IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/35185.html

Revealing Life Preferences Through LLMs

Author

Listed:
  • Omar Abdel Haq
  • Amitabh Chandra
  • Tomáš Jagelka
  • Erzo F.P. Luttmer
  • Joshua Schwartzstein

Abstract

Large Language Models (LLMs) are trained on a prodigious corpus of human writing and may reveal human preferences over characteristics of life courses, such as income, longevity, and working conditions. We present OpenAI's GPT-5.4 and a broadly representative sample of Americans with pairs of life stories and ask them to choose the life they would prefer for themselves. A person's choice is better predicted by the LLM's choice than by another person’s choice over the same stories, and LLM valuations of several life attributes are similar to those derived from human responses. Our results suggest that LLM responses offer a scalable and cost-effective complement to existing methods for studying human preferences.

Suggested Citation

  • Omar Abdel Haq & Amitabh Chandra & Tomáš Jagelka & Erzo F.P. Luttmer & Joshua Schwartzstein, 2026. "Revealing Life Preferences Through LLMs," NBER Working Papers 35185, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:35185
    Note: AG EH PE
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w35185.pdf
    Download Restriction: Access to the full text is generally limited to series subscribers, however if the top level domain of the client browser is in a developing country or transition economy free access is provided. More information about subscriptions and free access is available at http://www.nber.org/wwphelp.html. Free access is also available to older working papers.
    ---><---

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

    for a different version of it.

    More about this item

    JEL classification:

    • D0 - Microeconomics - - General
    • H0 - Public Economics - - General
    • I0 - Health, Education, and Welfare - - General

    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:nbr:nberwo:35185. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.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.