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LLM Voting: Human Choices and AI Collective Decision Making

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

Abstract

This paper investigates the voting behaviors of Large Language Models (LLMs), particularly OpenAI's GPT4 and LLaMA2, and their alignment with human voting patterns. Our approach included a human voting experiment to establish a baseline for human preferences and a parallel experiment with LLM agents. The study focused on both collective outcomes and individual preferences, revealing differences in decision-making and inherent biases between humans and LLMs. We observed a trade-off between preference diversity and alignment in LLMs, with a tendency towards more uniform choices as compared to the diverse preferences of human voters. This finding indicates that LLMs could lead to more homogenized collective outcomes when used in voting assistance, underscoring the need for cautious integration of LLMs into democratic processes.

Suggested Citation

  • Joshua C. Yang & Marcin Korecki & Damian Dailisan & Carina I. Hausladen & Dirk Helbing, 2024. "LLM Voting: Human Choices and AI Collective Decision Making," Papers 2402.01766, arXiv.org.
    • 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.
    2. John J. Horton, 2023. "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?," NBER Working Papers 31122, National Bureau of Economic Research, Inc.
    3. John J. Horton, 2023. "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?," Papers 2301.07543, arXiv.org.
    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.
    6. Blanco, Mariana & Engelmann, Dirk & Normann, Hans Theo, 2011. "A within-subject analysis of other-regarding preferences," Games and Economic Behavior, Elsevier, vol. 72(2), pages 321-338, June.
    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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