IDEAS home Printed from https://ideas.repec.org/a/ebl/ecbull/eb-23-00457.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.accessecon.com/Pubs/EB/2024/Volume44/EB-24-V44-I1-P3.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Afriat, Sidney N, 1972. "Efficiency Estimation of Production Function," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 13(3), pages 568-598, October.
    2. Fulin Guo, 2023. "GPT in Game Theory Experiments," Papers 2305.05516, arXiv.org, revised Dec 2023.
    3. 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.
    4. Steve Phelps & Yvan I. Russell, 2023. "The Machine Psychology of Cooperation: Can GPT models operationalise prompts for altruism, cooperation, competitiveness and selfishness in economic games?," Papers 2305.07970, arXiv.org, revised Jun 2024.
    5. 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.
    6. Leland Bybee, 2023. "Surveying Generative AI's Economic Expectations," Papers 2305.02823, arXiv.org, revised May 2023.
    7. John J. Horton, 2023. "Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?," Papers 2301.07543, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kirshner, Samuel N., 2024. "GPT and CLT: The impact of ChatGPT's level of abstraction on consumer recommendations," Journal of Retailing and Consumer Services, Elsevier, vol. 76(C).
    2. Christoph Engel & Max R. P. Grossmann & Axel Ockenfels, 2023. "Integrating machine behavior into human subject experiments: A user-friendly toolkit and illustrations," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2024_01, Max Planck Institute for Research on Collective Goods.
    3. Fulin Guo, 2023. "GPT in Game Theory Experiments," Papers 2305.05516, arXiv.org, revised Dec 2023.
    4. Jingru Jia & Zehua Yuan & Junhao Pan & Paul E. McNamara & Deming Chen, 2024. "Decision-Making Behavior Evaluation Framework for LLMs under Uncertain Context," Papers 2406.05972, arXiv.org, revised Oct 2024.
    5. Nunzio Lor`e & Babak Heydari, 2023. "Strategic Behavior of Large Language Models: Game Structure vs. Contextual Framing," Papers 2309.05898, arXiv.org.
    6. Ennio Bilancini & Leonardo Boncinelli & Eugenio Vicario, 2024. "AI-powered Chatbots: Effective Communication Styles for Sustainable Development Goals," Papers 2407.01057, arXiv.org.
    7. Bauer, Kevin & Liebich, Lena & Hinz, Oliver & Kosfeld, Michael, 2023. "Decoding GPT's hidden "rationality" of cooperation," SAFE Working Paper Series 401, Leibniz Institute for Financial Research SAFE.
    8. Kevin Leyton-Brown & Paul Milgrom & Neil Newman & Ilya Segal, 2024. "Artificial Intelligence and Market Design: Lessons Learned from Radio Spectrum Reallocation," NBER Chapters, in: New Directions in Market Design, National Bureau of Economic Research, Inc.
    9. Zengqing Wu & Run Peng & Xu Han & Shuyuan Zheng & Yixin Zhang & Chuan Xiao, 2023. "Smart Agent-Based Modeling: On the Use of Large Language Models in Computer Simulations," Papers 2311.06330, arXiv.org, revised Dec 2023.
    10. 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.
    11. Nir Chemaya & Daniel Martin, 2023. "Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals," Papers 2311.14720, arXiv.org, revised Jan 2024.
    12. Lijia Ma & Xingchen Xu & Yong Tan, 2024. "Crafting Knowledge: Exploring the Creative Mechanisms of Chat-Based Search Engines," Papers 2402.19421, arXiv.org.
    13. Ali Goli & Amandeep Singh, 2023. "Exploring the Influence of Language on Time-Reward Perceptions in Large Language Models: A Study Using GPT-3.5," Papers 2305.02531, arXiv.org, revised Jun 2023.
    14. Evangelos Katsamakas, 2024. "Business models for the simulation hypothesis," Papers 2404.08991, arXiv.org.
    15. Yuan Gao & Dokyun Lee & Gordon Burtch & Sina Fazelpour, 2024. "Take Caution in Using LLMs as Human Surrogates: Scylla Ex Machina," Papers 2410.19599, arXiv.org, revised Jan 2025.
    16. Yiting Chen & Tracy Xiao Liu & You Shan & Songfa Zhong, 2023. "The emergence of economic rationality of GPT," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 120(51), pages 2316205120-, December.
    17. Samuel Chang & Andrew Kennedy & Aaron Leonard & John A. List, 2024. "12 Best Practices for Leveraging Generative AI in Experimental Research," NBER Working Papers 33025, National Bureau of Economic Research, Inc.
    18. Jiafu An & Difang Huang & Chen Lin & Mingzhu Tai, 2024. "Measuring Gender and Racial Biases in Large Language Models," Papers 2403.15281, arXiv.org.
    19. Daniel Albert & Stephan Billinger, 2024. "Reproducing and Extending Experiments in Behavioral Strategy with Large Language Models," Papers 2410.06932, arXiv.org.
    20. Cova, Joshua & Schmitz, Luuk, 2024. "A primer for the use of classifier and generative large language models in social science research," OSF Preprints r3qng, Center for Open Science.

    More about this item

    Keywords

    Large language models (LLMs); Generative Pre-trained Transformer (GPT); Experimental Economics; Game Theory; AI;
    All these keywords.

    JEL classification:

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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ebl:ecbull:eb-23-00457. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: John P. Conley (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.