IDEAS home Printed from https://ideas.repec.org/p/gre/wpaper/2025-09.html
   My bibliography  Save this paper

Uncovering the Fairness of AI: Exploring Focal Point, Inequality Aversion, and Altruism in ChatGPT's Dictator Game Decisions

Author

Listed:
  • Eléonore Dodivers

    (Université Côte d'Azur, CNRS, GREDEG, France)

  • Ismaël Rafaï

    (Toulouse School of Economics, Toulouse School of Management)

Abstract

This paper investigates Artificial intelligence Large Language Models (AI-LLM) social preferences’ in Dictator Games. Brookins and Debacker (2024, Economics Bulletin) previously observed a tendency of ChatGPT-3.5 to give away half its endowment in a standard Dictator Game and interpreted this as an expression of fairness. We replicate their experiment and introduce a multiplicative factor on donations which varies the efficiency of the transfer. Varying transfer efficiency disentangles three donation explanations (inequality aversion, altruism, or focal point). Our results show that ChatGPT-3.5 donations should be interpreted as a focal point rather than the expression of fairness. In contrast, a more advanced version (ChatGPT-4o) made decisions that are better explained by altruistic motives than inequality aversion. Our study highlights the necessity to explore the parameter space, when designing experiments to study AI-LLM preferences.

Suggested Citation

  • 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.
    • Handle: RePEc:gre:wpaper:2025-09
    as

    Download full text from publisher

    File URL: http://195.220.190.85/GREDEG-WP-2025-09.pdf
    File Function: First version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Shumiao Ouyang & Hayong Yun & Xingjian Zheng, 2024. "How Ethical Should AI Be? How AI Alignment Shapes the Risk Preferences of LLMs," Papers 2406.01168, arXiv.org, revised Aug 2024.
    3. Nunzio Lor`e & Babak Heydari, 2023. "Strategic Behavior of Large Language Models: Game Structure vs. Contextual Framing," Papers 2309.05898, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    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. Herbert Dawid & Philipp Harting & Hankui Wang & Zhongli Wang & Jiachen Yi, 2025. "Agentic Workflows for Economic Research: Design and Implementation," Papers 2504.09736, arXiv.org.
    2. Manish Jha & Jialin Qian & Michael Weber & Baozhong Yang, 2024. "Harnessing Generative AI for Economic Insights," Papers 2410.03897, arXiv.org, revised Feb 2025.
    3. Jiaxin Liu & Yi Yang & Kar Yan Tam, 2025. "Evaluating and Aligning Human Economic Risk Preferences in LLMs," Papers 2503.06646, arXiv.org.
    4. Thomas Henning & Siddhartha M. Ojha & Ross Spoon & Jiatong Han & Colin F. Camerer, 2025. "LLM Trading: Analysis of LLM Agent Behavior in Experimental Asset Markets," Papers 2502.15800, arXiv.org, revised Apr 2025.
    5. Ayato Kitadai & Sinndy Dayana Rico Lugo & Yudai Tsurusaki & Yusuke Fukasawa & Nariaki Nishino, 2024. "Can AI with High Reasoning Ability Replicate Human-like Decision Making in Economic Experiments?," Papers 2406.11426, arXiv.org.
    6. 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).
    7. Chuanhao Li & Runhan Yang & Tiankai Li & Milad Bafarassat & Kourosh Sharifi & Dirk Bergemann & Zhuoran Yang, 2024. "STRIDE: A Tool-Assisted LLM Agent Framework for Strategic and Interactive Decision-Making," Cowles Foundation Discussion Papers 2393, Cowles Foundation for Research in Economics, Yale University.

    More about this item

    Keywords

    Artificial Intelligence; Large Language Models; Dictator Games; Experimental Economics; Social Preferences;
    All these keywords.

    JEL classification:

    • D90 - Microeconomics - - Micro-Based Behavioral Economics - - - General
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:gre:wpaper:2025-09. 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: Patrice Bougette (email available below). General contact details of provider: https://edirc.repec.org/data/credcfr.html .

    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.