Dynamic Personality in LLM Agents: A Framework for Evolutionary Modeling and Behavioral Analysis in the Prisoner’s Dilemma
Weiqi Zeng, Bo Wang, Dongming Zhao, Zongfeng Qu, Ruifang He, Yuexian Hou, Qinghua Hu
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.
BibTeX
@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://aclanthology.org/2025.findings-acl.1185/",
doi = "10.18653/v1/2025.findings-acl.1185",
pages = "23087--23100",
ISBN = "979-8-89176-256-5"
}