Potential Applications of Generative AI in Economic Simulations

In this article, we present some preliminary analyses in which Large Language Models (LLMs) are used as economic agents in simulations, as an example of utilizing Generative AI in economic analysis. Existing research reports that Generative AI provides responses consistent with predictions suggested in fields like behavioral economics. There are also some studies which have applied Agent-Based Models (ABM) by treating Generative AI as "players" in a market. However, even though Generative AI exhibits behavior similar to actual economic agents, in reality, it is merely outputting statistically consistent responses based on patterns found in its training data. Therefore, whether the results of simulations that treat Generative AI as economic agents are consistent with economic theory depends crucially on the AI's training data. In this article, we conduct simple ABM simulations to demonstrate how Generative AI can be applied, and examine whether its responses are aligned with intuition and economic theory. Our results are consistent with economic theory: (1) consumers adjust their spending in response to real wage fluctuations; and (2) firms find it easier to pass costs on to consumers in a monopoly market compared to a duopoly market. We conclude that it is necessary to continue verifying through other economic analyses whether simulations using Generative AI consistently lead to conclusions congruent with economic theory.