Human decision-making in real-life deviates significantly from the optimal decisions made by fully rational agents, primarily due to computational limitations or psychological biases. While existing studies in behavioral finance have discovered various aspects of human sub-rationality, there lacks a comprehensive framework to transfer these findings into an adaptive human model applicable across diverse financial market scenarios. In this study, we introduce a flexible model that incorporates five different aspects of human sub-rationality using reinforcement learning. Our model is trained using a high-fidelity multi-agent market simulator, which overcomes limitations associated with the scarcity of labeled data of individual investors. We evaluate the behavior of sub-rational human investors using hand-crafted market scenarios and SHAP value analysis, showing that our model accurately reproduces the observations in the previous studies and reveals insights of the driving factors of human behavior. Finally, we explore the impact of sub-rationality on the investor's Profit and Loss (PnL) and market quality. Our experiments reveal that bounded-rational and prospect-biased human behaviors improve liquidity but diminish price efficiency, whereas human behavior influenced by myopia, optimism, and pessimism reduces market liquidity.

翻译：人类在现实生活中的决策与完全理性主体的最优决策显著偏离，主要源于计算局限或心理偏差。尽管行为金融学的现有研究已发现人类亚理性的多个方面，但缺乏一个综合框架将这些发现转化为适用于不同金融市场场景的自适应人类模型。本研究引入了一个灵活模型，通过强化学习整合了人类亚理性的五个不同方面。该模型利用高保真多智能体市场模拟器进行训练，克服了个体投资者标记数据稀缺的相关局限。我们通过人工构建的市场场景及SHAP值分析评估亚理性人类投资者的行为，结果表明该模型能准确复现前人研究中的观察结果，并揭示人类行为驱动因素的深层见解。最后，我们探讨了亚理性对投资者损益及市场质量的影响。实验揭示：有限理性与展望偏差行为改善了流动性但降低了价格效率，而受短视、乐观与悲观影响的人类行为则降低了市场流动性。