用分层知识架构模拟人类行为，提升金融系统预测精度。
Advanced simulation paradigm of human behaviour unveils complex financial systemic projection
- 构建分层知识架构驱动的智能体，模拟不同人群行为
- 危机场景下价格预测偏差仅13.29%，峰值涨幅达285.34%
- 适合金融建模、行为经济学与市场风险分析者
人类行为的高阶复杂性是金融市场预测困难的根本原因。本文提出一种基于智能体的新型行为模拟范式，每个智能体由分层知识架构支持，融合语言模型与专业模型，模拟特定情境下的行为过程。在期货市场测试中，该模拟器在价格飙升达285.34%的危机场景下，预测偏差仅为13.29%；在正常条件下，对特定商品期货价格的均方误差也更低。该方法实现了非量化信息与多样化市场行为的融合，为投资者行为及其对市场动态的影响提供了可信赖的仿真平台。
原文摘要 · Abstract (English)
The high-order complexity of human behaviour is likely the root cause of extreme difficulty in financial market projections. We consider that behavioural simulation can unveil systemic dynamics to support analysis. Simulating diverse human groups must account for the behavioural heterogeneity, especially in finance. To address the fidelity of simulated agents, on the basis of agent-based modeling, we propose a new paradigm of behavioural simulation where each agent is supported and driven by a hierarchical knowledge architecture. This architecture, integrating language and professional models, imitates behavioural processes in specific scenarios. Evaluated on futures markets, our simulator achieves a 13.29% deviation in simulating crisis scenarios whose price increase rate reaches 285.34%. Under normal conditions, our simulator also exhibits lower mean square error in predicting futures price of specific commodities. This technique bridges non-quantitative information with diverse market behaviour, offering a promising platform to simulate investor behaviour and its impact on market dynamics.
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