Strategic Learning and the Topology of Social Networks

Understand

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.