Predicting human decisions under risk and uncertainty remains a fundamental challenge across disciplines. Existing models often struggle even in highly stylized tasks like choice between lotteries. We introduce BEAST Gradient Boosting (BEAST-GB), a hybrid model integrating behavioral theory (BEAST) with machine learning. We first present CPC18, a competition for predicting risky choice, in which BEAST-GB won. Then, using two large datasets, we demonstrate BEAST-GB predicts more accurately than neural networks trained on extensive data and dozens of existing behavioral models. BEAST-GB also generalizes robustly across unseen experimental contexts, surpassing direct empirical generalization, and helps refine and improve the behavioral theory itself. Our analyses highlight the potential of anchoring predictions on behavioral theory even in data-rich settings and even when the theory alone falters. Our results underscore how integrating machine learning with theoretical frameworks, especially those-like BEAST-designed for prediction, can improve our ability to predict and understand human behavior.

翻译：在风险与不确定性条件下预测人类决策，始终是跨学科领域的根本性挑战。现有模型即使在高度简化的任务（如彩票选择）中也常常表现不佳。我们提出了BEAST梯度提升（BEAST-GB），这是一种将行为理论（BEAST）与机器学习相结合的混合模型。我们首先介绍了预测风险选择的竞赛CPC18，BEAST-GB在该竞赛中获胜。随后，利用两个大型数据集，我们证明BEAST-GB的预测准确性优于基于大量数据训练的神经网络以及数十个现有行为模型。BEAST-GB在未见过的实验情境中也展现出稳健的泛化能力，超越了直接的经验泛化，并有助于改进和完善行为理论本身。我们的分析强调了即使在数据丰富的场景下，甚至在理论本身表现不足时，将预测锚定于行为理论仍具有重要潜力。研究结果进一步表明，将机器学习与理论框架（尤其是像BEAST这样为预测而设计的框架）相结合，能够提升我们预测和理解人类行为的能力。