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

TL;DR

This paper investigates how Large Language Models can improve the internal validity of behavioral economics experiments by aiding in experimental design and mental model measurement.

Contribution

It introduces a novel application of LLMs to enhance internal validity and mental model elicitation in behavioral economics research.

Findings

LLMs can improve adherence to exclusion restrictions.

LLMs enhance participant engagement in experiments.

LLMs increase the validity of mental model measurements.

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.