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
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/ETD-UT-2011-08-3868 |
| Date | 30 September 2011 |
| Creators | Zantedeschi, Daniel |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
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