Is Ockham’s razor losing its edge? New perspectives on the principle of model parsimony

The preference for simple explanations, known as the parsimony
principle, has long guided the development of scientific theories,
hypotheses, and models. Yet recent years have seen a number of
successes in employing highly complex models for scientific inquiry
(e.g., for 3D protein folding or climate forecasting). In this paper,
we re-examine the parsimony principle in light of these scientific
and technological advancements. We review recent developments,
including the surprising benefits of modeling with more parameters
than data, the increasing appreciation of the context-sensitivity of
data and misspecification of scientific models, and the development
of new modeling tools. By integrating these insights, we reassess
the utility of parsimony as a proxy for desirable model traits, such
as predictive accuracy, interpretability, effectiveness in guiding new
research, and resource efficiency. We conclude that more complex
models are sometimes essential for scientific progress, and discuss
the ways in which parsimony and complexity can play complementary
roles in scientific modeling practice.