We study how long-lived, rational agents learn in a social network. In every period, after observing the past actions of his neighbors, each agent receives a private signal, and chooses an action whose payoff depends only on the state. Since equilibrium actions depend on higher order beliefs, it is difficult to characterize behavior. Nevertheless, we show that regardless of the size and shape of the network, the utility function, and the patience of the agents, the speed of learning in any equilibrium is bounded from above by a constant that only depends on the private signal distribution.

翻译：我们研究理性且长寿命的智能体如何在社会网络中进行学习。在每一期，每个智能体在观察到邻居过去的行为后，接收一个私有信号，并选择一个回报仅取决于状态的行动。由于均衡行动依赖于高阶信念，因此很难对行为进行刻画。尽管如此，我们证明，无论网络的大小和形状、效用函数以及智能体的耐心程度如何，任何均衡中的学习速度都受限于一个仅依赖于私有信号分布的常数。