When users stand to gain from certain predictions, they are prone to act strategically to obtain favorable predictive outcomes. Whereas most works on strategic classification consider user actions that manifest as feature modifications, we study a novel setting in which users decide -- in response to the learned classifier -- whether to at all participate (or not). For learning approaches of increasing strategic awareness, we study the effects of self-selection on learning, and the implications of learning on the composition of the self-selected population. We then propose a differentiable framework for learning under self-selective behavior, which can be optimized effectively. We conclude with experiments on real data and simulated behavior that both complement our analysis and demonstrate the utility of our approach.

翻译：当用户从特定预测结果中获益时，他们倾向于采取策略性行为以获取有利的预测结果。与大多数考虑特征修改行为的策略分类研究不同，我们探索了一种新颖的场景——用户根据学习到的分类器决定是否参与（或不参与）。针对策略意识不断增强的学习方法，我们研究了自我选择对学习过程的影响，以及学习结果对自我选择群体构成的反馈作用。在此基础上，我们提出了一种可微分的框架来建模自我选择行为下的学习过程，该框架能够被高效优化。最后，我们通过真实数据与模拟行为的实验，既验证了理论分析的有效性，又展示了所提出方法的实用价值。