Intelligent Computing Social Modeling and Methodological Innovations in Political Science in the Era of Large Language Models

2024-10-07

Journal of Chinese Political Science

AI Summary

This study investigates how large language models (LLMs) catalyze transformative shifts in social science knowledge production and political methodology. To address this, we propose the Intelligent Computational Social Modeling (ICSM) framework, which integrates LLMs’ capabilities for ideational synthesis and behavioral simulation with agent-based modeling (ABM)-inspired prompt engineering, chain-of-reasoning simulation, and hybrid method design—unifying micro-level mechanism identification and macro-level effect evaluation within a “social simulation construction–empirical validation” closed loop. Applied to the U.S. presidential election, ICSM successfully reproduces salient electoral dynamics while preserving interpretability and enhancing predictive accuracy. Results demonstrate that LLMs enable deep integration—not replacement—of quantitative and qualitative paradigms. ICSM constitutes the first generative computational methodology for political science that simultaneously ensures mechanistic insight, scalability, and empirical verifiability.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Natural Language Processing: (Large) Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

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.

Problem

• Understanding LLMs' impact on knowledge production and paradigm transformation in social sciences
• Exploring LLMs' specific applications and innovative methods in political science research
• Addressing methodological gaps in integrating technology with social science research paradigms

Innovation

• Leveraging LLMs for idea synthesis and action simulation
• Proposing Intelligent Computing Social Modeling method
• Simulating social construction and validating through simulations