| Summary: | Based on a previous study by Amador and Weill (2009), I study the
diffusion 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 from
prices, and a bi-dimensional private channel that represents learning from lo-
cal interactions via information concerning the good state and the catastrophe
probability. I show an equilibrium solution based on conditional Bayes rule,
which weakens the usual condition of ”slow learning” as presented in Amador
and 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 to
models of ”reaching consensus”, ”investments under uncertainty”, ”market
efficiency” and ”prediction markets”. === text
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