| 总结: | 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.
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