Mechanisms of Cooperative Dynamics Emerging in Interrole Conflict Situations

Abstract: This study explores how people manage conflict when they belong to multiple groups and decide which groups to support at a given time. We introduced a computer simulation called the interrole conflict game to mimic real-life situations in which individuals face competing demands. To represent the differences between adolescence and adulthood, we used Recurrent Q-Learning (RQL) agents that adjusted behavior based on past experiences. Our findings show that an agent’s ability to cooperate depends on how well it can predict future actions. When agents have a limited memory, they perform better by focusing on immediate rewards. In contrast, when agents can remember more information, they can plan ahead and work together more effectively over time. We also observed that gradual adjustments in the learning process using a low learning rate helped the agents become more stable and cooperative. This study provides insight into how short-term reactions and long-term planning can be balanced to improve cooperation in complex social settings.