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.

翻译：机器学习系统常常用于个人调整其特征以获得理想结果的环境。 在这种环境中,战略行为导致模型部署性能急剧下降。 在这项工作中,我们的目标是通过学习分类人员来解决这一问题,这些分类人员鼓励决策对象改变其特征,从而改进预测的/emph{and}真实结果。我们把预测和适应的动态设定为两阶段游戏,并为模型设计者及其决策对象确定最佳战略。在模拟和真实世界数据集的基准中,我们发现,使用我们的方法培训的分类人员保持现有方法的准确性,同时提高改进水平,减少操纵。