The payoff matrix of a finite stage game is realized randomly, and then the stage game is repeated infinitely. The distribution over states of the world (a state corresponds to a payoff matrix) is commonly known, but players do not observe nature’s choice. Over time, they can learn the state...

We experimentally implement a dynamic public-good problem, where the public good in question is the dynamically evolving information about agents' common state of the world. Subjects' behavior is consistent with free-riding because of strategic concerns. We also find that subjects adopt more...

Social learning often occurs between groups with different levels of experience. Yet little is known about the ideal behavioral rules in such contexts. Existing insights only apply when individuals learn from each other in the same group. In this paper, we close this gap and consider two groups,...

While payoff-based learning models are almost exclusively devised for finite action games, where players can test every action, it is harder to design such learning processes for continuous games. We construct a stochastic learning rule, designed for games with continuous action sets, which...

A learning rule is uncoupled if a player does not condition his strategy on the opponent's payoffs. It is radically uncoupled if a player does not condition his strategy on the opponent's actions or payoffs. We demonstrate a family of simple, radically uncoupled learning rules whose...

Under the assumption that individuals know the conditional distributions of signals given the payoff-relevant parameters, existing results conclude that as individuals observe infinitely many signals, their beliefs about the parameters will eventually merge. We first show that these results are...

We study perfect Bayesian equilibria of a sequential social learning model in which agents in a network learn about an underlying state by observing neighbors' choices. In contrast with prior work, we do not assume that the agents' sets of neighbors are mutually independent. We introduce a new...