Humans act via a nuanced process that depends both on rational deliberation and also on identity and contextual factors. In this work, we study how large language models (LLMs) can simulate human action in the context of social dilemma games. While prior work has focused on "steering" (weak binding) of chat models to simulate personas, we analyze here how deep binding of base models with extended backstories leads to more faithful replication of identity-based behaviors. Our study has these findings: simulation fidelity vs human studies is improved by conditioning base LMs with rich context of narrative identities and checking consistency using instruction-tuned models. We show that LLMs can also model contextual factors such as time (year that a study was performed), question framing, and participant pool effects. LLMs, therefore, allow us to explore the details that affect human studies but which are often omitted from experiment descriptions, and which hamper accurate replication.

翻译：人类行为遵循一个既依赖理性思考又受身份认同与情境因素影响的微妙过程。本研究探讨了大型语言模型（LLM）如何在社交困境博弈情境中模拟人类行为。先前研究主要关注通过"引导"（弱绑定）对话模型来模拟人物角色，而本文分析了将基础模型与扩展背景故事进行深度绑定如何能更真实地复现基于身份的行为。我们的研究发现：通过为基础语言模型赋予叙事身份的丰富情境，并利用指令调优模型进行一致性检验，可以提升模拟结果与人类研究之间的保真度。我们证明LLM还能模拟时间（研究开展的年份）、问题框架和参与者群体效应等情境因素。因此，LLM使我们能够深入探究那些影响人类研究却常被实验描述所忽略、并阻碍精确复现的细节因素。