Estimating Bayesian Decision Problems with Heterogeneous Priors

In many areas of economics there is a growing interest in how expertise and preferences drive individual and group decision making under uncertainty. Increas- ingly, we wish to estimate such models to quantify which of these drive decision making. In this paper we propose a new channel through which we can empiri- cally identify expertise and preference parameters by using variation in decisions over heterogeneous priors. Relative to existing estimation approaches, our \Prior- Based Identiffication" extends the possible environments which can be estimated, and also substantially improves the accuracy and precision of estimates in those environments which can be estimated using existing methods.