用大模型模拟选民决策，还原2024美国大选结果
FlockVote: LLM-Empowered Agent-Based Modeling for Simulating U.S. Presidential Elections
- 让大模型扮演带背景的虚拟选民，生成真实投票理由
- 在7个关键摇摆州模拟结果与实际选举高度吻合
- 可追溯每名选民决策逻辑，适合政治建模与政策推演
模拟复杂人类行为（如全国选举中的选民决策）是计算社会科学长期面临的挑战。传统基于代理的模型受限于规则过于简化，而大规模统计模型常缺乏可解释性。我们提出FlockVote，一种利用大语言模型（LLMs）构建的“计算实验室”式框架，通过高保真人口统计特征和动态上下文信息（如候选人政策），使每个代理能进行细致的生成式推理，模拟投票决策。该框架被部署于2024年美国总统选举，聚焦七个关键摇摆州。模拟的宏观结果成功复现了现实选举结果，证明了“虚拟社会”的高保真度。其主要贡献不仅是预测能力，更在于作为可解释研究工具的潜力。FlockVote突破黑箱输出局限，使研究者可探查个体代理的推理过程，并分析大模型驱动社会模拟的稳定性与敏感性。
原文摘要 · Abstract (English)
Modeling complex human behavior, such as voter decisions in national elections, is a long-standing challenge for computational social science. Traditional agent-based models (ABMs) are limited by oversimplified rules, while large-scale statistical models often lack interpretability. We introduce FlockVote, a novel framework that uses Large Language Models (LLMs) to build a "computational laboratory" of LLM agents for political simulation. Each agent is instantiated with a high-fidelity demographic profile and dynamic contextual information (e.g. candidate policies), enabling it to perform nuanced, generative reasoning to simulate a voting decision. We deploy this framework as a testbed on the 2024 U.S. Presidential Election, focusing on seven key swing states. Our simulation's macro-level results successfully replicate the real-world outcome, demonstrating the high fidelity of our "virtual society". The primary contribution is not only the prediction, but also the framework's utility as an interpretable research tool. FlockVote moves beyond black-box outputs, allowing researchers to probe agent-level rationale and analyze the stability and sensitivity of LLM-driven social simulations.
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