Learnable Randomization as Commitment Against Adaptive Optimizers
A pricing page can walk the posted price up to the last amount a buyer still accepts, a recommender can hold back a better item for a barely acceptable promoted one, and a classifier can shift its boundary once applicants change their features. The system predicts the response and then picks the menu that serves its own objective, so the surplus above the user's cutoff is taken. Playing the single best action publishes that cutoff, while noise on actions the user would never take throws away payoff and teaches the platform that a worse menu is still acceptable. We study unpredictable near-optimal policies (UNOP), which mix uniformly on near-best actions that remain individually rational. ...
| Authors | Zihan Deng, Chuanzhi Xu, Xiaozhen Zhong et al. |
|---|---|
| Primary category | cs.GT |
| Published | 26 September 2026 |
| Last updated at source | 26 September 2026 |
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