@inproceedings{zeng-etal-2025-dynamic,
    title = "Dynamic Personality in {LLM} Agents: A Framework for Evolutionary Modeling and Behavioral Analysis in the Prisoner{'}s Dilemma",
    author = "Zeng, Weiqi  and
      Wang, Bo  and
      Zhao, Dongming  and
      Qu, Zongfeng  and
      He, Ruifang  and
      Hou, Yuexian  and
      Hu, Qinghua",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.findings-acl.1185/",
    doi = "10.18653/v1/2025.findings-acl.1185",
    pages = "23087--23100",
    ISBN = "979-8-89176-256-5",
    abstract = "Using Large Language Model agents to simulate human game behaviors offers valuable insights for human social psychology in anthropomorphic AI research. While current models rely on static personality traits, real-world evidence shows personality evolves through environmental feedback. Recent work introduced dynamic personality traits but lacked natural selection processes and direct psychological metrics, failing to accurately capture authentic dynamic personality variations. To address these limitations, we propose an enhanced framework within the Prisoner{'}s Dilemma, a socially significant scenario. By using game payoffs as environmental feedback, we drive adaptive personality evolution and analyze correlations between personality metrics and behavior. Our framework reveals new behavioral patterns of agents and evaluates personality-behavior relationships, advancing agent-based social simulations and human-AI symbiosis research."
}