CHARISMA: Character-Based Interaction Simulation with Multi-LLM Agents Toward Computational Social Psychology

How people seek, request, and exchange information in social interactions is shaped by personality and situational context, connecting the fields of interactive information science and attribution theory in social psychology. In everyday life, people seek information to achieve goals, collaborate, and manage social conflicts. Understanding how individual traits and contextual factors influence information-seeking behavior remains a challenge. Recent advances with large language models (LLMs) enable the simulation of socially grounded information-seeking behaviors in realistic and controllable ways. We introduce CHARISMA, a simulation framework that uses LLMs to examine how personality traits and situational factors influence information seeking as a form of social behavior. CHARISMA leverages movie characters and public figures as personality anchors, drawing on LLMs’ knowledge to simulate human-like interaction. CHARISMA’s utility is demonstrated in two studies: (1) agreeable pairs resolve conflicts more successfully, and (2) low-agreeable agents compete for information, while high-agreeable agents cooperate through prosocial exchange.