Revealed Bayesian Expected Utility with Limited Data

Abstract

This paper studies when stochastic choices are consistent with behavior from Bayesian expected utility maximization and information acquisition. This is a limited dataset since other characterization require state-dependent stochastic choice data. The conditions that characterize this behavior are similar to the Wald-Bernheim-Pearce lemma and essentially requires that there is no random deviation rule that improves ex-ante expected utility for all possible information structures consistent with the observed stochastic choices. I extend the result to place bounds on an unknown prior and facilitate welfare comparisons in the presence of framing. These bounds are computable via linear programming.