Predicting human decision-making under risk and uncertainty represents a quintessential challenge that spans economics, psychology, and related disciplines. Despite decades of research effort, no model can be said to accurately describe and predict human choice even for the most stylized tasks like choice between lotteries. Here, we introduce BEAST Gradient Boosting (BEAST-GB), a novel hybrid model that synergizes behavioral theories, specifically the model BEAST, with machine learning techniques. First, we show the effectiveness of BEAST-GB by describing CPC18, an open competition for prediction of human decision making under risk and uncertainty, in which BEAST-GB won. Second, we show that it achieves state-of-the-art performance on the largest publicly available dataset of human risky choice, outperforming purely data-driven neural networks, indicating the continued relevance of BEAST theoretical insights in the presence of large data. Third, we demonstrate BEAST-GB's superior predictive power in an ensemble of choice experiments in which the BEAST model alone falters, underscoring the indispensable role of machine learning in interpreting complex idiosyncratic behavioral data. Finally, we show BEAST-GB also displays robust domain generalization capabilities as it effectively predicts choice behavior in new experimental contexts that it was not trained on. These results confirm the potency of combining domain-specific theoretical frameworks with machine learning, underscoring a methodological advance with broad implications for modeling decisions in diverse environments.

翻译：在风险与不确定性下预测人类决策是横跨经济学、心理学及相关学科的一项典型挑战。尽管经过数十年研究，即便对于彩票选择这类最简化的任务，尚无模型能准确描述和预测人类选择。本文提出BEAST梯度提升（BEAST-GB），一种将行为理论（具体为BEAST模型）与机器学习技术相结合的新型混合模型。首先，我们通过描述CPC18（一项预测风险与不确定性下人类决策的公开竞赛）中BEAST-GB的胜出来证明其有效性；其次，我们证明其在最大的公开人类风险选择数据集上达到了最先进性能，超越了纯数据驱动的神经网络，表明在大数据背景下BEAST理论洞见仍具有持续相关性；再者，我们通过一系列BEAST模型单独表现欠佳的选择实验集成，展示了BEAST-GB的卓越预测能力，凸显机器学习在解读复杂异质性行为数据中不可或缺的作用；最后，我们揭示BEAST-GB还展现出稳健的域泛化能力，能有效预测其未训练过的新实验情境中的选择行为。这些结果证实了将领域特定理论框架与机器学习相结合的效力，标志着一种对多样化环境下决策建模具有广泛意义的方法论突破。