用AI代理模拟交易市场，发现其行为像人且能制造泡沫。
Dissecting AI Trading: Behavioral Finance and Market Bubbles
- 用大语言模型模拟交易员，观察其心理偏差和预测模式。
- 市场中出现价格泡沫，交易量与意见分歧正相关。
- 通过改写提示词可控制行为，影响泡沫大小。
我们研究了人工智能代理在实验性资产市场中的预期形成与交易行为。通过一个由自主大型语言模型（LLM）代理组成的模拟开放拍卖市场，发现三个主要结果：第一，AI代理表现出经典行为金融特征，包括显著的处置效应和基于近期趋势的外推性信念；第二，这些个体层面的行为模式汇聚为均衡动态，复现了经典实验发现（Smith et al., 1988），包括超额需求对后续价格的预测力，以及意见分歧与交易量之间的正相关关系；第三，通过采用二十机制评分框架分析代理的推理文本，我们证明针对性的提示干预可因果性地放大或抑制特定行为机制，显著改变市场泡沫的规模。
原文摘要 · Abstract (English)
We study how AI agents form expectations and trade in experimental asset markets. Using a simulated open-call auction populated by autonomous Large Language Model (LLM) agents, we document three main findings. First, AI agents exhibit classic behavioral patterns: a pronounced disposition effect and recency-weighted extrapolative beliefs. Second, these individual-level patterns aggregate into equilibrium dynamics that replicate classic experimental findings (Smith et al., 1988), including the predictive power of excess demand for future prices and the positive relationship between disagreement and trading volume. Third, by analyzing the agents' reasoning text through a twenty-mechanism scoring framework, we show that targeted prompt interventions causally amplify or suppress specific behavioral mechanisms, significantly altering the magnitude of market bubbles.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。