Ambiguity and partial Bayesian updating

Matthew Kovach

Economic Theory, 2024, vol. 78, issue 1, No 6, 155-180

Abstract: 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.

Keywords: Ambiguity aversion; Dynamic consistency; Full Bayesian updating; Maximum likelihood updating; Partial Bayesian updating (search for similar items in EconPapers)
JEL-codes: D01 D81 D83 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00199-023-01528-7

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