  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.
