Linear Strategic Classification with Endogenous Improvements
This study addresses improvement-aware strategic classification, where agents strategically change features after a classifier is deployed to get better outcomes at a cost. It shows that the optimal strategic classifier is simply a parallel shift of the standard Bayes-optimal decision boundary. This shifted classifier serves as a superior proxy for the true improvement-aware objective compared to the non-strategic Bayes classifier.