Predicting and understanding how people make decisions has been a long-standing goal in many fields, with quantitative models of human decision-making informing research in both the social sciences and engineering. We show how progress toward this goal can be accelerated by using large datasets to power machine-learning algorithms that are constrained to produce interpretable psychological theories. Conducting the largest experiment on risky choice to date and analyzing the results using gradient-based optimization of differentiable decision theories implemented through artificial neural networks, we were able to recapitulate historical discoveries, establish that there is room to improve on existing theories, and discover a new, more accurate model of human decision-making in a form that preserves the insights from centuries of research.

预测和理解人们如何做出决策一直是许多领域的长期目标，人类决策的定量模型为社会科学和工程学的研究提供了信息。我们展示了如何通过使用大型数据集来支持机器学习算法来加速实现这一目标，这些算法被限制为产生可解释的心理学理论。进行迄今为止最大的风险选择实验，并使用通过人工神经网络实现的可微决策理论的基于梯度的优化来分析结果，我们能够概括历史发现，确定现有理论有改进的空间，并发现新的、更准确的人类决策模型，其形式保留了数百年研究的见解。