When predictions support decisions they may influence the outcome they aim to predict. We call such predictions performative; the prediction influences the target. Performativity is a well-studied phenomenon in policy-making that has so far been neglected in supervised learning. When ignored, performativity surfaces as undesirable distribution shift, routinely addressed with retraining. We develop a risk minimization framework for performative prediction bringing together concepts from statistics, game theory, and causality. A conceptual novelty is an equilibrium notion we call performative stability. Performative stability implies that the predictions are calibrated not against past outcomes, but against the future outcomes that manifest from acting on the prediction. Our main results are necessary and sufficient conditions for the convergence of retraining to a performatively stable point of nearly minimal loss. In full generality, performative prediction strictly subsumes the setting known as strategic classification. We thus also give the first sufficient conditions for retraining to overcome strategic feedback effects.

中文翻译：

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表演预测

当预测支持决策时，它们可能会影响他们旨在预测的结果。我们称这种预测为表演性的；预测影响目标。表演性是政策制定中一种经过充分研究的现象，迄今为止在监督学习中一直被忽视。当被忽视时，表现性会表现为不良的分布转移，通常通过再培训来解决。我们开发了一个风险最小化框架，用于将统计学、博弈论和因果关系中的概念结合在一起的执行预测。概念新颖性是一种平衡概念，我们称之为表演稳定性。表演稳定性意味着预测不是根据过去的结果进行校准，而是根据根据预测采取行动所体现的未来结果进行校准。我们的主要结果是重新训练收敛到几乎最小损失的性能稳定点的充分必要条件。总的来说，执行性预测严格包含了称为战略分类的设置。因此，我们也给出了再培训克服战略反馈效应的第一个充分条件。