用大模型模拟社会行为，推动政治学研究方法革新。
Intelligent Computing Social Modeling and Methodological Innovations in Political Science in the Era of Large Language Models
- 提出智能计算社会建模方法，融合大模型的生成与推理能力。
- 通过模拟美国总统选举，验证了该方法在机制发现与预测上的有效性。
- 适合关注方法创新的政治学者与量化/质性研究者参考。
大型语言模型（LLMs）为政治科学的方法论创新带来机遇与挑战，引发对社会科学范式转变的讨论。然而，如何从技术与方法融合的视角理解LLMs对知识生产与范式转型的影响，以及其在政治科学研究中的具体应用和代表性创新方法，仍缺乏系统探讨。本文提出“智能计算社会建模”（ICSM）方法，阐明LLMs在思想整合与行为模拟方面的核心机制。ICSM通过“模拟社会建构”与“模拟验证”推进政治科学的认知探索。以美国总统选举模拟为例，实证展示了ICSM的操作路径与方法优势。该方法既增强定量范式利用大数据评估影响因素的能力，也为定性范式提供个体层面的社会机制发现证据，实现可解释性与预测力的平衡。研究认为，LLMs将通过集成优化而非直接替代，驱动政治学方法论创新。
原文摘要 · Abstract (English)
The recent wave of artificial intelligence, epitomized by large language models (LLMs),has presented opportunities and challenges for methodological innovation in political science,sparking discussions on a potential paradigm shift in the social sciences. However, how can weunderstand the impact of LLMs on knowledge production and paradigm transformation in thesocial sciences from a comprehensive perspective that integrates technology and methodology? What are LLMs' specific applications and representative innovative methods in political scienceresearch? These questions, particularly from a practical methodological standpoint, remainunderexplored. This paper proposes the "Intelligent Computing Social Modeling" (ICSM) methodto address these issues by clarifying the critical mechanisms of LLMs. ICSM leverages thestrengths of LLMs in idea synthesis and action simulation, advancing intellectual exploration inpolitical science through "simulated social construction" and "simulation validation." Bysimulating the U.S. presidential election, this study empirically demonstrates the operationalpathways and methodological advantages of ICSM. By integrating traditional social scienceparadigms, ICSM not only enhances the quantitative paradigm's capability to apply big data toassess the impact of factors but also provides qualitative paradigms with evidence for socialmechanism discovery at the individual level, offering a powerful tool that balances interpretabilityand predictability in social science research. The findings suggest that LLMs will drivemethodological innovation in political science through integration and improvement rather thandirect substitution.
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