IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2407.12032.html

Large Language Models for Behavioral Economics: Internal Validity and Elicitation of Mental Models

Author

Listed:
  • Brian Jabarian

Abstract

In this article, we explore the transformative potential of integrating generative AI, particularly Large Language Models (LLMs), into behavioral and experimental economics to enhance internal validity. By leveraging AI tools, researchers can improve adherence to key exclusion restrictions and in particular ensure the internal validity measures of mental models, which often require human intervention in the incentive mechanism. We present a case study demonstrating how LLMs can enhance experimental design, participant engagement, and the validity of measuring mental models.

Suggested Citation

  • Brian Jabarian, 2024. "Large Language Models for Behavioral Economics: Internal Validity and Elicitation of Mental Models," Papers 2407.12032, arXiv.org.
    • Handle: RePEc:arx:papers:2407.12032
    as

    Download full text from publisher

    File URL: https://arxiv.org/pdf/2407.12032
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gary Charness & Brian Jabarian & John A. List, 2023. "Generation Next: Experimentation with AI," NBER Working Papers 31679, National Bureau of Economic Research, Inc.
    2. John List, 2024. "Experimental Economics: Theory and Practice," Natural Field Experiments 00792, The Field Experiments Website.
    3. Ben Gillen & Erik Snowberg & Leeat Yariv, 2019. "Experimenting with Measurement Error: Techniques with Applications to the Caltech Cohort Study," Journal of Political Economy, University of Chicago Press, vol. 127(4), pages 1826-1863.
    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. Chang, Samuel & Kennedy, Andrew & Leonard, Aaron & List, John A., 2026. "12 best practices for leveraging Generative AI in experimental research," Journal of Economic Behavior & Organization, Elsevier, vol. 246(C).
    2. Johannes Abeler & Armin Falk & Fabian Kosse, 2025. "Malleability of Preferences for Honesty," The Economic Journal, Royal Economic Society, vol. 135(667), pages 982-998.
    3. Francesco Capozza & Ingar Haaland & Christopher Roth & Johannes Wohlfart, 2021. "Studying Information Acquisition in the Field: A Practical Guide and Review," CEBI working paper series 21-15, University of Copenhagen. Department of Economics. The Center for Economic Behavior and Inequality (CEBI).
    4. José de Sousa & Guillaume Hollard, 2021. "From Micro to Macro Gender Differences: Evidence from Field Tournaments," Post-Print hal-03389151, HAL.
    5. Simon Gächter & Chris Starmer & Fabio Tufano, 2025. "Measuring Group Cohesion to Reveal the Power of Social Relationships in Team Production," The Review of Economics and Statistics, MIT Press, vol. 107(2), pages 539-554, March.
    6. Ardila Brenøe, Anne & Eyibak, Zeynep & Heursen, Lea & Ranehill, Eva & Weber, Roberto A., 2024. "Gender Identity and Economic Decision Making," Working Papers 2024:6, Lund University, Department of Economics.
    7. Chi Trieu, 2023. "Who’s who: how uncertainty about the favored group affects outcomes of affirmative action," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 9(2), pages 252-292, December.
    8. Kessel, Dany & Mollerstrom, Johanna & van Veldhuizen, Roel, 2021. "Can simple advice eliminate the gender gap in willingness to compete?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 138, pages 1-1.
    9. John A. List, 2024. "Optimally generate policy-based evidence before scaling," Nature, Nature, vol. 626(7999), pages 491-499, February.
    10. Benjamin Enke & Ricardo Rodríguez-Padilla & Florian Zimmermann, 2019. "Moral Universalism: Measurement and Heterogeneity," CESifo Working Paper Series 7921, CESifo.
    11. Grosch, Kerstin & Fischer, Sabine, 2024. "Gender equivalence in overconfidence A large-scale experimental study in a non-WEIRD country," Department for Strategy and Innovation Working Paper Series 02/2024, WU Vienna University of Economics and Business.
    12. Brodeur, Abel & Cook, Nikolai & Heyes, Anthony, 2022. "We Need to Talk about Mechanical Turk: What 22,989 Hypothesis Tests Tell Us about Publication Bias and p-Hacking in Online Experiments," IZA Discussion Papers 15478, IZA Network @ LISER.
    13. Halko, Marja-Liisa & Lappalainen, Olli & Sääksvuori, Lauri, 2021. "Do non-choice data reveal economic preferences? Evidence from biometric data and compensation-scheme choice," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 87-104.
    14. Simon Jäger & Christopher Roth & Nina Roussille & Benjamin Schoefer, 2024. "Worker Beliefs About Outside Options," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 139(3), pages 1505-1556.
    15. Clochard, Gwen-Jirō & Hollard, Guillaume & Sene, Omar, 2026. "Bringing contact interventions to the lab: Effects of brief bilateral discussions on interethnic trust in Senegal," World Development, Elsevier, vol. 199(C).
    16. Rezaei, Sarah & Rosenkranz, Stephanie & Weitzel, Utz & Westbrock, Bastian, 2024. "Social preferences on networks," Journal of Public Economics, Elsevier, vol. 234(C).
    17. Rhee, Elaine & Noussair, Charles N., 2022. "Is the gender difference in competitive behavior history dependent?," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 44-67.
    18. Wang, Xinghua & Navarro-Martinez, Daniel, 2023. "Increasing the external validity of social preference games by reducing measurement error," Games and Economic Behavior, Elsevier, vol. 141(C), pages 261-285.
    19. Christian Belzil & Tomáš Jagelka, 2024. "Separating Preferences from Endogenous Effort and Cognitive Noise in Observed Decisions," Working Papers 2024-13, Center for Research in Economics and Statistics.
    20. Fidanoski, Filip & Johnson, Timothy, 2023. "A z-Tree implementation of the Dynamic Experiments for Estimating Preferences [DEEP] method," Journal of Behavioral and Experimental Finance, Elsevier, vol. 38(C).

    More about this item

    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:arx:papers:2407.12032. 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: arXiv administrators (email available below). General contact details of provider: https://arxiv.org/ .

    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.