Rethinking Scaling Laws for Learning in Strategic Environments

The deployment of ever-larger machine learning models reflects a growing consensus that the more expressive the model$\unicode{x2013}$and the more data one has access to$\unicode{x2013}$the more one can improve performance. As models get deployed in a variety of real world scenarios, they inevitably face strategic environments. In this work, we consider the natural question of how the interplay of models and strategic interactions affects scaling laws. We find that strategic interactions can break the conventional view of scaling laws$\unicode{x2013}$meaning that performance …

consensus cs.gt cs.lg data deployment environments face laws machine machine learning machine learning models natural performance scaling stat.ml unicode work world