The role of implicit motives in strategic decision-making: Computational models of motivated learning and the evolution of motivated agents

Individual behavioral differences in humans have been linked to measurable differences in their mental activities, including differences in their implicit motives. In humans, individual differences in the strength of motives such as power, achievement and affiliation have been shown to have a significant impact on behavior in social dilemma games and during other kinds of strategic interactions. This paper presents agent-based computational models of power-, achievement- and affiliation-motivated individuals engaged in game-play. The first model captures learning by motivated agents during strategic interactions. The second model captures the evolution of a society of motivated agents. It is demonstrated that misperception, when it is a result of motivation, causes agents with different motives to play a given game differently. When motivated agents who misperceive a game are present in a population, higher explicit payoff can result for the population as a whole. The implications of these results are discussed, both for modeling human behavior and for designing artificial agents with certain salient behavioral characteristics.