IDEAS home Printed from https://ideas.repec.org/a/eee/jeborg/v237y2025ics0167268125002781.html
   My bibliography  Save this article

How do individuals interact with an AI advisor in strategic reasoning? An experimental study in beauty contest

Author

Listed:
  • Cagno, Daniela Di
  • Lin, Lihui

Abstract

This paper experimentally investigates how individuals use generative AI to learn and respond in a strategic reasoning contest. An advisor based on level k theory and implemented using ChatGPT is introduced in a four-stage beauty contest experiment. The experiment is designed to explore how AI advisors influence the depth of human reasoning by shaping beliefs, learning, and sophisticated backward induction. Extended cognitive hierarchy models (Camerer et al., 2004) are applied to identify heterogeneous level distribution and more sophisticated thinking. Additionally, the interactions between participants with and without AI advisors are examined. Two key results emerge. First, individuals overestimate AI capabilities when competing against AI-guided participants, which motivates them to employ higher levels of thinking. This observed higher-level behaviour is driven by more sophisticated backward reasoning. Second, improved reasoning under AI guidance shows heterogeneous effects across Cognitive Reflection Test scores, suggesting that AI's impact depends on participants' pre-existing cognitive abilities. Overall, this early research provides insights into the interaction between generative AI and human cognition and reasoning.

Suggested Citation

  • Cagno, Daniela Di & Lin, Lihui, 2025. "How do individuals interact with an AI advisor in strategic reasoning? An experimental study in beauty contest," Journal of Economic Behavior & Organization, Elsevier, vol. 237(C).
    • Handle: RePEc:eee:jeborg:v:237:y:2025:i:c:s0167268125002781
    DOI: 10.1016/j.jebo.2025.107159
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167268125002781
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jebo.2025.107159?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:eee:jeborg:v:237:y:2025:i:c:s0167268125002781. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jebo .

    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.