Strategic Learning and the Topology of Social Networks

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.

arXiv comment30 pages, one figure

References in corpus (2)

• Strategic Learning and the Topology of Social Networks
• Asymptotic Learning on Bayesian Social Networks

Cited by in corpus (7)

• Strategic Learning and the Topology of Social Networks
• Opinion Exchange Dynamics
• Social learning equilibria
• Network Structure and Naive Sequential Learning
• Efficient Bayesian Learning in Social Networks with Gaussian Estimators
• Collective decision-making under changing social environments among agents adapted to sparse connectivity
• Bayesian Decision Making in Groups is Hard