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LLM Voting: Human Choices and AI Collective Decision Making

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  • Joshua C. Yang
  • Damian Dailisan
  • Marcin Korecki
  • Carina I. Hausladen
  • Dirk Helbing

Abstract

This paper investigates the voting behaviors of Large Language Models (LLMs), specifically GPT-4 and LLaMA-2, their biases, and how they align with human voting patterns. Our methodology involved using a dataset from a human voting experiment to establish a baseline for human preferences and conducting a corresponding experiment with LLM agents. We observed that the choice of voting methods and the presentation order influenced LLM voting outcomes. We found that varying the persona can reduce some of these biases and enhance alignment with human choices. While the Chain-of-Thought approach did not improve prediction accuracy, it has potential for AI explainability in the voting process. We also identified a trade-off between preference diversity and alignment accuracy in LLMs, influenced by different temperature settings. Our findings indicate that LLMs may lead to less diverse collective outcomes and biased assumptions when used in voting scenarios, emphasizing the need for cautious integration of LLMs into democratic processes.

Suggested Citation

  • Joshua C. Yang & Damian Dailisan & Marcin Korecki & Carina I. Hausladen & Dirk Helbing, 2024. "LLM Voting: Human Choices and AI Collective Decision Making," Papers 2402.01766, arXiv.org, revised Aug 2024.
  • Handle: RePEc:arx:papers:2402.01766
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    References listed on IDEAS

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    1. Jean-François Laslier & Karine Straeten, 2016. "Strategic voting in multi-winner elections with approval balloting: a theory for large electorates," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 47(3), pages 559-587, October.
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    4. Joshua C. Yang & Carina I. Hausladen & Dominik Peters & Evangelos Pournaras & Regula Hanggli Fricker & Dirk Helbing, 2023. "Designing Digital Voting Systems for Citizens: Achieving Fairness and Legitimacy in Participatory Budgeting," Papers 2310.03501, arXiv.org, revised Mar 2024.
    5. Jamshid Sourati & James A. Evans, 2023. "Accelerating science with human-aware artificial intelligence," Nature Human Behaviour, Nature, vol. 7(10), pages 1682-1696, October.
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    7. Edith Elkind & Piotr Faliszewski & Piotr Skowron & Arkadii Slinko, 2017. "Properties of multiwinner voting rules," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 48(3), pages 599-632, March.
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