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来模拟复杂系统动态，是一种极具影响力的方法。ABM的优势在于其自下而上的方法论，通过建模系统各组成部分的行为来揭示涌现现象。然而，ABM面临一系列挑战，尤其是在用数学方程或规则对自然语言指令和常识进行建模方面存在困难。本文旨在通过将GPT等大型语言模型（LLM）融入ABM来突破这些局限。这种融合催生了一种新颖框架——智能智能体基建模（SABM）。基于智能体这一概念——即具有智能性、适应性和计算能力的实体——我们探索了利用LLM驱动的智能体以更高精微度和真实感模拟现实场景的方向。在这项全面探索中，我们阐述了ABM的技术现状，介绍了SABM的潜力与方法论，并呈现三个案例研究（源代码见https://github.com/Roihn/SABM），演示了SABM方法论并验证了其在模拟现实世界系统中的有效性。此外，我们展望了SABM未来发展的若干方向，预期其应用领域将更加广阔。通过这项研究，我们致力于重新定义计算机模拟的边界，从而更深刻地理解复杂系统。