[2512.05982] FlockVote: LLM-Empowered Agent-Based Modeling for Simulating U.S. Presidential Elections

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Physics > Physics and Society

arXiv:2512.05982 (physics)

[Submitted on 27 Nov 2025]

Title:FlockVote: LLM-Empowered Agent-Based Modeling for Simulating U.S. Presidential Elections

Authors:Lingfeng Zhou, Yi Xu, Zhenyu Wang, Dequan Wang<br>View a PDF of the paper titled FlockVote: LLM-Empowered Agent-Based Modeling for Simulating U.S. Presidential Elections, by Lingfeng Zhou and 3 other authors

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Abstract:Modeling complex human behavior, such as voter decisions in national elections, is a long-standing challenge for computational social science. Traditional agent-based models (ABMs) are limited by oversimplified rules, while large-scale statistical models often lack interpretability. We introduce FlockVote, a novel framework that uses Large Language Models (LLMs) to build a "computational laboratory" of LLM agents for political simulation. Each agent is instantiated with a high-fidelity demographic profile and dynamic contextual information (e.g. candidate policies), enabling it to perform nuanced, generative reasoning to simulate a voting decision. We deploy this framework as a testbed on the 2024 U.S. Presidential Election, focusing on seven key swing states. Our simulation's macro-level results successfully replicate the real-world outcome, demonstrating the high fidelity of our "virtual society". The primary contribution is not only the prediction, but also the framework's utility as an interpretable research tool. FlockVote moves beyond black-box outputs, allowing researchers to probe agent-level rationale and analyze the stability and sensitivity of LLM-driven social simulations.

Comments:<br>Published as a conference paper at ICAIS 2025

Subjects:

Physics and Society (physics.soc-ph); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)

Cite as:<br>arXiv:2512.05982 [physics.soc-ph]

(or<br>arXiv:2512.05982v1 [physics.soc-ph] for this version)

https://doi.org/10.48550/arXiv.2512.05982

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arXiv-issued DOI via DataCite

Submission history<br>From: Lingfeng Zhou [view email]<br>[v1]<br>Thu, 27 Nov 2025 12:04:07 UTC (1,243 KB)

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