UAI 2024 Tutorial 1: An Introduction to Performative Prediction

Celestine Mendler-Duenner, Tijana Zrnic
Predictions in the social world generally influence the target of prediction, a phenomenon known as performativity. Self-fulfilling and self-negating predictions are examples of performativity. Of fundamental importance to economics, finance, and the social sciences, the notion has long been absent from the development of machine learning. In machine learning applications, performativity surfaces as distribution shift. A predictive model deployed on a digital platform, for example, influences consumption and thereby changes the data-generating distribution. We will provide an introduction to the recently founded area of performative prediction that provides a definition and conceptual framework to study performativity in machine learning. A consequence of performative prediction is a natural equilibrium notion that gives rise to new optimization challenges. Another consequence is a distinction between learning and steering, two mechanisms at play in performative prediction. The notion of steering is in turn intimately related to questions of power in digital markets. Throughout, we will focus on presenting the key technical results in performative prediction and highlight connections to statistics, game theory, and causality. Finally, we end on a discussion of future directions, such as the role that performativity plays in contesting algorithmic systems.
Slides:
www.auai.org/u...