The decisions of individuals and organizations are often suboptimal because normative decision strategies are too demanding in the real world. Recent work suggests that some errors can be prevented by leveraging artificial intelligence to discover and teach prescriptive decision strategies that take people's constraints into account. So far, this line of research has been limited to simplified decision problems. This article is the first to extend this approach to a real-world decision problem, namely project selection. We develop a computational method (MGPS) that automatically discovers project selection strategies that are optimized for real people and develop an intelligent tutor that teaches the discovered strategies. We evaluated MGPS on a computational benchmark and tested the intelligent tutor in a training experiment with two control conditions. MGPS outperformed a state-of-the-art method and was more computationally efficient. Moreover, the intelligent tutor significantly improved people's decision strategies. Our results indicate that our method can improve human decision-making in naturalistic settings similar to real-world project selection, a first step towards applying strategy discovery to the real world.

翻译：个人和组织的决策往往并非最优，因为现实世界中的规范性决策策略要求过高。近期研究表明，通过利用人工智能发现并教授考虑人类限制条件的规范性决策策略，可以预防部分决策错误。但迄今该研究领域仍局限于简化决策问题。本文首次将这种研究方法拓展至真实决策问题——项目选择领域。我们开发了一种自动发现针对真实人类优化的项目选择策略的计算方法（MGPS），并构建了教授所发现策略的智能辅导系统。我们在计算基准测试中评估了MGPS方法，并在包含两个对照组的训练实验中测试了智能辅导系统。实验结果表明：MGPS方法在性能上超越当前最先进方法，且计算效率更高；同时智能辅导系统显著提升了人们的决策策略水平。研究显示，该方法能够改善类似于真实项目选择的自然情境中的人类决策能力，这标志着策略发现方法向现实世界应用迈出了第一步。