Social learning equilibria

TL;DR

This paper introduces Social Learning Equilibria, a static concept capturing the long-term behavior of agents in dynamic social learning models, and explores conditions for agreement, herding, and information aggregation.

Contribution

It proposes a new equilibrium concept that simplifies analysis of social learning dynamics and establishes conditions linking agreement and information aggregation.

Findings

Conditions for agreement and herding are established.

Social Learning Equilibria effectively capture asymptotic behavior.

Connections between agreement and information aggregation are highlighted.

Abstract

We consider a large class of social learning models in which a group of agents face uncertainty regarding a state of the world, share the same utility function, observe private signals, and interact in a general dynamic setting. We introduce Social Learning Equilibria, a static equilibrium concept that abstracts away from the details of the given extensive form, but nevertheless captures the corresponding asymptotic equilibrium behavior. We establish general conditions for agreement, herding, and information aggregation in equilibrium, highlighting a connection between agreement and information aggregation.