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035   $a(NBER)w14040
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100   $a20230622d2008      fy             0
101 0 $aeng
135   $aurcnu||||||||
200 10$aBayesian Learning in Social Networks /$fDaron Acemoglu, Munther A. Dahleh, Ilan Lobel, Asuman Ozdaglar
210   $aCambridge, Mass$cNational Bureau of Economic Research$d2008
215   $a1 online resource$cillustrations (black and white);
225 1 $aNBER working paper series$vno. w14040
300   $aMay 2008.
330 3 $aWe study the perfect Bayesian equilibrium of a model of learning over a general social network. Each individual receives a signal about the underlying state of the world, observes the past actions of a stochastically-generated neighborhood of individuals, and chooses one of two possible actions. The stochastic process generating the neighborhoods defines the network topology (social network). The special case where each individual observes all past actions has been widely studied in the literature. We characterize pure-strategy equilibria for arbitrary stochastic and deterministic social networks and characterize the conditions under which there will be asymptotic learning -- that is, the conditions under which, as the social network becomes large, individuals converge (in probability) to taking the right action. We show that when private beliefs are unbounded (meaning that the implied likelihood ratios are unbounded), there will be asymptotic learning as long as there is some minimal amount of "expansion in observations". Our main theorem shows that when the probability that each individual observes some other individual from the recent past converges to one as the social network becomes large, unbounded private beliefs are sufficient to ensure asymptotic learning. This theorem therefore establishes that, with unbounded private beliefs, there will be asymptotic learning an almost all reasonable social networks. We also show that for most network topologies, when private beliefs are bounded, there will not be asymptotic learning. In addition, in contrast to the special case where all past actions are observed, asymptotic learning is possible even with bounded beliefs in certain stochastic network topologies.
410  0$aWorking Paper Series (National Bureau of Economic Research)$vno. w14040.
606   $aNoncooperative Games$2jelc
606   $aSearch ? Learning ? Information and Knowledge ? Communication ? Belief ? Unawareness$2jelc
615  7$aNoncooperative Games
615  7$aSearch ? Learning ? Information and Knowledge ? Communication ? Belief ? Unawareness
686   $aC72$2jelc
686   $aD83$2jelc
700   $aAcemoglu$b Daron$0126088
701   $aDahleh$b Munther A$01367212
701   $aLobel$b Ilan$01367213
701   $aOzdaglar$b Asuman$0497087
712 02$aNational Bureau of Economic Research.
801  0$bMaCbNBER
801  1$bMaCbNBER
906   $aBOOK
912   $a9910693954303321
996   $aBayesian Learning in Social Networks$93389958
997   $aUNINA