---
title: "CompeteAI — paper by Zhao et al. (2023) — summary and ideas"
description: "The authors propose CompeteAI, a four-stage methodology: pick an environment, set it up, run… 45 references, cited by 20 papers here."
authors: ["Qinlin Zhao", "Jindong Wang", "Yixuan Zhang", "Yiqiao Jin", "Kaijie Zhu", "Hao Chen", "Xing Xie"]
year: 2023
venue: "arXiv:2310.17512"
source: "https://arxiv.org/abs/2310.17512"
doi: "10.48550/arxiv.2310.17512"
arxiv: "2310.17512"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhao-2023-competeai-understanding-competition-dynamics-large/"
---
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# CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents

**Authors:** [Qinlin Zhao](https://md.research.elamur.ai/free/en/authors/qinlin-zhao/), [Jindong Wang](https://md.research.elamur.ai/free/en/authors/jindong-wang/), [Yixuan Zhang](https://md.research.elamur.ai/free/en/authors/yixuan-zhang/), [Yiqiao Jin](https://md.research.elamur.ai/free/en/authors/yiqiao-jin/), [Kaijie Zhu](https://md.research.elamur.ai/free/en/authors/kaijie-zhu/), [Hao Chen](https://md.research.elamur.ai/free/en/authors/hao-chen/) and [Xing Xie](https://md.research.elamur.ai/free/en/authors/xing-xie/) · **Year:** 2023 · **Venue:** arXiv:2310.17512 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2310.17512) · [PDF](https://arxiv.org/pdf/2310.17512) · [DOI 10.48550/arxiv.2310.17512](https://doi.org/10.48550/arxiv.2310.17512) · [arXiv 2310.17512](https://arxiv.org/abs/2310.17512) · [OpenAlex](https://openalex.org/W4387994499)

**Citations:** 7 citations (OpenAlex, 2026-10-14) · [20](https://md.research.elamur.ai/free/en/papers/zhao-2023-competeai-understanding-competition-dynamics-large/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Studies of multi-agent LLM systems have mostly examined cooperation, so it remains unclear how LLM-based agents behave when they compete for scarce resources. Competition is a major driver of economic and social change, and earlier approaches had clear limits. Empirical research cannot observe individual-level dynamics, and classic agent-based models struggle to reproduce realistic human behavior. The authors propose CompeteAI, a four-stage methodology: pick an environment, set it up, run simulations, then analyze. They instantiate it as a virtual town powered by GPT-4. Two restaurant agents run their businesses through a management API, handling menus, chefs and advertising. Fifty customer agents act as judges, choosing where to eat each day and leaving feedback. The simulations reproduced several patterns that economics and sociology describe. Restaurants differentiated from and imitated each other, customers based their choices on several factors, and a self-reinforcing Matthew Effect emerged. Competition also raised product quality. The work suggests LLM agents can serve as a testbed for social-science research on competition.

## Key points
- The environment has 2 restaurants and 50 customers, all driven by GPT-4 (0613). A run lasts up to 15 days and stops early if one restaurant quits.
- Customers differ in income, taste, health conditions and dietary restrictions. Some dine in groups of 2–4 people, linked as family, colleagues, couples or friends.
- Each day, restaurants receive their daybooks, customer comments and data on their rival. They then revise their strategy through pre-defined actions such as editing the menu, managing chefs and placing ads.
- Popular restaurants kept gaining customers, a pattern known as the Matthew Effect. Group dining weakened the resulting winner-take-all outcome.
- The paper appeared at ICML 2024, and the code is released at github.com/microsoft/competeai. The authors note limits from the small scale imposed by API constraints, text-only interaction, and dependence on a specific GPT-4 version.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Carnegie Mellon University](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/) — University, Pittsburgh, United States · 633 works · 195,217 citations
- [Microsoft](https://md.research.elamur.ai/free/en/companies/microsoft/) — Company, Redmond, United States · 404 works · 163,863 citations
- [Georgia Institute of Technology](https://md.research.elamur.ai/free/en/universities/georgia-institute-of-technology/) — University, Atlanta, United States · 159 works · 28,096 citations
- [University of Science and Technology of China](https://md.research.elamur.ai/free/en/universities/university-of-science-and-technology-of-china/) — University, Hefei, China · 152 works · 114,627 citations
- [William & Mary](https://md.research.elamur.ai/free/en/universities/william-mary/) — University, Williamsburg, United States · 9 works · 895 citations

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## Cite

Cite as: Qinlin Zhao, Jindong Wang, Yixuan Zhang, Yiqiao Jin, Kaijie Zhu, Hao Chen and Xing Xie. 2023. CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents. arXiv:2310.17512.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{zhao2023competeai,
  title = {{CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents}},
  author = {Qinlin Zhao, Jindong Wang, Yixuan Zhang, Yiqiao Jin, Kaijie Zhu, Hao Chen and Xing Xie},
  year = {2023},
  doi = {10.48550/arxiv.2310.17512},
  eprint = {2310.17512},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2310.17512}
}
```

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*Updated 3 November 2026*