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

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  • 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.

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  • 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
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    References listed on IDEAS

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    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. 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.
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