  Large language models (LLMs) have been widely used as agents to complete
different tasks, such as personal assistance or event planning. While most of
the work has focused on cooperation and collaboration between agents, little
work explores competition, another important mechanism that promotes the
development of society and economy. In this paper, we seek to examine the
competition dynamics in LLM-based agents. We first propose a general framework
for studying the competition between agents. Then, we implement a practical
competitive environment using GPT-4 to simulate a virtual town with two types
of agents, restaurant agents and customer agents. Specifically, the restaurant
agents compete with each other to attract more customers, where competition
encourages them to transform, such as cultivating new operating strategies.
Simulation experiments reveal several interesting findings at the micro and
macro levels, which align well with existing market and sociological theories.
We hope that the framework and environment can be a promising testbed to study
competition that fosters understanding of society. Code is available at:
https://github.com/microsoft/competeai.
