This talk considers social learning settings in which a group of agents face uncertainty regarding a state of the world, observe private signals, share the same utility function, and act in general dynamic setting. Tamuz introduces Social Learning Equilibria, a static equilibrium concept that abstracts away from the details of the given dynamics, but nevertheless captures the corresponding asymptotic equilibrium behavior. He establishes strong equilibrium properties on agreement, herding, and information aggregation.
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