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.

翻译：本研究探讨了利用大语言模型（LLMs）开展市场实验的潜力，旨在理解其理解竞争性市场动态的能力。我们在受控实验环境中模拟市场主体的行为，评估其向竞争均衡收敛的能力。研究结果表明，当前LLMs在复现人类交易行为特有的动态决策过程方面面临挑战。与人类不同，LLMs缺乏实现市场均衡的能力。本研究表明，虽然LLMs为可扩展且可复现的市场模拟提供了有价值的工具，但其当前局限性需要进一步的技术突破才能完整捕捉市场行为的复杂性。未来通过增强动态学习能力并融入行为经济学要素的研究，有望提升LLMs在经济学领域的应用效能，从而为理解市场动态提供新视角，并助力经济政策的优化完善。