Genetic algorithms as a model of adaptive learning in economic systems

In this paper Genetic Algorithms (GAs) are used to model the adaptive behavior of a population of bounded rational agents interacting within an economic system. We show that GAs may be an economically meaningful model of adaptive learning if we consider a learning process incorporating imitation, communication and innovation effects. We discuss the underlying economic assumptions of such an interpretation and present a precise mathematical description of the behavior of GAs in economic systems. Key analytical results for this mathematical model are presented and compared to results from the related replicator dynamics. In particular we show that contrary to the replicator dynamics not every locally stable point is an equilibrium.