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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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    1. Matthew Embrey & Guillaume R Fréchette & Sevgi Yuksel, 2018. "Cooperation in the Finitely Repeated Prisoner’s Dilemma," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 509-551.
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    Full references (including those not matched with items on IDEAS)

    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, IZA Network @ LISER.
    2. Ferraz, Vinícius & Olah, Tamas & Sazedul, Ratin & Schmidt, Robert & Schwieren, Christiane, 2025. "When Artificial Minds Negotiate: Dark Personality and the Ultimatum Game in Large Language Models," Working Papers 0768, University of Heidelberg, Department of Economics.
    3. 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.
    4. Ryota IWAMOTO & Takunori ISHIHARA & Takanori IDA, 2025. "Comparing Risk Preferences and Reference Dependence in Humans and AI: A Persona-Based Approach with Fine-Tuning," Discussion papers e-25-006, Graduate School of Economics , Kyoto University.
    5. Eleonore Dodivers & Ismael Rafai, 2025. "Uncovering the fairness of AI: exploring focal point, inequality aversion, and altruism in ChatGPT's dictator game decisions," Economics Bulletin, AccessEcon, vol. 45(4), pages 1818-1825.
    6. Chowdhury Mohammad Sakib Anwar & Konstantinos Georgalos, 2026. "Playing Against the Machine: Cooperation, Communication, and Strategy Heterogeneity in Repeated Prisoner's Dilemma," Papers 2603.15852, arXiv.org.
    7. Yingnan Yan & Tianming Liu & Yafeng Yin, 2025. "Valuing Time in Silicon: Can Large Language Models Replicate Human Value of Travel Time," Papers 2507.22244, arXiv.org, revised Dec 2025.
    8. Pietro Bini & Lin William Cong & Xing Huang & Lawrence J. Jin, 2026. "Behavioral Economics of AI: LLM Biases and Corrections," Papers 2602.09362, arXiv.org.
    9. 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.
    10. Herbert Dawid & Philipp Harting & Hankui Wang & Zhongli Wang & Jiachen Yi, 2025. "Agentic Workflows for Economic Research: Design and Implementation," Papers 2504.09736, arXiv.org.
    11. Priyanka Verma & Balagopal Unnikrishnan, 2025. "When AI Democratizes Exploitation: LLM-Assisted Strategic Manipulation of Fair Division Algorithms," Papers 2511.14722, arXiv.org, revised Feb 2026.
    12. 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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    Keywords

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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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