计算实验已成为研究复杂系统的一种重要方法，其核心涉及反事实的算法化。然而，由于人类具有有限理性、异质性等多样且复杂的特征，在基于智能体的建模（Agent-based Modeling，ABM）中准确表征真实社会系统极具挑战性。为解决这一局限，学界提出整合大语言模型（Large Language Models，LLMs）的方案，使智能体具备复杂推理、自主学习等拟人化能力。这类智能体被称为基于LLM的智能体（LLM-based Agent），有望弥补ABM中缺失的拟人化特性。尽管如此，LLMs缺乏明确可解释性的问题，严重阻碍了其在社会科学领域的应用。与之相对，计算实验在个体行为与复杂现象的因果分析方面表现突出。因此，将计算实验与基于LLM的智能体相结合具备重大研究潜力。本文旨在对这一融合方向展开全面探究：首先梳理智能体结构的历史发展及其向人工社会的演进过程，强调其在计算实验中的重要性；接着分别从基于LLM的智能体赋能计算实验、计算实验反哺基于LLM的智能体两个视角，阐明二者相互赋能的优势；最后探讨该研究领域面临的挑战与未来趋势，为后续相关研究提供指导。
英文摘要：
Computational experiments have emerged as a valuable method for studying complex systems, involving the algorithmization of counterfactuals. However, accurately representing real social systems in Agent-based Modeling (ABM) is challenging due to the diverse and intricate characteristics of humans, including bounded rationality and heterogeneity. To address this limitation, the integration of Large Language Models (LLMs) has been proposed, enabling agents to possess anthropomorphic abilities such as complex reasoning and autonomous learning. These agents, known as LLM-based Agent, offer the potential to enhance the anthropomorphism lacking in ABM. Nonetheless, the absence of explicit explainability in LLMs significantly hinders their application in the social sciences. Conversely, computational experiments excel in providing causal analysis of individual behaviors and complex phenomena. Thus, combining computational experiments with LLM-based Agent holds substantial research potential. This paper aims to present a comprehensive exploration of this fusion. Primarily, it outlines the historical development of agent structures and their evolution into artificial societies, emphasizing their importance in computational experiments. Then it elucidates the advantages that computational experiments and LLM-based Agents offer each other, considering the perspectives of LLM-based Agent for computational experiments and vice versa. Finally, this paper addresses the challenges and future trends in this research domain, offering guidance for subsequent related studies.
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