Deployed supervised machine learning models make predictions that interact with and influence the world. This phenomenon is called "performative prediction" by Perdomo et al. (2020), who investigated it in a stateless setting. We generalize their results to the case where the response of the population to the deployed classifier depends both on the classifier and the previous distribution of the population. We also demonstrate such a setting empirically, for the scenario of strategic manipulation.

中文翻译：

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有状态世界中的执行预测

部署的监督机器学习模型做出与世界互动并影响世界的预测。这种现象被 Perdomo 等人称为“性能预测”。（2020），他在无国籍环境中对其进行了调查。我们将他们的结果概括为人口对部署的分类器的响应取决于分类器和人口的先前分布的情况。对于战略操纵的场景，我们还凭经验证明了这样的设置。