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

TL;DR

FlockVote leverages large language models to create an interpretable agent-based simulation of U.S. presidential elections, accurately predicting outcomes and enabling detailed analysis of voter behavior.

Contribution

Introduces FlockVote, a novel LLM-based framework for simulating elections with high-fidelity, interpretable agents, advancing social science research tools.

Findings

Successfully replicated 2024 U.S. election results

Demonstrated high fidelity of the virtual society

Enabled agent-level rationale analysis

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.