Entia non sunt multiplicanda praeter necessitatem

Or Plurality should not be posited without necessity. The parsimony principle, Ockham’s razor, has long guided the development of scientific theories, hypotheses, and models.

In a new paper¹ published in PNAS this week, Dubova et. al. argue that “Relying on parsimony alone as our guiding principle limits what we can learn about the world and potentially drives us in wrong directions,” which sounds also like another famous saying usually attributed to Albert Einstein: “Everything Should Be Made as Simple as Possible, But Not Simpler.”

Parsimony and complexity are complementary tools. Scientists need to use evidence, judgment, and context-specific demands to determine whether a more parsimonious or complex model suits their research goals.

The autjhors hope that the paper will kickstart new research into when scientific modelers should choose parsimony or complexity. There remain many open questions and unexplored nuances of the principle of parsimony, and they expect the principle of parsimony to both facilitate the evolution of and evolve alongside science itself.

Here is the abstract:

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 reexamine 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.

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(1) Dubova, Marina, Suyog Chandramouli, Gerd Gigerenzer, Peter Grünwald, William Holmes, Tania Lombrozo, Marco Marelli, et al. “Is Ockham’s Razor Losing Its Edge? New Perspectives on the Principle of Model Parsimony.” Proceedings of the National Academy of Sciences 122, no. 5 (February 4, 2025): e2401230121. https://doi.org/10.1073/pnas.2401230121.

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