An Experimental Study of Competitive Market Behavior Through LLMs
It addresses the problem of using LLMs for scalable market simulations in economics, but is incremental as it highlights limitations rather than breakthroughs.
This study investigated whether large language models (LLMs) can simulate competitive market behavior and converge to equilibria, finding that current LLMs fail to achieve market equilibrium unlike humans.
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.