# Political Actor Agent: Simulating Legislative System for Roll Call Votes Prediction with Large Language Models

arXiv: https://arxiv.org/abs/2412.07144 · HTML: https://mlsanity.com/paper/2412.07144/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 10 Dec 2024.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 11 of 11)

- [ChatDev: Communicative Agents for Software Development](https://mlsanity.com/paper/2307.07924/) (2023)
- [Emergent autonomous scientific research capabilities of large language models](https://mlsanity.com/paper/2304.05332/) (2023)
- [Embodied Task Planning with Large Language Models](https://mlsanity.com/paper/2307.01848/) (2023)
- [Unveiling the Truth and Facilitating Change: Towards Agent-based Large-scale Social Movement Simulation](https://mlsanity.com/paper/2402.16333/) (2024)
- [LLM Voting: Human Choices and AI Collective Decision Making](https://mlsanity.com/paper/2402.01766/) (2024)
- [Beyond Demographics: Aligning Role-playing LLM-based Agents Using Human Belief Networks](https://mlsanity.com/paper/2406.17232/) (2024)
- [Artificial Leviathan: Exploring Social Evolution of LLM Agents Through the Lens of Hobbesian Social Contract Theory](https://mlsanity.com/paper/2406.14373/) (2024)
- [KGAP: Knowledge Graph Augmented Political Perspective Detection in News Media](https://mlsanity.com/paper/2108.03861/) (2021)
- [Generative AI voting: fair collective choice is resilient to LLM biases and inconsistencies](https://mlsanity.com/paper/2406.11871/) (2024)
- [Are LLMs Rational Investors? A Study on Detecting and Reducing the Financial Bias in LLMs](https://mlsanity.com/paper/2402.12713/) (2024)
- [PAR: Political Actor Representation Learning with Social Context and Expert Knowledge](https://mlsanity.com/paper/2210.08362/) (2022)

## Built on by (most cited first, 6 of 6)

- [Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directions](https://mlsanity.com/paper/2602.22680/) (2026)
- [ValuesRAG: Enhancing Cultural Alignment Through Retrieval-Augmented Contextual Learning](https://mlsanity.com/paper/2501.01031/) (2025)
- [LLM Agents in Law: Taxonomy, Applications, and Challenges](https://mlsanity.com/paper/2601.06216/) (2026)
- [Beyond Isolated Investor: Predicting Startup Success via Roleplay-Based Collective Agents](https://mlsanity.com/paper/2512.22608/) (2025)
- [Cross-Cultural Simulation of Citizen Emotional Responses to Bureaucratic Red Tape Using LLM Agents](https://mlsanity.com/paper/2604.12545/) (2026)
- [Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents](https://mlsanity.com/paper/2607.15095/) (2026)

## Abstract (arXiv)

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.