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（Agent-Based Modeling, ABM）这一极具影响力的方法，通过个体智能体的交互来模拟复杂的系统动态。ABM的优势在于其自下而上的方法论，通过建模系统中各组件的个体行为来揭示涌现现象。然而，ABM自身也面临一系列挑战，特别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等大语言模型（LLMs）集成到ABM中来突破这些局限。这种融合催生了一个名为“智能体基建模”（Smart Agent-Based Modeling, SABM）的新框架。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驱动的智能体来模拟现实世界场景，以增强模拟的细腻度与真实感。在这项综合性探索中，我们阐述了ABM的研究现状，介绍了SABM的潜力与方法论，并展示了三个案例研究（源代码见 https://github.com/Roihn/SABM），以说明SABM方法论并验证其在建模现实世界系统方面的有效性。此外，我们展望了SABM未来发展的几个方向，预判其更广阔的应用前景。通过这一努力，我们期冀重新定义计算机模拟的边界，从而更深刻地理解复杂系统。