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Playing games with GPT: What can we learn about a large language model from canonical strategic games?

Author

Listed:
  • Philip Brookins

    (University of South Carolina)

  • Jason DeBacker

    (University of South Carolina)

Abstract

We aim to understand fundamental preferences over fairness and cooperation embedded in artificial intelligence (AI). We do this by having a large language model (LLM), GPT-3.5, play two classic games: the dictator game and the prisoner's dilemma game. We compare the decisions of the LLM to those of humans in laboratory experiments. We find that the LLM replicates human tendencies towards fairness and cooperation. It does not choose the optimal strategy in most cases. Rather, it shows a tendency towards fairness in the dictator game, even more so than human participants. In the prisoner's dilemma, the LLM displays rates of cooperation much higher than human participants (about 65% versus 37% for humans). These findings aid our understanding of the ethics and rationality embedded in AI.

Suggested Citation

  • Philip Brookins & Jason DeBacker, 2024. "Playing games with GPT: What can we learn about a large language model from canonical strategic games?," Economics Bulletin, AccessEcon, vol. 44(1), pages 25-37.
    • Handle: RePEc:ebl:ecbull:eb-23-00457
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    References listed on IDEAS

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    2. Fulin Guo, 2023. "GPT in Game Theory Experiments," Papers 2305.05516, arXiv.org, revised Dec 2023.
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    Citations

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    Cited by:

    1. Polachek, Solomon & Romano, Kenneth & Tonguc, Ozlem, 2024. "Homo-Silicus: Not (Yet) a Good Imitator of Homo Sapiens or Homo Economicus," IZA Discussion Papers 17521, Institute of Labor Economics (IZA).
    2. Thomas Henning & Siddhartha M. Ojha & Ross Spoon & Jiatong Han & Colin F. Camerer, 2025. "LLM Agents Do Not Replicate Human Market Traders: Evidence From Experimental Finance," Papers 2502.15800, arXiv.org, revised Oct 2025.
    3. Ryota IWAMOTO & Takunori ISHIHARA & Takanori IDA, 2025. "Comparing Risk Preferences and Loss Aversion in Humans and AI: A Persona-Based Approach with Fine-Tuning," Discussion papers e-25-006, Graduate School of Economics , Kyoto University.
    4. Yingnan Yan & Tianming Liu & Yafeng Yin, 2025. "Valuing Time in Silicon: Can Large Language Model Replicate Human Value of Travel Time," Papers 2507.22244, arXiv.org.
    5. Shu Wang & Zijun Yao & Shuhuai Zhang & Jianuo Gai & Tracy Xiao Liu & Songfa Zhong, 2025. "When Experimental Economics Meets Large Language Models: Evidence-based Tactics," Papers 2505.21371, arXiv.org, revised Jul 2025.
    6. Herbert Dawid & Philipp Harting & Hankui Wang & Zhongli Wang & Jiachen Yi, 2025. "Agentic Workflows for Economic Research: Design and Implementation," Papers 2504.09736, arXiv.org.
    7. Eléonore Dodivers & Ismaël Rafaï, 2025. "Uncovering the Fairness of AI: Exploring Focal Point, Inequality Aversion, and Altruism in ChatGPT's Dictator Game Decisions," GREDEG Working Papers 2025-09, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.

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    JEL classification:

    • C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments

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