Simulating Economic Experiments Using Large Language Models: Design and Development of a Computational Tool

Abstract

Large language models, known for capturing the syntax, semantics, and broader representation of human behavior, can be used to simulate humans in AI-based experimentation. Because these models provide responses consistent with humans, they may be used to pilot studies or glean insights into social and economic scenarios. This research presents the development of homo silicus, a Python-based library for simulating economic experiments and social scenarios using AI subjects. Using the library, users can design, test, and analyze results of experiments conducted in silico. We replicate various classical economic research to better understand how well AI subject responses align with observed human behavior, and we test the impact of parameters such as temperature and prompt engineering to determine their influence on results. The homo silicus library provides researchers with a cost-effective method to iterate on projects without extensive resources or specific participant recruitment. This research contributes to advancing the field of AI-powered social simulation models and their applications in economic experimentation.