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Probabilistically Sophisticated Multiple Priors

Author

Listed:
  • Simon Grant

    (Department of Economics, Rice University)

  • Atsushi Kajii

    (Institute of Economic Research, Kyoto University)

Abstract

We characterize the intersection of the probabilistically sophisticated and multiple prior models. We show this class is strictly larger than the subjective expected utility model and that its elements can be generated from a generalized class of the -contaminated priors, which we dub the - contaminated/ -truncated prior.

Suggested Citation

  • Simon Grant & Atsushi Kajii, 2005. "Probabilistically Sophisticated Multiple Priors," KIER Working Papers 608, Kyoto University, Institute of Economic Research.
    • Handle: RePEc:kyo:wpaper:608
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    File URL: http://www.kier.kyoto-u.ac.jp/DP/DP608.pdf
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    References listed on IDEAS

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    8. Fabio Maccheroni & Massimo Marinacci & Aldo Rustichini, 2006. "Ambiguity Aversion, Robustness, and the Variational Representation of Preferences," Econometrica, Econometric Society, vol. 74(6), pages 1447-1498, November.
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    Cited by:

    1. Massimo Marinacci, 2002. "Probabilistic Sophistication and Multiple Priors," Econometrica, Econometric Society, vol. 70(2), pages 755-764, March.
    2. Tomasz, Strzalecki, 2011. "Probabilistic sophistication and variational preferences," Journal of Economic Theory, Elsevier, vol. 146(5), pages 2117-2125, September.
    3. Jan Werner, 2009. "Risk and risk aversion when states of nature matter," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 41(2), pages 231-246, November.
    4. Werner, Jan, 2011. "Risk aversion for variational and multiple-prior preferences," Journal of Mathematical Economics, Elsevier, vol. 47(3), pages 382-390.

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    More about this item

    Keywords

    subjective probability; maximin expected utility; epsilon-contamined priors.;
    All these keywords.

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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