03248oam 22004093a 450 991069395430332120230622022652.0(NBER)w14040(CKB)3240000000014019(EXLCZ)99324000000001401920230622d2008 fy 0engurcnu||||||||Bayesian Learning in Social Networks /Daron Acemoglu, Munther A. Dahleh, Ilan Lobel, Asuman OzdaglarCambridge, MassNational Bureau of Economic Research20081 online resourceillustrations (black and white);NBER working paper seriesno. w14040May 2008.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). The special case where each individual observes all past actions has been widely studied in the literature. We characterize pure-strategy 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". Our main theorem shows that when the probability that each individual observes some other individual from the recent past converges to one as the social network becomes large, unbounded private beliefs are sufficient to ensure asymptotic learning. This theorem therefore establishes that, with unbounded private beliefs, there will be asymptotic learning an almost all reasonable social networks. We also show that for most network topologies, when private beliefs are bounded, there will not be asymptotic learning. In addition, in contrast to the special case where all past actions are observed, asymptotic learning is possible even with bounded beliefs in certain stochastic network topologies.Working Paper Series (National Bureau of Economic Research)no. w14040.Noncooperative GamesjelcSearch • Learning • Information and Knowledge • Communication • Belief • UnawarenessjelcNoncooperative GamesSearch • Learning • Information and Knowledge • Communication • Belief • UnawarenessC72jelcD83jelcAcemoglu Daron126088Dahleh Munther A1367212Lobel Ilan1367213Ozdaglar Asuman497087National Bureau of Economic Research.MaCbNBERMaCbNBERBOOK9910693954303321Bayesian Learning in Social Networks3389958UNINA