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

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arxiv 2406.18702 v1 pith:2AORKH7T submitted 2024-06-26 cs.HC cs.CL

Zachary R. Baker , Zarif L. Azher This is my paper · ORCID
classification cs.HCcs.CL
keywords agentsapproachlegislativellm-drivenagentbipartisanshipcommitteemodeling
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 5 Pith papers

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