IMPRECISE PROBABILITIES AND THEIR APPLICATIONS

30 June - 2 July 1999

** Abstract **

Bayesian advocates of expected utility maximization use sets of probability distributions to represent very different ideas. Strict Bayesians insist that probability judgment is numerically determinate even though the agent can represent such judgments only in imprecise terms. According to Quasi Bayesians rational agents may make indeterminate subjective probability judgments. Both kinds of Bayesians require that admissible options maximize expected utility according to some probability distribution. Quasi Bayesians permit the distribution to vary with the context of choice. Maximalists allow for choices that do not maximize expected utility against any distribution. Maximiners mandate what maximalists allow. This paper defends the quasi Bayesian view against strict Bayesians on the one hand and maximalists and maximiners on the other.

** Keywords. ** Keywords. Strict Bayesian, Quasi Bayesian, E-admissibility, E-maximality, Maximizing lower expectation.

The paper is available in the following formats and sites:

- Gzipped pdf file (Granada - Spain)
- pdf file (Granada - Spain)
- pdf file (Sau Paulo - Brasil)
- pdf file (Gent - Belgium)

718 Philosophy Hall

Columbia University

New York, NY 10027

USA

** E-mail addresses: **

Isaac Levi | levi@columbia.edu |

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