An Experimental Study of Competitive Market Behavior Through LLMs

Jingru Jia, Zehua Yuan

Keywords: Large Language Models, LLM Reasoning, Experimental Economics, Algorithmic Trading, Adaptive Learning, Market Simulation, LLM Prompting

TL;DR: This study explores the application of large language models (LLMs) to simulate competitive market dynamics, highlighting the challenges and limitations of LLMs in achieving market equilibrium compared to human trading behaviors.

Abstract:

This study explores the potential of large language models (LLMs) to conduct market experiments, aiming to understand their capability to comprehend competitive market dynamics. We model the behavior of market agents in a controlled experimental setting, assessing their ability to converge toward competitive equilibria. The results reveal the challenges current LLMs face in replicating the dynamic decision-making processes characteristic of human trading behavior.
Unlike humans, LLMs lacked the capacity to achieve market equilibrium.
The research demonstrates that while LLMs provide a valuable tool for scalable and reproducible market simulations, their current limitations necessitate further advancements to fully capture the complexities of market behavior.
Future work that enhances dynamic learning capabilities and incorporates elements of behavioral economics could improve the effectiveness of LLMs in the economic domain, providing new insights into market dynamics and aiding in the refinement of economic policies.

Submission Number: 24