Performative Prediction: Past and Future
Moritz Hardt Celestine Mendler-Dünner
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 been absent from
the development of machine learning. In machine learning applications,
performativity often surfaces as distribution shift. A predictive model
deployed on a digital platform, for example, influences consumption and thereby
changes the data-generating distribution. We survey 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. We review the
notion of performative power that gives an answer to the question how much a
platform can steer participants through its predictions. We end on a discussion
of future directions, such as the role that performativity plays in contesting
algorithmic systems.