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Bayesian Learning in Social Networks (2010)
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BibTeX
@MISC{Acemoglu10bayesianlearning,
author = {Daron Acemoglu and Munther A. Dahleh and Ilan Lobel and Asuman Ozdaglar},
title = {Bayesian Learning in Social Networks },
year = {2010}
}
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Abstract
We study the (perfect Bayesian) equilibrium of a model of learning over a general social network. Each individual receives a signal about the underlying state of the world, observes the past actions of a stochastically-generated neighborhood of individuals, and chooses one of two possible actions. The stochastic process generating the neighborhoods defines the network topology (social network). We characterize pure-strategy equilibria for arbitrary stochastic and deterministic social networks and characterize the conditions under which there will be asymptotic learning—convergence (in probability) to the right action as the social network becomes large. We show that when private beliefs are unbounded (meaning that the implied likelihood ratios are unbounded), there will be asymptotic learning as long as there is some minimal amount of “expansion in observations”. We also characterize conditions under which there will be asymptotic learning when private beliefs are bounded.
Keyphrases
social network bayesian learning private belief minimal amount general social network possible action social network becomes stochastically-generated neighborhood stochastic process implied likelihood ratio asymptotic learning past action right action deterministic social network pure-strategy equilibrium perfect bayesian network topology
