Large Language Models for Behavioral Economics: Internal Validity and Elicitation of Mental Models
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914874507919360 |
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| author | Jabarian, Brian |
| author_facet | Jabarian, Brian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_12032 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Language Models for Behavioral Economics: Internal Validity and Elicitation of Mental Models Jabarian, Brian Human-Computer Interaction Artificial Intelligence General Economics Economics 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. |
| title | Large Language Models for Behavioral Economics: Internal Validity and Elicitation of Mental Models |
| topic | Human-Computer Interaction Artificial Intelligence General Economics Economics |
| url | https://arxiv.org/abs/2407.12032 |