Performative prediction studies feedback loops that arise when predictive models are deployed in consequential domains. In these settings, deploying a model can change the population whose patterns the model aims to predict, inducing a distribution shift that is endogenous to the learning system. This perspective departs from classical treatments of distribution shift, where shifts are typically modeled as exogenous changes in the data-generating process. Yet, in practice, distribution shift is rarely one or the other. Predictive models may influence future data through the decisions they support, while the world itself continues to drift for reasons beyond the learner's control. We study partially performative prediction, a framework that captures both endogenous and exogenous sources of distribution shift. The framework generalizes performative prediction by allowing the data distribution to evolve both in response to the deployed model and according to an external, time-varying process. We extend the central notions of performative stability and performative optimality to this setting by defining their online analogues that track the evolving partially performative environment. We analyze practical learning heuristics, including repeated retraining, and characterize when they successfully adapt to partially performative environments.

翻译：表现性预测研究当预测模型部署于影响深远领域时产生的反馈循环。在这些情境中，部署模型可能改变模型旨在预测的群体格局，引发内生性分布偏移，这种偏移源于学习系统本身。这一视角区别于经典的分布偏移处理方法——后者通常将偏移建模为数据生成过程中外生性的变化。然而在实践中，分布偏移很少单纯属于其中一种。预测模型可能通过其支持的决策影响未来数据，而世界本身也因学习系统无法控制的原因持续漂移。我们研究"部分表现性预测"这一框架，该框架同时捕捉分布偏移的内生性与外生性来源。该框架通过允许数据分布既响应部署模型又根据外部时变过程演化，从而泛化了表现性预测。我们将表现性稳定性和表现性最优性等核心概念扩展到这一设定，通过定义其在线对应物来追踪演化中的部分表现性环境。我们分析包括重复再训练在内的实用学习启发式方法，并刻画其在部分表现性环境中成功适应的条件。