发表机构
University of Warsaw; Warsaw University of Technology; Centre for Credible Artificial Intelligence (CCAI); Group for Research in Applied Economics (GRAPE)（华沙大学; 华沙理工大学; 可信人工智能中心（CCAI）; 应用经济学研究组（GRAPE））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过将LLM主体替代人类主体复制双拍卖经济实验，发现LLM市场向均衡收敛更慢、效率更低，不同模型及角色决策异质，交易决策与战略考量转向紧迫性相关，还公开了测试框架。
AI 中文摘要
大语言模型（LLM）正越来越多地被用作经济主体，但几乎没有证据表明LLM主体是否适合参与为人类设计的市场机制，以及当市场机制面对LLM主体时是否能产生理想的结果。我们通过复制开创性的经济实验，用LLM主体替代人类主体来解决这个问题。我们将主体置于双拍卖环境中，这是一种广泛使用的市场机制。我们检验这样的市场是否能够实现资源的有效配置，从而测试LLM主体对齐的一个新维度——它们与基础市场机制的兼容性。我们发现，由LLM主体组成的市场向市场均衡收敛的速度更慢，或者根本不收敛，因此与人类主体组成的市场相比，资源配置效率更低。随后我们分析主体的个体交易决策，发现不同模型家族和市场角色之间存在显著异质性。我们还对主体生成的思维链（CoT）轨迹进行了词汇分析，发现执行交易而非继续逐步调整价格的决策，与从战略考虑向紧迫性的转变相关。我们公开发布了我们的测试框架，可用于未来的评估。
英文摘要
Large language models (LLMs) are increasingly deployed as economic agents, yet there is little evidence whether LLM agents are suited for participating in market mechanisms designed for humans, and whether these mechanisms deliver desired outcomes when faced with LLM agents. We address this question by replicating seminal economic experiments, replacing human subjects with LLM agents. We place agents in a double auction environment, which is a widely-used market mechanism. We check whether such a market is able to deliver an efficient allocation of resources, thereby testing a novel dimension of alignment of LLM agents -- their compatibility with a fundamental market mechanism. We find that markets populated by LLM agents exhibit slower or no convergence towards market equilibrium, thus providing less efficient allocations than markets populated by humans. We then analyze agents' individual trading decisions and find substantial heterogeneity both across model families and market roles. We also run a lexical analysis of Chain-of-Thought (CoT) traces generated by the agents. We find that the decision to execute a trade rather than continue incrementally adjusting prices is associated with a shift from strategic considerations toward urgency. We publicly release our testing framework, which can be used for future evaluations.
