用大模型模拟市场行为，发现难达均衡，但可作可复现的经济实验工具。
An Experimental Study of Competitive Market Behavior Through LLMs
- 构建受控市场环境，让大模型扮演交易者角色
- 模型无法收敛到竞争性均衡，决策缺乏动态适应性
- 适合研究经济政策或市场机制的初探者使用
本研究探索大语言模型（LLMs）进行市场实验的潜力，旨在理解其对竞争性市场动态的理解能力。在受控实验环境中建模市场参与者的行為，评估其向竞争性均衡收敛的能力。结果显示，当前大模型难以复制人类交易行为所特有的动态决策过程。与人类不同，大模型无法实现市场均衡。研究证明，尽管大模型可作为可扩展、可复现的市场模拟工具，但其当前局限性要求进一步提升动态学习能力和融入行为经济学元素，以更好捕捉市场行为复杂性。未来工作可通过增强动态学习与引入行为经济学机制，提升大模型在经济领域的有效性，为理解市场动态和优化经济政策提供新视角。
原文摘要 · Abstract (English)
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.
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