  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.
