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Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives

Paper · 2023 · Chen Gao, Xiaochong Lan, Nian Li et al.

Summary

This survey from Tsinghua University reviews a young research area: using large language models as the decision-making core of agents in agent-based modeling and simulation. Classic simulations rely on hand-written rules or narrow neural models, so they struggle to reproduce realistic, adaptive human behavior. The authors first cover the basics of simulation (agents, environment, interaction) and list the capabilities an agent needs. They then argue that LLM agents help meet these needs and identify the main obstacles: perceiving the environment, aligning with human behavior, generating actions and evaluating outcomes. At its center, the survey classifies recent studies under a four-part scheme of application areas, namely physical, social, cyber and hybrid settings. For each domain it explains how environments are designed and how LLM-driven agents are built, covering both real-world and virtual settings. The paper presents no new dataset or model. Its contribution is a taxonomy and a research agenda. This is useful for anyone planning LLM-based simulations of social, economic or urban systems.

Key points

  • The survey defines four required agent capabilities: autonomy, social ability, reactivity and pro-activeness. It also notes that bounded rationality matters for human-like behavior in markets and organizations.
  • According to the authors, LLMs add value through perception, reasoning and decision-making, adaptivity, and agent heterogeneity.
  • Simulation work is grouped into four domains: physical (ecology, urban mobility, manufacturing), social (social networks, economics), cyber (web behavior, security) and hybrid (e.g., socio-physical urban systems).
  • The open directions include scaling up simulations, open simulation platforms, robustness and ethical risks. On robustness, adversarial attacks may spread between agents, and collective behaviors such as conformity and homophily can be exploited.
  • The ethics evidence cited includes a roughly 6× rise in ChatGPT toxicity when certain personas are assigned. The authors also mention the CoMPosT framework for measuring caricature in persona-based agents and benchmarks based on Moral Foundation theory.

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

🎓Tsinghua UniversityBeijing · China415 works · 94,028 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksComputational Theory and Mathematics · 321 worksDecision Sciences · 249 worksManagement Science and Operations Research · 134 works

Cite

Cite as Chen Gao, Xiaochong Lan, Nian Li, Yuan Yuan, Jingtao Ding, Zhilun Zhou, Fengli Xu and Yong Li. 2023. Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives. arXiv:2312.11970.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2312.11970 · arXiv 2312.11970

@misc{gao2023large,
  title = {{Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives}},
  author = {Chen Gao, Xiaochong Lan, Nian Li, Yuan Yuan, Jingtao Ding, Zhilun Zhou, Fengli Xu and Yong Li},
  year = {2023},
  doi = {10.48550/arxiv.2312.11970},
  eprint = {2312.11970},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2312.11970}
}

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