Bayesian learning with catastrophe risk : information externalities in a large economy

Based on a previous study by Amador and Weill (2009), I study thediffusion of dispersed private information in a large economy subject to a”catastrophe risk” state. I assume that agents learn from the actions of oth-ers through two channels: a public channel, that represents learning fromprices, and a bi-dimensional private channel that represents learning from lo-cal interactions via information concerning the good state and the catastropheprobability. I show an equilibrium solution based on conditional Bayes rule,which weakens the usual condition of ”slow learning” as presented in Amadorand Weill and first introduced by Vives (1993). I study asymptotic conver-gence ”to the truth” deriving that ”catastrophe risk” can lead to ”non-linear”adjustments that could in principle explain fluctuations of price aggregates.I finally discuss robustness issues and potential applications of this work tomodels of ”reaching consensus”, ”investments under uncertainty”, ”marketefficiency” and ”prediction markets”.