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Ambiguity and partial Bayesian updating

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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.

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  1. Department of Economics, Purdue University, West Lafayette, USA

    Matthew Kovach

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  1. Matthew Kovach
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Correspondence to Matthew Kovach.

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I would like to thank Pietro Ortoleva and Federico Echenique for their guidance and support through all stages of this project. I would also like to thank Adam Dominiak, Kota Saito, Euncheol Shin, Hector Tzavellas, and Gerelt Tserenjigmid. Much of this work is derived from my second-year paper in the Social Science PhD program at Caltech. All errors are my own.

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Kovach, M. Ambiguity and partial Bayesian updating. Econ Theory 78, 155–180 (2024). https://doi.org/10.1007/s00199-023-01528-7

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