We report experiments designed to test the theoretical possibility, first discovered by Shapley (1964), that in some games learning fails to converge to any equilibrium, either in terms of marginal frequencies or of average play. Subjects played repeatedly in fixed pairings one of two 3 × 3...

A well known problem in economics is to describe properly a situation where N agents are repeatedly competing to use the same limited resource. A version of this problem is known in the literature as the El Farol game: week after week N agents face the decision whether to go or not to go to a...

To isolate the impact of the assumption of model-consistent expectations, this paper proposes a baseline case in which households are individually rational, have full information and learn using forecast rules specified as in the minimum state variable representation of the economy. Applying...

We develop a learning rule that generalises the well known fading memory learning in the sense that the weights attached to the available time series data are not constant and are updated in light of the prediction error(s). The underlying idea is that confidence in the available data will be...

Most studies of optimal monetary policy under learning rely on optimality conditions derived for the case when agents have rational expectations. In this paper, we derive optimal monetary policy in an economy where the Central Bank knows, and makes active use of, the learning algorithm agents...

Under the assumption of bounded rationality, economic agents learn from their past mistaken predictions by combining new and old information to form new beliefs. The purpose of this paper is to examine how the policy-maker, by affecting private agents' learning process, determines the speed at...

We investigate games whose Nash equilibria are mixed and are unstable under fictitious play-like learning processes. We show that when players learn using weighted stochastic fictitious play and so place greater weight on more recent experience that the time average of play often converges in...

We propose a simple payoff-based learning rule that is completely decentralized, and that leads to an efficient configuaration of actions in any n-person finite strategic-form game with generic payoffs.  The algorithm follows the theme of exploration versus exploitation and is hence stochastic...

We exploit a unique opportunity to study how a large population of players in the field learn to play a novel game with a complicated and non-intuitive mixed strategy equilibrium.  We argue that standard models of belief-based learning and reinforcement learning are unable to explain the data,...

In this paper we investigate how cognitive ability and character skills influence behavior, success and the evolution of play towards Nash equilibrium in repeated strategic interactions.  We study behavior in a p-beauty contest experiment and find striking differences according to cognitive...