Large Language Models for Behavioral Economics: Internal Validity and Elicitation of Mental Models
This addresses methodological issues for researchers in behavioral and experimental economics, but appears incremental as it applies existing AI tools to a specific domain.
The paper tackles the challenge of ensuring internal validity in behavioral economics experiments, particularly for eliciting mental models, by integrating Large Language Models (LLMs) to improve experimental design and participant engagement, with a case study demonstrating enhanced validity.
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.