WP 2020-11 Belief Distributions, Bayes Rule and Bayesian Overconfidence

Posted On July 21, 2020
Categories Working Papers, WP 2020

Download Paper

AUTHORS: Glenn W. Harrison and J. Todd Swarthout

ABSTRACT: We provide evidence that individuals generally behave consistently with Bayes Rule defined over continuous events in the sense that they report subjective belief distributions that are unbiased, but that they behave inconsistently in the sense that they exhibit significant overconfidence compared to the appropriate Bayesian confidence as defined by the variance of the posterior distribution. Only by eliciting distributions are we able to assess the dispersion of an individual’s beliefs, giving us a natural metric for subjective confidence. The evidence involves the elicitation of beliefs with financial incentives, and tests Bayes Rule in a setting in which the posterior distribution is known for each individual and belief elicitation. Previous tests of the consistency of beliefs with Bayes Rule were not designed to independently test bias and overconfidence.