We propose an extension to smooth fictitious play and prove that play converges to an ε-Markov perfect equilibrium with probability one in a class of stochastic games known as Markov potential games. We then prove a partial Folk theorem for repeated games under one-period perfect monitoring. We...

This paper investigates the way in which adaptive players behave in the long run in finitely repeated games. Each player assigns subjective payoff assessments to his own actions and chooses the action which has the highest assessment at each of his information sets. After receiving payoffs,...

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...

We present experiments on repeated non-cooperative network formation games, based on Bala and Goyal (2000). We treat the one-way and the two-ways flow models, each for high and low link costs. The models show both multiple equilibria and coordination problems. We conduct experiments under...

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...

This paper studies a novel setting in game theory: a player may learn new actions over time by observing the opponent's play. We investigate the impact of such learning behavior in the context of finitely repeated games. In contrast to related literature such as Kreps et al. (1982), we provide a...

Psychologists have long recognized two kinds of learning: one that is relatively shallow and domain-specific; and another that is deeper, producing generalizable insights that transfer across domains. The game theory literature has only recently considered this distinction, and the conditions...