Ambiguity and Partial Bayesian Updating

TL;DR

This paper introduces a new partial Bayesian updating method that balances between full Bayesian and maximum likelihood updating, allowing more flexible inference about priors based on event-dependent thresholds.

Contribution

It characterizes a general updating method, partial Bayesian updating, which encompasses both full Bayesian and maximum likelihood updating, and explores its behavioral implications.

Findings

PB nests FB and ML updating methods.

PB allows inference about priors using event-dependent thresholds.

Behavioral properties of PB are analyzed.

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.