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.

本研究探讨了大型语言模型（LLMs）在市场实验中的潜力，以理解其对竞争市场动态的理解能力。研究发现，当前LLMs在复制人类交易行为的动态决策过程中面临挑战，无法实现市场均衡，凸显了其在捕捉复杂市场行为方面的局限性。此研究为市场模拟提供了有价值的工具，但未来需加强其动态学习能力以提高在经济领域的有效性。

通过大型语言模型的竞争市场行为实验研究