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.

大型语言模型（LLM）代理程序作为模拟人类行为的应用工具已经得到越来越广泛的应用，本文关注于研究 LLM 代理程序能否真正模拟人类的信任行为，通过对 LLM 代理程序在信任博弈框架下的行为模式以及与人类的行为一致性的研究，探究了代理程序对信任的偏好和对人类与代理程序之间的信任差异，并对信任在不同场景下的重要性提出了重要启示，从而推动了对 LLM 代理程序与人类之间行为类比的理解。