Modeling Intelligence of Learning Agents in An Artificial Double Auction Market

Abstract

In psychological as well as socioeconomic studies, individual intelligence has been found decisive in many domains. In this paper, we employ genetic programming as the algorithm of our learning agents who compete with other designed strategies extracted from the literature.We then discuss the possibility of using population size as a proxy parameter of individual intelligence of software agents. By modeling individual intelligence in this way, we demonstrate not only a nearly positive relation between individual intelligence and performance, but more interestingly the effect of decreasing marginal contribution of IQ to performance found in psychological literature.