用大模型模拟美国大选投票行为，提升准确性并揭示其局限。
A Large-Scale Simulation on Large Language Models for Decision-Making in Political Science
- 构建融合人口、时间与意识形态的多步推理框架
- 基于真实数据生成虚拟选民，模拟近年美国总统选举
- 验证不同大模型表现，揭示政治模拟中的挑战
尽管大语言模型在文本生成和推理方面表现出色，但其在政治情境下模拟人类决策的能力仍不明确。由于选民层级数据有限、政治格局持续变化及人类推理复杂性，建模选民行为面临独特挑战。本文提出一种基于理论的多步推理框架，整合人口统计、时间动态与意识形态因素，实现大规模选民决策模拟。通过使用校准至真实选民数据的合成人格，我们对近年美国总统选举进行了大规模仿真。该方法显著提升了模拟精度，并缓解了模型偏差。我们进一步对比了不同大语言模型的表现，评估其鲁棒性，并深入探讨基于大模型的政治模拟所面临的挑战与限制。本研究为政治决策行为建模提供可扩展框架，同时揭示了大模型在政治科学研究中的潜力与边界。
原文摘要 · Abstract (English)
While LLMs have demonstrated remarkable capabilities in text generation and reasoning, their ability to simulate human decision-making -- particularly in political contexts -- remains an open question. However, modeling voter behavior presents unique challenges due to limited voter-level data, evolving political landscapes, and the complexity of human reasoning. In this study, we develop a theory-driven, multi-step reasoning framework that integrates demographic, temporal and ideological factors to simulate voter decision-making at scale. Using synthetic personas calibrated to real-world voter data, we conduct large-scale simulations of recent U.S. presidential elections. Our method significantly improves simulation accuracy while mitigating model biases. We examine its robustness by comparing performance across different LLMs. We further investigate the challenges and constraints that arise from LLM-based political simulations. Our work provides both a scalable framework for modeling political decision-making behavior and insights into the promise and limitations of using LLMs in political science research.
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