CompeteAI: Understanding the Competition Behaviors in Large Language
  Model-based Agents
Qinlin Zhao Jindong Wang Yixuan Zhang Yiqiao Jin Kaijie Zhu Hao Chen Xing Xie
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.