Experimental Evidence on Valuation and Learning with Multiple Priors

Abstract Popular models for decision making under ambiguity assume that people use not one but multiple priors. This paper is a first attempt to experimentally elicit multiple priors. In an ambiguous scenario with two underlying states we measure a subject’s single prior, her other potential priors (multiple priors), her confidence in these priors valuation of an ambiguous asset with the same underlying states. We also investigate subjects' updating of (multiple) priors after receiving signals about the true states. We find that single priors are best understood as a confidence-weighted average of multiple priors. Single priors also predict the valuation of ambiguous assets best, while both the minimum and maximum of subjects' multiple priors add explanatory power. This provides some but no exclusive support for the maxmin (Gilboa and Schmeidler, 1989) and the alpha maxmin model (Ghirardato et al., 2004). With regard to updating of priors, we do not observe strong deviations from Bayesian learning, although subjects overadjust/underadjust their priors and their confidence in multiple priors after a contradictory/confirming signal. Subjects also react to neutral information with more confidence in their priors. This holds under ambiguity, but not in a comparison treatment under risk.