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.

翻译：通过建模政治行动者来预测唱名表决已成为定量政治学和计算机科学领域的一个研究焦点。广泛使用的基于嵌入的方法从多样化数据集中为立法者生成向量以预测其立法行为。然而，这些方法常面临诸多挑战，例如需要手动预定义特征、依赖大量训练数据以及缺乏可解释性。在灵活条件下实现更具可解释性的预测仍是一个未解决的问题。本文提出政治行动者智能体，一种新颖的基于智能体的框架，它利用大语言模型来克服这些局限。通过采用角色扮演架构并模拟立法系统，PAA为唱名表决预测提供了一个可扩展且可解释的范式。我们的方法不仅提高了预测准确性，还提供了多视角、人类可理解的决策推理，为政治行动者行为研究提供了新见解。我们使用美国第117-118届众议院的投票记录进行了全面实验，验证了PAA的优越性能和可解释性。本研究不仅证明了PAA的有效性，也展现了其在政治学研究中的潜力。