Simulating The U.S. Senate: An LLM-Driven Agent Approach to Modeling Legislative Behavior and Bipartisanship

TL;DR

This paper presents a novel simulation of the U.S. Senate using LLM-driven agents that can engage in realistic debates, reflect on issues, and model bipartisan shifts, offering new tools for legislative analysis.

Contribution

It introduces a new LLM-based agent framework for simulating legislative behavior and bipartisanship in the U.S. Senate, advancing modeling capabilities.

Findings

Agents can engage in realistic debates

Agents demonstrate bipartisan solutions

Simulation models shifts towards bipartisanship

Abstract

This study introduces a novel approach to simulating legislative processes using LLM-driven virtual agents, focusing on the U.S. Senate Intelligence Committee. We developed agents representing individual senators and placed them in simulated committee discussions. The agents demonstrated the ability to engage in realistic debate, provide thoughtful reflections, and find bipartisan solutions under certain conditions. Notably, the simulation also showed promise in modeling shifts towards bipartisanship in response to external perturbations. Our results indicate that this LLM-driven approach could become a valuable tool for understanding and potentially improving legislative processes, supporting a broader pattern of findings highlighting how LLM-based agents can usefully model real-world phenomena. Future works will focus on enhancing agent complexity, expanding the simulation scope, and exploring applications in policy testing and negotiation.