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

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Auteur
Date

30 juin 2024

Discipline
Type de document
Périmètre
Identifiant
  • 2407.12032
Source

arXiv - économie

Collection

arXiv

Organisation

Cornell University

Mots-clés Und

Computer Science - Human-Computer Interaction Computer Science - Artificial Intelligence Economics - General Economics

Sujets proches En Fr

Language (New words, slang, etc.) Informatics Economic theory Political economy computing informatisation

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Brian Jabarian, « Large Language Models for Behavioral Economics: Internal Validity and Elicitation of Mental Models », arXiv - économie

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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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