Increase of Agent’s Internal Complexities in Mutual Trading by Delayed Reward

Abstract

Social brain theory hypothesizes that the human brain becomes larger through evolution mainly because of reading others’ intentions in society. Reading opponents’ intentions and cooperating with them or outsmarting them results in an intelligence arms race. The authors discuss the evolution of such an arms race, represented as finite state automatons, under three distinct payoff schemes and the implications of these results, which suggest that agents increase complexity of their strategies. The analyses of the high-ranking agents’ automata suggests the process to acquire complex strategy in delayed reward condition.