Linear Strategic Classification with Endogenous Improvements
Classical models of strategic classification treat agents' responses to a deployed classifier as cosmetic: features change, but true labels do not. We study an improvement-aware variant in which strategic responses can change outcomes: agents choose post-deployment features strategically at a cost, and labels are then drawn from a fixed conditional outcome law. We formalize this problem for linear classifiers under a single-index qualification model and linear-decomposable costs. Under these assumptions, agents' best responses reduce to a closed-form shift of the decision boundary, and the resulting improvement-aware classification problem is learnable with linear sample complexity. As po...
| Authors | Siddharth Shrivastava, Mahvith Akshintala, B Vamsha Vardhan Reddy et al. |
|---|---|
| Primary category | cs.LG |
| Published | 31 May 2026 |
| Last updated at source | 15 October 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.