This study investigates the potential of Large Language Models (LLMs) to simulate human group dynamics, particularly within politically charged contexts. We replicate the Wisdom of Partisan Crowds phenomenon using LLMs to role-play as Democrat and Republican personas, engaging in a structured interaction akin to human group study. Our approach evaluates how agents' responses evolve through social influence. Our key findings indicate that LLM agents role-playing detailed personas and without Chain-of-Thought (CoT) reasoning closely align with human behaviors, while having CoT reasoning hurts the alignment. However, incorporating explicit biases into agent prompts does not necessarily enhance the wisdom of partisan crowds. Moreover, fine-tuning LLMs with human data shows promise in achieving human-like behavior but poses a risk of overfitting certain behaviors. These findings show the potential and limitations of using LLM agents in modeling human group phenomena.

翻译：本研究探讨了大语言模型（LLMs）模拟人类群体动态的潜力，特别是在政治敏感情境下的应用。我们通过让LLMs扮演民主党与共和党角色，复现了"党派群体智慧"现象，并开展与人类群体研究类似的结构化互动。我们的评估方法分析了智能体响应如何随社会影响而演变。关键发现表明：采用详细角色扮演且不使用思维链（CoT）推理的LLM智能体，其行为与人类高度一致，而使用CoT推理则会削弱这种一致性。然而，在智能体提示中显式加入偏见并不必然增强党派群体智慧。此外，使用人类数据对LLM进行微调虽在实现类人行为方面展现出潜力，但存在过度拟合特定行为的风险。这些发现揭示了利用LLM智能体建模人类群体现象的能力与局限性。