This paper studies fictitious play in networks of noncooperative two-player games. We show that continuous-time fictitious play converges to Nash equilibrium provided that the overall game is zero-sum. Moreover, the rate of convergence is 1/T , regardless of the size of the network. In contrast,...

This paper studies fictitious play in networks of noncooperative two-person games. We show that continuous-time fictitious play converges to the set of Nash equilibria if the overall n-person game is zero-sum. Moreover, the rate of convergence is 1/T, regardless of the size of the network. In...

We use an experiment to explore how subjects learn to play against computers which are programmed to follow one of a number of standard learning algorithms. The learning theories are (unbeknown to subjects) a best response process, fictitious play, imitation, reinforcement learning, and a trial...

We prove that, in all finite generic extensive-form games of perfect information, a continuous-time best response dynamic always converges to a Nash equilibrium component. We show the robustness of convergence by an approximate best response dynamic: whatever the initial state and an allowed...

In this paper I study the economics of self-enforcing international environmental agreements where agents never know what exactly the state of the world is. Explicitly, I consider countries using Bayesian learning to update their beliefs on the state of the world. Using a very simple framework...

Recently there has been much work on learning in games. However, learning usually means learning about behavior of opponents rather than learning about the game as such. Here we test in an experiment whether players in a repeated encounter can learn the payoff structures of their opponents by...

We introduce the algorithmic learning equations (ALEs), a set of ordinary differential equations which characterizes the finite-time and asymptotic behaviour of the stochastic interaction between state-dependent learning algorithms in dynamic games. Our framework allows for a variety of...

Recently there has been much work on learning in games. However, learning usually means "learning about behavior of opponents" rather than "learning about the game" as such. Here we test in an experiment whether players in a repeated encounter can learn the payoff structures of their opponents...