Observational learning in large anonymous games
研究了在收益相互依赖的大型匿名博弈中，代理人通过观察他人行动进行学习的过程，发现当信号强度无界时，行动在事后最优。
I present a model of observational learning with payoff interdependence. Agents, ordered in a sequence, receive private signals about an uncertain state of the world and sample previous actions. Unlike in standard models of observational learning, an agent's payoff depends both on the state and on the actions of others. Agents want both to learn the state and to anticipate others' play. As the sample of previous actions provides information on both dimensions, standard informational externalities are confounded with payoff externalities. I show that in spite of these confounding factors, when signals are of unbounded strength, there is learning in a strong sense: agents' actions are ex post optimal given both the state of the world and others' actions. With bounded signals, actions approach ex post optimality as the signal structure becomes more informative.