Algorithmic predictions steer markets, drive consumption, shape communities, and alter life trajectories. The theory and practice of machine learning, however, has long neglected the often invisible causal forces of prediction. A recent conceptual framework, called performative prediction, draws attention to the fundamental difference between learning from a population and steering a population through predictions. After covering some emerging insights on performative prediction, the lecture turns to an application of performativity to the question of power in digital economies. Traditional economic concepts struggle with identifying anti-competitive patterns in digital platforms not least due to the difficulty of defining the market. I will introduce the notion of performative power that sidesteps the complexity of market definition and directly measures how much a firm can benefit from steering consumer behavior. I end on a discussion of the normative implications of high performative power, its connections to measures of market power in economics, and its relationship to ongoing antitrust debates.

The talk is based on joint works with Meena Jagadeesan, Celestine Mendler-Dünner, Juan C. Perdomo, and Tijana Zrnic.