Computer simulations offer a robust toolset for exploring complex systems across various disciplines. A particularly impactful approach within this realm is Agent-Based Modeling (ABM), which harnesses the interactions of individual agents to emulate intricate system dynamics. ABM's strength lies in its bottom-up methodology, illuminating emergent phenomena by modeling the behaviors of individual components of a system. Yet, ABM has its own set of challenges, notably its struggle with modeling natural language instructions and common sense in mathematical equations or rules. This paper seeks to transcend these boundaries by integrating Large Language Models (LLMs) like GPT into ABM. This amalgamation gives birth to a novel framework, Smart Agent-Based Modeling (SABM). Building upon the concept of smart agents -- entities characterized by their intelligence, adaptability, and computation ability -- we explore in the direction of utilizing LLM-powered agents to simulate real-world scenarios with increased nuance and realism. In this comprehensive exploration, we elucidate the state of the art of ABM, introduce SABM's potential and methodology, and present three case studies (source codes available at https://github.com/Roihn/SABM), demonstrating the SABM methodology and validating its effectiveness in modeling real-world systems. Furthermore, we cast a vision towards several aspects of the future of SABM, anticipating a broader horizon for its applications. Through this endeavor, we aspire to redefine the boundaries of computer simulations, enabling a more profound understanding of complex systems.

翻译：计算机模拟为跨学科探索复杂系统提供了强大的工具集。其中，智能体基建模（ABM）通过个体智能体的交互模拟复杂系统动态，因其自底向上的方法论——通过建模系统组件的个体行为揭示涌现现象——而独具影响力。然而，ABM面临固有挑战，特别是在用数学方程或规则建模自然语言指令与常识方面存在困难。本文通过将GPT等大型语言模型（LLMs）融入ABM以突破这些局限，由此诞生了新型框架——智能体基建模（SABM）。基于智能体的核心概念（具有智能性、适应性与计算能力的实体），我们探索了利用LLM驱动的智能体以更高细腻度与真实感模拟现实场景的路径。在这项系统性研究中，我们阐述了ABM领域的最新进展，介绍了SABM的潜力与方法论，并通过三个案例研究（源代码见https://github.com/Roihn/SABM）演示了SABM方法论及其在建模真实世界系统中的有效性验证。此外，我们展望了SABM未来的多个发展方向，预期其应用前景将更为广阔。通过这一探索，我们旨在重新定义计算机模拟的边界，助力更深刻地理解复杂系统。