{"title":"An Experimental Study of Competitive Market Behavior Through LLMs","authors":"Jingru Jia, Zehua Yuan","doi":"arxiv-2409.08357","DOIUrl":null,"url":null,"abstract":"This study explores the potential of large language models (LLMs) to conduct\nmarket experiments, aiming to understand their capability to comprehend\ncompetitive market dynamics. We model the behavior of market agents in a\ncontrolled experimental setting, assessing their ability to converge toward\ncompetitive equilibria. The results reveal the challenges current LLMs face in\nreplicating the dynamic decision-making processes characteristic of human\ntrading behavior. Unlike humans, LLMs lacked the capacity to achieve market\nequilibrium. The research demonstrates that while LLMs provide a valuable tool\nfor scalable and reproducible market simulations, their current limitations\nnecessitate further advancements to fully capture the complexities of market\nbehavior. Future work that enhances dynamic learning capabilities and\nincorporates elements of behavioral economics could improve the effectiveness\nof LLMs in the economic domain, providing new insights into market dynamics and\naiding in the refinement of economic policies.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"45 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - ECON - General Economics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.08357","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.