Modeling Social Heterogeneity with Genetic Programming in an Artificial Double Auction Market

Abstract

Individual differences in intellectual abilities can be observed across time and everywhere in the world, and this fact has been well studied by psychologists for a long time. To capture the innate heterogeneity of human intellectual abilities, this paper employs genetic programming as the algorithm of the learning agents, and then proposes the possibility of using population size as a proxy parameter of individual intelligence. 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.