The field of performative prediction had its beginnings in 2020 with the seminal paper "Performative Prediction" by Perdomo et al., which established a novel machine learning setup where the deployment of a predictive model causes a distribution shift in the environment, which in turn causes a mismatch between the distribution expected by the predictive model and the real distribution. This shift is defined by a so-called distribution map. In the half-decade since, a literature has emerged which has, among other things, introduced new solution concepts to the original setup, extended the setup, offered new theoretical analyses, and examined the intersection of performative prediction and other established fields. In this survey, we first lay out the performative prediction setting and explain the different optimization targets: performative stability and performative optimality. We introduce a new way of classifying different performative prediction settings, based on how much information is available about the distribution map. We survey existing implementations of distribution maps and existing methods to address the problem of performative prediction, while examining different ways to categorize them. Finally, we point out known and previously unknown connections that can be drawn to other fields, in the hopes of stimulating future research.

翻译：表现性预测领域始于2020年Perdomo等人的开创性论文《表现性预测》，该文建立了一种新颖的机器学习框架，其中预测模型的部署会导致环境中的分布偏移，进而造成预测模型预期的分布与实际分布之间的不匹配。这种偏移由一个所谓的分布映射所定义。在此后的五年间，相关文献不断涌现，这些工作不仅为原始框架引入了新的解决方案概念，扩展了该框架，提供了新的理论分析，还探讨了表现性预测与其他成熟领域的交叉。在本综述中，我们首先阐述了表现性预测的设置，并解释了不同的优化目标：表现性稳定性和表现性最优性。我们引入了一种基于对分布映射信息的掌握程度来对不同的表现性预测设置进行分类的新方法。我们综述了分布映射的现有实现方法以及解决表现性预测问题的现有技术，同时探讨了对它们进行分类的不同方式。最后，我们指出了该领域与其他领域之间已知及先前未知的联系，以期激发未来的研究。