用大模型模拟议员行为，预测国会投票更准且可解释。
Political Actor Agent: Simulating Legislative System for Roll Call Votes Prediction with Large Language Models
- 让大模型扮演议员角色，模拟立法过程做预测
- 在美众议院117-118届投票数据上表现更优
- 结果可读性强，适合政治学与计算社会科学者
通过建模政治人物预测国会投票已成为定量政治学与计算机科学的热点。现有基于嵌入的方法需人工设计特征、依赖大量训练数据，且缺乏可解释性。本文提出政治行为体代理（PAA），一种基于大语言模型的新型代理框架，通过角色扮演架构与立法系统模拟，实现可扩展、可解释的投票预测。该方法不仅提升预测准确率，还提供多视角、人类可理解的决策推理，揭示政治行为规律。我们在美众议院第117-118届投票记录上进行了全面实验，验证了PAA在性能与可解释性上的优势。研究不仅证明其有效性，也展示了在政治科学研究中的潜力。
原文摘要 · Abstract (English)
Predicting roll call votes through modeling political actors has emerged as a focus in quantitative political science and computer science. Widely used embedding-based methods generate vectors for legislators from diverse data sets to predict legislative behaviors. However, these methods often contend with challenges such as the need for manually predefined features, reliance on extensive training data, and a lack of interpretability. Achieving more interpretable predictions under flexible conditions remains an unresolved issue. This paper introduces the Political Actor Agent (PAA), a novel agent-based framework that utilizes Large Language Models to overcome these limitations. By employing role-playing architectures and simulating legislative system, PAA provides a scalable and interpretable paradigm for predicting roll-call votes. Our approach not only enhances the accuracy of predictions but also offers multi-view, human-understandable decision reasoning, providing new insights into political actor behaviors. We conducted comprehensive experiments using voting records from the 117-118th U.S. House of Representatives, validating the superior performance and interpretability of PAA. This study not only demonstrates PAA's effectiveness but also its potential in political science research.
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