LLM代理在酒吧难题中自发形成集体决策，类人行为凸显社会动机与理性权衡。
Emergent Social Dynamics of LLM Agents in the El Farol Bar Problem
- LLM代理通过自组织形成集体行动，自发产生去酒吧的动机。
- 未完全解决难题，决策表现更接近人类而非纯理性模型。
- 适合研究群体行为、社会智能与具身化代理的学者参考。
我们研究了大型语言模型（LLM）代理在空间扩展的埃尔法罗尔酒吧问题中的涌现社会动态，观察其如何自主应对这一经典社会困境。结果表明，LLM代理自发产生了前往酒吧的动机，并通过集体协作改变决策模式。同时发现，这些代理并未完全解决问题，而是表现出更像人类的行为特征。这揭示了外部激励（如提示中设定的60%阈值等约束条件）与内部激励（源自预训练的文化编码社会偏好）之间的复杂互动，表明LLM代理自然地在形式博弈论理性与体现人类行为特征的社会动机之间寻求平衡。这些发现表明，一种在传统博弈论框架下无法实现的新群体决策模型，可通过LLM代理得以实现。
原文摘要 · Abstract (English)
We investigate the emergent social dynamics of Large Language Model (LLM) agents in a spatially extended El Farol Bar problem, observing how they autonomously navigate this classic social dilemma. As a result, the LLM agents generated a spontaneous motivation to go to the bar and changed their decision making by becoming a collective. We also observed that the LLM agents did not solve the problem completely, but rather behaved more like humans. These findings reveal a complex interplay between external incentives (prompt-specified constraints such as the 60% threshold) and internal incentives (culturally-encoded social preferences derived from pre-training), demonstrating that LLM agents naturally balance formal game-theoretic rationality with social motivations that characterize human behavior. These findings suggest that a new model of group decision making, which could not be handled in the previous game-theoretic problem setting, can be realized by LLM agents.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。