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...

A generic property of biological, social and economical networks is their ability to evolve in time, creating or supressing links. We model this situation with an adaptive network of agents playing a Prisoner's Dilemma game. Each agent plays with its local neighbors, collects an aggregate payoff...

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 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...

While under recursive least squares learning the dynamics of the economy converges to rational expectations equilibria (REE) which are E–stable, some recent examples propose that E–stability is not a sufficient condition for learnability. In this paper, we provide some further...

We provide sufficient conditions and necessary conditions for stability of an economy under structural mixed recursive least squares/stochastic gradient heterogeneous learning of agents with possibly different degrees of inertia. We have found a unifying condition which is sufficient for...