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, 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 for various scenarios where trust is paramount. Our study represents a significant step in understanding the behaviors of LLM agents and the LLM-human analogy.

翻译：大型语言模型（LLM）智能体已日益被用作模拟工具，在社会科学等应用场景中建模人类行为。然而，一个基本问题依然存在：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的信任行为。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，我们将其称为"智能体信任"。随后，我们观察到LLM智能体在信任行为上与人类具有高度行为一致性，这表明利用LLM智能体模拟人类信任行为具有可行性。此外，我们深入探究了智能体信任中的偏差问题，以及智能体信任对同类智能体与人类对象的差异。我们还研究了在高级推理策略和外部干预等条件下智能体信任的内在特性，并进一步为信任至关重要的各类场景提供了重要启示。本研究标志着我们在理解LLM智能体行为及LLM-人类类比方面迈出了重要一步。