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Ambiguity and partial Bayesian updating

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  • Matthew Kovach

    (Purdue University)

Abstract

Models of updating a set of priors either do not allow a decision maker to make inference about her priors (full bayesian updating or FB) or require an extreme degree of selection (maximum likelihood updating or ML). I characterize a general method for updating a set of priors, partial bayesian updating (PB), in which the decision maker (1) utilizes an event-dependent threshold to determine whether a prior is likely enough, conditional on observed information, and then (2) applies Bayes’ rule to the sufficiently likely priors. I show that PB nests FB and ML and explore its behavioral properties.

Suggested Citation

  • Matthew Kovach, 2024. "Ambiguity and partial Bayesian updating," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 78(1), pages 155-180, August.
    • Handle: RePEc:spr:joecth:v:78:y:2024:i:1:d:10.1007_s00199-023-01528-7
    DOI: 10.1007/s00199-023-01528-7
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    Keywords

    Ambiguity aversion; Dynamic consistency; Full Bayesian updating; Maximum likelihood updating; Partial Bayesian updating;
    All these keywords.

    JEL classification:

    • D01 - Microeconomics - - General - - - Microeconomic Behavior: Underlying Principles
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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