用多智能体模拟投资机构层级决策，效果接近真人交易员。
Hierarchical Organization Simulacra in the Investment Sector
- 构建分层智能体，用新闻驱动投资决策。
- 15年300家公司超11.5万条新闻验证，结果与真人高度一致。
- 提示词和角色等级影响判断，暴露LLM决策偏差。
本文通过多智能体仿真设计具备专业行为的投资人工组织，模拟投资公司中的层级决策机制，利用新闻文章作为决策依据。一项大规模研究分析了15年间300家公司的超过11.5万篇新闻文章，将该方法与专业交易员的决策进行对比。结果显示，分层模拟在决策频率和盈利能力上均与专业交易员高度吻合。然而，研究也揭示出显著的决策偏差：提示词表述方式及代理角色的“资历感”会显著影响结果，凸显大型语言模型在复制专业金融决策时的潜力与局限。
原文摘要 · Abstract (English)
This paper explores designing artificial organizations with professional behavior in investments using a multi-agent simulation. The method mimics hierarchical decision-making in investment firms, using news articles to inform decisions. A large-scale study analyzing over 115,000 news articles of 300 companies across 15 years compared this approach against professional traders' decisions. Results show that hierarchical simulations align closely with professional choices, both in frequency and profitability. However, the study also reveals biases in decision-making, where changes in prompt wording and perceived agent seniority significantly influence outcomes. This highlights both the potential and limitations of large language models in replicating professional financial decision-making.
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