The decisions of individuals and organizations are often suboptimal because fully rational decision-making is too demanding in the real world. Recent work suggests that some errors can be prevented by leveraging artificial intelligence to discover and teach clever heuristics. So far, this line of research has been limited to simplified, artificial decision-making tasks. This article is the first to extend this approach to a real-world decision problem, namely, executives deciding which project their organization should launch next. We develop a computational method (MGPS) that automatically discovers project selection strategies that are optimized for real people, and we develop an intelligent tutor that teaches the discovered project selection procedures. 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, people who practiced with our intelligent tutor learned significantly better project selection strategies than the control groups. These findings suggest that AI could be used to automate the process of discovering and formalizing the cognitive strategies taught by intelligent tutoring systems.

翻译：个体与组织的决策常因现实世界中完全理性决策的苛刻性而难以达到最优。近期研究表明，通过人工智能发现并传授高效启发式方法可预防部分决策失误。目前该研究领域仅限于简化的人工决策任务。本文首次将这一方法拓展至现实决策问题——即企业管理者决定组织下一步应启动何种项目。我们开发了一种计算方法（MGPS），可自动发现适用于真实人群的优化项目选择策略，并构建了传授这些项目选择流程的智能辅导系统。我们在计算基准测试中评估了MGPS，并通过包含两种对照条件的训练实验测试了智能辅导系统。MGPS在性能上超越了现有先进方法且计算效率更高。此外，使用智能辅导系统进行训练的人员，其掌握的项目选择策略显著优于对照组。这些发现表明，人工智能可用于自动化发现和形式化智能辅导系统所教授的认知策略。