Mind the hubris

Abstract

Abstract Here we briefly reflect on the philosophical foundations that ground the quest towards ever more detailed models, and identify four practical dangers derived from this pursuit: an explosion of the model’s uncertainty space, model black-boxing, computational exhaustion, and model attachment. We explain why more complex models tend to produce more uncertain estimates and discuss the link between model size and computational bugs. Finally, we evaluate the implications that the end of Moore’s law and Dennard’s scaling have for model development, and show (using hydrological models) that large models tend to become institutionalized. The growth of a mathematical model should be continuously tested against the available evidence lest models become extraneous constructs chasing the Cartesian dream.