STRATEGIC LEARNING AND THE TOPOLOGY OF SOCIAL NETWORKS

We consider a group of strategic agents who must each repeatedly take one of two possible actions. They learn which of the two actions is preferable from initial private signals and by observing the actions of their neighbors in a social network. We show that the question of whether or not the agent...

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Bibliographic Details
Published in:Econometrica 2015-09, Vol.83 (5), p.1755-1794
Main Authors: Mossel, Elchanan, Sly, Allan, Tamuz, Omer
Format: Article
Language:English
Subjects:
Agency theory
aggregation of information
Behavioral decision theory
Equilibrium
Estimators
informational externalities
Mathematical theorems
Neighborhoods
Observational learning
Random variables
Social learning
Social networking
Social networks
Studies
Topological spaces
Topological theorems
Topology
Vertices
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Summary:We consider a group of strategic agents who must each repeatedly take one of two possible actions. They learn which of the two actions is preferable from initial private signals and by observing the actions of their neighbors in a social network. We show that the question of whether or not the agents learn efficiently depends on the topology of the social network. In particular, we identify a geometric "egalitarianism" condition on the social network that guarantees learning in infinite networks, or learning with high probability in large finite networks, in any equilibrium. We also give examples of nonegalitarian networks with equilibria in which learning fails.
ISSN:0012-9682
1468-0262
DOI:10.3982/ECTA12058