Short Memory-Based Human Strategy Modeling in Social Dilemmas

Abstract

Human decision-making processes are complex. It is thus challenging to mine human strategies from real games in social networks. To model human strategies in social dilemmas, we conducted a series of human subject experiments in which the temporal two-player non-cooperative games among 1092 players were intensively investigated. Our goal is to model the individuals’ moves in the next round based on the information observed in each round. Therefore, the developed model is a strategy model based on short-term memory. Due to the diversity of user strategies, we first cluster players’ behaviors to aggregate them with similar strategies for the following modeling. Through behavior clustering, our observations show that the performance of the tested binary strategy models can be highly promoted in the largest behavior groups. Our results also suggest that no matter whether in the classical mode or the dissipative mode, the influence of individual accumulated payoffs on individual behavior is more significant than the gaming result of the last round. This result challenges a previous consensus that individual moves largely depend on the gaming result of the last round. Therefore, our model provides a novel perspective for understanding the evolution of human altruistic behavior.