An outcome is ambiguous if it is an incomplete description of the probability distribution over consequences. An `incomplete description' is identified with the set of probabilities that satisfy the incomplete description. A choice problem is uncertain if the decision maker is choosing between distributions, and is ambiguous if the decision maker is choosing between sets of probabilities. The von Neumann/Morgenstern approach to uncertain choice problems uses a continuous linear function on probabilities. This paper develops the theory of ambiguous choice problems as a continuous, linear functions on closed convex sets of probabilities. This delivers: a framework encompassing most of the extant ambiguity averse preferences; a complete separation of attitudes towards risk and attitudes toward ambiguity; and generalizations of rst and second order stochastic dominance rankings to ambiguous decision problem. Quasi-concave preferences on sets that satisfy a restricted betweenness property capture variational preferences. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/ETD-UT-2011-08-4292 |
| Date | 27 February 2012 |
| Creators | Dumav, Martin |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
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