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,...

In this paper we study a large class of non-atomic games arising from interactions on the Internet, such as many users sharing a network link, researchers accessing a database or web server, subscribers to a network services provider trying to gain access to the modem pool and many more. We...

This paper describes the results of simulation experiments performed on a suite of learning algorithms. We focus on games in network contexts. These are contexts in which (1) agents have very limited information about the game; users do not know their own (or any other agent's) payoff function,...

A representative consumer uses Bayes' law to learn about parameters of several models and to construct probabilities with which to perform ongoing model averaging. The arrival of signals induces the consumer to alter his posterior distribution over models and parameters. The consumer's...

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...