The Taylor rule in combination with the zero lower bound on nominal rates has been shown to create an unintended liquidity-trap equilibrium. The relevance of this equilibrium has been challenged on the basis that it is not stable under least-square learning. In this paper, we show that the...

This paper proposes a model selection methodology for feedforward network models based on the genetic algorithms and makes a number of distinct but inter-related contributions to the model selection literature for the feedforward networks. First, we construct a genetic algorithm which can search...

In this paper we explore how specific aspects of market transparency and agents’ behavior affect the efficiency of the market outcome. In particular, we are interested whether learning behavior with and without information about actions of other participants improves market efficiency. We...

The ability to adjust to changes in the environment (shifts in regime) has not been demonstrated in the models of individual behavior studied in the literature. This is an important issue that has not been given much attention in the learning literature. In this paper, we study the behavior of...

We study three learning rules (reinforcement learning (RL), experience weighted attraction learning (EWA), and individual evolutionary learning (IEL)) and how they perform in three different Groves-Ledyard mechanisms. We are interested in how well these learning rules duplicate human behavior in...

This paper describes the dynamics of adaptation in a two-country, overlapping generations economy with no restrictions on foreign currency holdings. Governments of both countries finance their deficits via seignorage. Agents in this economy are boundedly rational. They use the genetic algorithm...