Large language models (LLMs) have been widely used as agents to complete
different tasks, such as personal assistance or event planning. While most work
has focused on cooperation and collaboration between agents, little work
explores competition, another important mechanism that fosters the development
of society and economy. In this paper, we seek to examine the competition
behaviors in LLM-based agents. We first propose a general framework to study
the competition between agents. Then, we implement a practical competitive
environment using GPT-4 to simulate a virtual town with two types of agents,
including restaurant agents and customer agents. Specifically, restaurant
agents compete with each other to attract more customers, where the competition
fosters them to transform, such as cultivating new operating strategies. The
results of our experiments reveal several interesting findings ranging from
social learning to Matthew Effect, which aligns well with existing sociological
and economic theories. We believe that competition between agents deserves
further investigation to help us understand society better. The code will be
released soon.

大型语言模型的竞争行为研究：本文提出了一个通用框架来研究基于 LLMs 的代理之间的竞争行为，并使用 GPT-4 实现了一个模拟虚拟城镇的实际竞争环境，包括餐厅代理和顾客代理。研究发现竞争促使餐厅代理采取不同的运营策略，呈现出社会学和经济学理论所支持的一些有趣结果。