  Machine learning systems are often used in settings where individuals adapt
their features to obtain a desired outcome. In such settings, strategic
behavior leads to a sharp loss in model performance in deployment. In this
work, we aim to address this problem by learning classifiers that encourage
decision subjects to change their features in a way that leads to improvement
in both predicted \emph{and} true outcome. We frame the dynamics of prediction
and adaptation as a two-stage game, and characterize optimal strategies for the
model designer and its decision subjects. In benchmarks on simulated and
real-world datasets, we find that classifiers trained using our method maintain
the accuracy of existing approaches while inducing higher levels of improvement
and less manipulation.
