Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives
On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks
Abstract (arXiv)
Agent-based modeling and simulation has evolved as a powerful tool for modeling complex systems, offering insights into emergent behaviors and interactions among diverse agents. 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, examining 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. Finally, since this area is new and quickly evolving, we discuss the open problems and promising future directions.
Key facts. Cited by 255 papers in the map (data through 13 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 111 of its 258 references that are in the map. First posted on arXiv 19 Dec 2023.
Builds on
111 of 258 references.
Closest ancestors · 14
Same direction, newest first
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Other references · 97
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Citing papers per year: 2023: 1; 2024: 45; 2025: 118; 2026: 91.
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