IDEAS home Printed from https://ideas.repec.org/p/kue/epaper/e-25-006.html
   My bibliography  Save this paper

Comparing Risk Preferences and Loss Aversion in Humans and AI: A Persona-Based Approach with Fine-Tuning

Author

Listed:
  • Ryota IWAMOTO
  • Takunori ISHIHARA
  • Takanori IDA

Abstract

This study empirically investigates the differences in risk preferences and loss aversion between humans and generative AI. We conduct a nationwide online survey of 4,838 individuals and generate AI responses under identical conditions by using personas constructed from demographic attributes. The results show that in gain domains, both humans and the AI select risk-averse options and exhibit similar preference patterns. However, in loss domains, AI shows a stronger risk-loving tendency and responds more sharply to individual attributes such as gender, age, and income. We retrain the AI by fine-tuning it based on human choice data. After fine-tuning, the AI’s preference distribution moves closer to that of humans, with loss-related decisions showing the greatest improvement. Using Wasserstein distance, we also confirm that fine-tuning reduces the behavioral gap between AI and humans.

Suggested Citation

  • 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.
    • Handle: RePEc:kue:epaper:e-25-006
    as

    Download full text from publisher

    File URL: http://www.econ.kyoto-u.ac.jp/dp/papers/e-25-006.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    bias; bias; loss aversion; risk preference; generative AI; persona; fine-tuning; Wasserstein distance;
    All these keywords.

    JEL classification:

    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior

    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:kue:epaper:e-25-006. 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: Graduate School of Economics Project Center (email available below). General contact details of provider: https://edirc.repec.org/data/fekyojp.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.