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- W2012109092 abstract "We study the learning problem of a set of agents connected via a general social network. We analyze the (perfect Bayesian) equilibrium of a dynamic game where each agent receives a signal about an 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 equilibria for arbitrary stochastic and deterministic social networks and characterize the conditions under which there will be asymptotic learning—that is, the conditions under which, as the social network becomes large, individuals converge (in probability) to taking the right action. 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 (in particular, as long as the probability that each individual observes some other from the recent past converges to one as the social network becomes large). This result therefore establishes that, with unbounded private beliefs, there will be asymptotic learning in almost all reasonable social networks. We also show that for a large class of network topologies, when private beliefs are bounded, there will not be asymptotic learning. Additionally, we show that learning is possible even with bounded beliefs in certain stochastic social networks." @default.
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- W2012109092 date "2009-01-01" @default.
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- W2012109092 title "Learning Over Complex Social Networks [Extended Abstract]" @default.
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- W2012109092 doi "https://doi.org/10.3182/20090706-3-fr-2004.00128" @default.
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