Large Language Model (LLM) agents have been increasingly adopted as simulation tools to model humans in social science and role-playing applications. However, one fundamental question remains: can LLM agents really simulate human behavior? In this paper, we focus on one critical and elemental behavior in human interactions, trust, and investigate whether LLM agents can simulate human trust behavior. We first find that LLM agents generally exhibit trust behavior, referred to as agent trust, under the framework of Trust Games, which are widely recognized in behavioral economics. Then, we discover that GPT-4 agents manifest high behavioral alignment with humans in terms of trust behavior, indicating the feasibility of simulating human trust behavior with LLM agents. In addition, we probe the biases of agent trust and differences in agent trust towards other LLM agents and humans. We also explore the intrinsic properties of agent trust under conditions including external manipulations and advanced reasoning strategies. Our study provides new insights into the behaviors of LLM agents and the fundamental analogy between LLMs and humans beyond value alignment. We further illustrate broader implications of our discoveries for applications where trust is paramount.

翻译：大型语言模型智能体日益被社会科学和角色扮演应用采纳为模拟人类行为的工具。然而，一个根本性问题依然存在：LLM智能体是否真能模拟人类行为？本文聚焦于人类互动中一种关键且基础的行为——信任，探究LLM智能体能否模拟人类的信任行为。我们首先发现，在行为经济学中广泛认可的信任博弈框架下，LLM智能体普遍表现出被称为智能体信任的行为。随后，我们发现GPT-4智能体在信任行为方面与人类表现出高度的行为对齐，这证明了用LLM智能体模拟人类信任行为的可行性。此外，我们探究了智能体信任的偏差，以及智能体对其他LLM智能体与人类信任行为的差异。我们还探索了在外部操控和高级推理策略等条件下智能体信任的内在特性。本研究为理解LLM智能体行为以及超越价值对齐的LLM与人类根本相似性提供了新视角。我们进一步阐述了这些发现对信任至关重要的应用场景所具有的广泛意义。