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Hypothesis Testing and Ambiguity Aversion

Author

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  • Pietro Ortoleva

    (Columbia University, New York)

Abstract

We study a model of non-Bayesian updating for ambiguity averse agents, based on the Hypothesis Testing model of Ortoleva (2012). Agents have a set of priors that they update as follows. If all priors assign to new information a probability above a threshold, they update every prior using Bayes rule. Otherwise: they look at a prior over sets of priors, update it, and choose the set to which the prior over sets of priors assigns the highest likelihood. When the threshold is zero this coincides with Bayesian updating when defined, but it also prescribes behavior when it is not defined.

Suggested Citation

  • Pietro Ortoleva, 2014. "Hypothesis Testing and Ambiguity Aversion," Rivista di Politica Economica, SIPI Spa, issue 3, pages 45-64, July-Sept.
    • Handle: RePEc:rpo:ripoec:y:2014:i:3:p:45-64
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    Citations

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    Cited by:

    1. Hill, Brian, 2022. "Updating confidence in beliefs," Journal of Economic Theory, Elsevier, vol. 199(C).
    2. Cheng, Xiaoyu, 2022. "Relative Maximum Likelihood updating of ambiguous beliefs," Journal of Mathematical Economics, Elsevier, vol. 99(C).
    3. Xiaoyu Cheng, 2019. "Relative Maximum Likelihood Updating of Ambiguous Beliefs," Papers 1911.02678, arXiv.org, revised Oct 2021.

    More about this item

    Keywords

    Bayes’ rule; updating; dynamic coherence; dynamic consistency; unambiguous preferences; ambiguity aversion.;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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