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