptualization of probability and utility
- Part III Questionable rules of rationality
- Part IV Unreliable probabilities
- 13 Risk, ambiguity, and the Savage axioms
- 14 Self-knowledge, uncertainty, and choice
- 15 On indeterminate probabilities
- 16 Unreliable probabilities, risk taking, and decision making
- Part V Causal decision theory
- References
- Name index
- Subject index
Summary
Are there uncertainties that are not risks?
There has always been a good deal of skepticism about the behavioral significance of Frank Knight's distinction between “measurable uncertainty” or “risk”, which may be represented by numerical probabilities, and “unmeasurable uncertainty” which cannot. Knight maintained that the latter “uncertainty” prevailed – and hence that numerical probabilities were inapplicable – in situations when the decision-maker was ignorant of the statistical frequencies of events relevant to his decision; or when a priori calculations were impossible; or when the relevant events were in some sense unique; or when an important, once-and-for-all decision was concerned.
Yet the feeling has persisted that, even in these situations, people tend to behave “as though” they assigned numerical probabilities, or “degrees of belief,” to the events impinging on their actions. However, it is hard either to confirm or to deny such a proposition in the absence of precisely-defined procedures for measuring these alleged “degrees of belief.”
What might it mean operationally, in terms of refutable predictions about observable phenomena, to say that someone behaves “as if” he assigned quantitative likelihoods to events: or to say that he does not? An intuitive answer may emerge if we consider an example proposed by Shackle, who takes an extreme form of the Knightian position that statistical information on frequencies within a large, repetitive class of events is strictly irrelevant to a decision whose outcome depends on a single trial.
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- Information
- Decision, Probability and UtilitySelected Readings, pp. 245 - 269Publisher: Cambridge University PressPrint publication year: 1988
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