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Strategic Learning and the Topology of Social Networks

Textual materials

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Disciplines
KeywordsTriple Keywords
Person (Philosophy)
Agents
Agent (Philosophy)
Agency (Philosophy)
Equality
Egalitarianism
Social inequality
Social equality
Inequality
Learning
Learning process

Abstract

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 non-egalitarian networks with equilibria in which learning fails.

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