Large Language Model (LLM) agents have been increasingly adopted as simulation tools to model humans in applications such as social science. However, one fundamental question remains: can LLM agents really simulate human behaviors? In this paper, we focus on one of the most critical behaviors in human interactions, trust, and aim to investigate whether or not LLM agents can simulate human trust behaviors. We first find that LLM agents generally exhibit trust behaviors, referred to as agent trust, under the framework of Trust Games, which are widely recognized in behavioral economics. Then, we discover that LLM agents can have high behavioral alignment with humans regarding trust behaviors, particularly for GPT-4, indicating the feasibility to simulate human trust behaviors with LLM agents. In addition, we probe into the biases in agent trust and the differences in agent trust towards agents and humans. We also explore the intrinsic properties of agent trust under conditions including advanced reasoning strategies and external manipulations. We further offer important implications of our discoveries for various scenarios where trust is paramount. Our study provides new insights into the behaviors of LLM agents and the fundamental analogy between LLMs and humans.

翻译：大型语言模型（LLM）智能体在社会科学等应用中日益被用作模拟人类行为的工具。然而，一个根本性问题依然存在：LLM智能体真的能模拟人类行为吗？本文聚焦于人类互动中最关键的行为之一——信任，旨在探究LLM智能体能否模拟人类信任行为。我们首先发现，在行为经济学广泛认可的信任博弈框架下，LLM智能体通常表现出信任行为，即智能体信任。接着，我们发现LLM智能体在信任行为上与人类具有高度的行为一致性，尤其是GPT-4，表明利用LLM智能体模拟人类信任行为具有可行性。此外，我们深入探讨了智能体信任中的偏差，以及智能体对智能体与对人类信任行为的差异。我们还研究了高级推理策略和外部操纵条件下智能体信任的内在特性，并进一步阐述了我们的发现对信任至关重要的各种场景的重要意义。本研究为理解LLM智能体的行为以及LLM与人类之间的根本相似性提供了新见解。