Cyclical or chaotic competitive equilibria that do not exist under perfect foresight are shown to occur in a decentralized growth model under constant gain adaptive learning. This paper considers an economy populated by boundedly rational households making one-period ahead constant gain adaptive...

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...

Bounded rationality is introduced into a standard growth model by assuming that households form one-period ahead least squares forecasts on production factor prices, and expect that future level of consumption and physical capital will be consistent with the balanced growth path. Under those...

This paper explores the question whether boundedly rational agents learn to behave optimally when asked to voluntarily contribute to a public good. The decision process of individuals is described by an Evolutionary Algorithm. We find that the contribution level converges towards the Nash...

In this paper, an agent-based computational capital asset pricing model is applied to address an issue, known as the elasticity puzzle, originating from a famous reciprocal relation between the elasticity of intertemporal substitution (EIS) and the relative risk aversion (RRA) coefficient. By...

This paper attempts to generalise some results obtained in previous work showing the conditions under which paradigm setters emerge. We distinguish two different but definitely complementary and overlapping ways through which searching and learning occur. The first exploits the spillover...

Many models of monetary policy predict a trade-off between inflation and output variance despite compelling evidence that the Federal Reserve has become more aggressive in fighting inflation and there has been a resulting decline in both inflation and output variance. We address this apparent...

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...