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Cited by 294Multi-Agent Systems and NegotiationSimulation Techniques and ApplicationsModeling and Simulation Systems

Large language models empowered agent-based modeling and simulation: a survey and perspectives

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Gao, Chen, Lan, Xiaochong, Li, Nian, Yuan, Yuan, Ding, Jingtao, Zhou, Zhilun, Xu, Fengli, Li, Yong·2024·Humanities and Social Sciences Communications
DOI 10.1057/s41599-024-03611-3
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Abstract

Agent-based modeling and simulation have evolved as a powerful tool for modeling complex systems, offering insights into emergent behaviors and interactions among diverse agents. Recently, integrating large language models into agent-based modeling and simulation presents a promising avenue for enhancing simulation capabilities. This paper surveys the landscape of utilizing large language models in agent-based modeling and simulation, discussing their challenges and promising future directions. In this survey, since this is an interdisciplinary field, we first introduce the background of agent-based modeling and simulation and large language model-empowered agents. We then discuss the motivation for applying large language models to agent-based simulation and systematically analyze the challenges in environment perception, human alignment, action generation, and evaluation. Most importantly, we provide a comprehensive overview of the recent works of large language model-empowered agent-based modeling and simulation in multiple scenarios, which can be divided into four domains: cyber, physical, social, and hybrid, covering simulation of both real-world and virtual environments, and how these works address the above challenges. Finally, since this area is new and quickly evolving, we discuss the open problems and promising future directions. We summarize the representative papers along with their code repositories in https://github.com/tsinghua-fib-lab/LLM-Agent-Based-Modeling-and-Simulation .

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Cited by

Sorted by citations

Authors
Gao, C., Lan, X., Li, N., Yuan, Y., Ding, J., Zhou, Z., Xu, F., & Li, Y.
Affiliations
Tsinghua University (CN)
ORCID
Venue
Humanities and Social Sciences Communications
Publisher
Springer Science and Business Media LLC
Published
2024-09-27
Volume
11
Issue
1
ISSN
  • 2055-1045 (ISSN-L)
  • 2662-9992
DOI
10.1057/s41599-024-03611-3
OpenAlex
W4402901320
Cited by
294
References
26
Citations by year
2026: 151 · 2025: 129 · 2024: 14
FWCI
43.11
Type
journal article
Language
en
Open access
Licence
Locations
  • Humanities and Social Sciences Communications (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
Funders
National Natural Science Foundation of China (U23B2030)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Simulation Techniques and Applications
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Modeling and Simulation Systems
Keywords
large language models, agent-based modeling, environmental perception, human alignment, action generation, simulation evaluation, physical simulation, social simulation, hybrid simulation, emergent behavior

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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