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Ambiguity and Partial Bayesian Updating

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  • Matthew Kovach

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 (i) utilizes an event-dependent threshold to determine whether a prior is likely enough, conditional on observed information, and then (ii) applies Bayes' rule to the sufficiently likely priors. I show that PB nests FB and ML and explore its behavioral properties.

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  • Matthew Kovach, 2021. "Ambiguity and Partial Bayesian Updating," Papers 2102.11429, arXiv.org, revised Mar 2023.
    • Handle: RePEc:arx:papers:2102.11429
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    References listed on IDEAS

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    Cited by:

    1. Brian Hill, 2022. "Updating confidence in beliefs," Post-Print hal-03503986, HAL.
    2. Yi-Hsuan Lin & Fernando Payró Chew, 2024. "Updating Under Imprecise Information," Working Papers 1424, Barcelona School of Economics.

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