NATIONAL BUREAU OF ECONOMIC RESEARCH
NATIONAL BUREAU OF ECONOMIC RESEARCH

Bayesian Learning in Social Networks

Daron Acemoglu, Munther A. Dahleh, Ilan Lobel, Asuman Ozdaglar

NBER Working Paper No. 14040
Issued in May 2008
NBER Program(s):   PE

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.

download in pdf format
   (482 K)

email paper

This paper is available as PDF (482 K) or via email.

Machine-readable bibliographic record - MARC, RIS, BibTeX

Document Object Identifier (DOI): 10.3386/w14040

Published: Daron Acemoglu & Munther A. Dahleh & Ilan Lobel & Asuman Ozdaglar, 2011. "Bayesian Learning in Social Networks," Review of Economic Studies, Oxford University Press, vol. 78(4), pages 1201-1236. citation courtesy of

Users who downloaded this paper also downloaded these:
Acemoglu, Bimpikis, and Ozdaglar w16410 Dynamics of Information Exchange in Endogenous Social Networks
Ambrus, Mobius, and Szeidl w15719 Consumption Risk-sharing in Social Networks
Acemoglu, Ozdaglar, and Tahbaz-Salehi w16516 Cascades in Networks and Aggregate Volatility
Figlio, Hamersma, and Roth w16930 Information Shocks and Social Networks
Snowberg, Wolfers, and Zitzewitz w18222 Prediction Markets for Economic Forecasting
 
Publications
Activities
Meetings
NBER Videos
Data
People
About

Support
National Bureau of Economic Research, 1050 Massachusetts Ave., Cambridge, MA 02138; 617-868-3900; email: info@nber.org

Contact Us