  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.
