An Experimental Study of Competitive Market Behavior Through LLMs

TL;DR

This paper investigates the use of large language models to simulate market behavior, revealing their current limitations in replicating human-like decision-making and market equilibrium achievement.

Contribution

It introduces a novel experimental framework for testing LLMs in market simulations and highlights their challenges in modeling dynamic economic behaviors.

Findings

LLMs struggle to reach market equilibrium.

Current LLMs cannot fully replicate human trading behavior.

Market simulations with LLMs are scalable but limited in complexity.

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.