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[2506.11798] Persona-driven Simulation of Voting Behavior in the European Parliament with Large Language Models
Summary
This paper explores the use of Large Language Models (LLMs) to simulate voting behavior in the European Parliament through persona-driven prompts, achieving a notable predictive accuracy.
Why It Matters
Understanding voting behavior in political contexts is crucial for predicting policy outcomes and enhancing democratic processes. This research leverages advanced AI techniques to provide insights into how LLMs can model complex human behaviors, which could have implications for political analysis and campaign strategies.
Key Takeaways
- LLMs can simulate voting behavior effectively using persona prompts.
- The study achieved a weighted F1 score of approximately 0.793 in predicting voting decisions.
- Persona-driven simulations can help understand political dynamics and group positions on policies.
Computer Science > Computation and Language arXiv:2506.11798 (cs) [Submitted on 13 Jun 2025 (v1), last revised 19 Feb 2026 (this version, v3)] Title:Persona-driven Simulation of Voting Behavior in the European Parliament with Large Language Models Authors:Maximilian Kreutner, Marlene Lutz, Markus Strohmaier View a PDF of the paper titled Persona-driven Simulation of Voting Behavior in the European Parliament with Large Language Models, by Maximilian Kreutner and 2 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) display remarkable capabilities to understand or even produce political discourse but have been found to consistently exhibit a progressive left-leaning bias. At the same time, so-called persona or identity prompts have been shown to produce LLM behavior that aligns with socioeconomic groups with which the base model is not aligned. In this work, we analyze whether zero-shot persona prompting with limited information can accurately predict individual voting decisions and, by aggregation, accurately predict the positions of European groups on a diverse set of policies. We evaluate whether predictions are stable in response to counterfactual arguments, different persona prompts, and generation methods. Finally, we find that we can simulate the voting behavior of Members of the European Parliament reasonably well, achieving a weighted F1 score of approximately 0.793. Our persona dataset of politicians in the 2024 European Parliament and...