Social Learning Equilibria
研究了一类广泛的社会学习模型，提出社会学习均衡这一静态均衡概念，用于刻画动态互动中的渐近均衡行为，并分析了达成一致、羊群行为和信息聚合的条件。
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.