用大模型模拟市场行为，发现其能复现人类决策的宏观趋势。
Can Generative AI agents behave like humans? Evidence from laboratory market experiments
- 让大模型在动态市场中相互影响，通过价格反馈调整决策。
- 仅需3步历史记忆+高多样性设置，就能复现正负反馈市场的整体趋势。
- 虽能模拟人类宏观行为，但个体差异仍小于真实人类，适合行为仿真研究。
我们研究大型语言模型（LLMs）在经济市场实验中复制人类行为的潜力。与以往研究不同，本文关注LLM代理间的动态反馈：每个LLM的决策影响当前市场价格，进而影响下一时刻其他LLM的决策。将LLM行为与实验室市场数据对比，发现它们不严格遵循理性预期，而是表现出类似人类的有限理性。仅提供三个前序时间步的记忆窗口，并结合捕捉响应异质性的高变异性设置，即可使LLM复现人类实验中的主要趋势，如正反馈与负反馈市场的区别。然而，在细粒度层面仍存在差异——LLM的行为异质性低于人类。结果表明，LLM在经济情境中模拟真实人类行为方面具有潜力，但仍需进一步研究以提升准确性和行为多样性。
原文摘要 · Abstract (English)
We explore the potential of Large Language Models (LLMs) to replicate human behavior in economic market experiments. Compared to previous studies, we focus on dynamic feedback between LLM agents: the decisions of each LLM impact the market price at the current step, and so affect the decisions of the other LLMs at the next step. We compare LLM behavior to market dynamics observed in laboratory settings and assess their alignment with human participants' behavior. Our findings indicate that LLMs do not adhere strictly to rational expectations, displaying instead bounded rationality, similarly to human participants. Providing a minimal context window i.e. memory of three previous time steps, combined with a high variability setting capturing response heterogeneity, allows LLMs to replicate broad trends seen in human experiments, such as the distinction between positive and negative feedback markets. However, differences remain at a granular level--LLMs exhibit less heterogeneity in behavior than humans. These results suggest that LLMs hold promise as tools for simulating realistic human behavior in economic contexts, though further research is needed to refine their accuracy and increase behavioral diversity.
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