Reaching a Consensus in Predictive Loops

Predictions on digital platforms must adapt over time, as individuals continuously update their beliefs through social interactions. At the same time, changing predictions can in turn influence the content people are exposed to and hence the very beliefs they seek to predict, rather than merely serving as passive observations. These emerging dynamics make it challenging to understand the long term effects of predictive systems on society. In this work, we blend models from network science with concepts from performative prediction to initiate the study of opinion dynamics in predictive loops. In our model, opinions and predictions co-evolve: a platform's predictions influence individual opinions, which then evolve through peer interactions and form the training data for future platform model updates. We demonstrate that this co-evolution induces a novel equilibrium that qualitatively differs from standard network equilibria. In particular, we show how standard predictive objectives can drive networks toward consensus even under conditions where classical opinion-dynamics models lead to disagreement. This emerges because predictive systems dynamically adapt to changing opinions, and learning objectives create spillover effects among individuals beyond the topology of the network. We further analyze systematic deviations from standard prediction and demonstrate amplified effects of targeted platform interventions on equilibrium outcomes, compared to classical network intervention analyses. We complement our results with simulations on real social network data and parametric learning settings. Together, our results illustrate performativity as an important, yet so far neglected, qualifying factor in social networks.