A model of boundedly rational learning in dynamic games

Abstract

There are various computer-based algorithms about boundedly rational players’ learning how to behave in dynamic games, including classifier systems, genetic algorithms and neural networks. Some examples of studies using boundedly rational players are Axelrod (1987), Miller (1989), Andreoni and Miller (1990) who use genetic algorithm and Marimon etal. (1990) and Arthur (1990) who use classifier systems. In this dissertation, a Two Armed Bandit Problem and the KiyotakiWright (1989) Economic Environment are constructed and the learning behaviour ol the boundedly rational players is observed by using classifier systems in computer programs. From the simulation results, we observe that experimentation and imitation enables faster convergence to the correct decision rules of players in both repeated static decision problems and dynamic games.