ReviewProceedings of the National Academy of Sciences of the United States of America2025
Is Ockham's razor losing its edge? New perspectives on the principle of model parsimony.
Review in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Illusions of Understanding in the Sciences.Computational brain & behavior · 2026Article
- From observable fermentation data to hidden cell states: A modeling study of a mixotrophic Clostridium coculture under perfusion mode.PLoS computational biology · 2026Article
- Lack of group-to-individual generalizability in pseudocontingencies.Scientific reports · 2026Article
- Video game engines as the new "virtual" Skinner box.Journal of the experimental analysis of behavior · 2026Article
- Model fit vs. predictive reliability: a case study of the 1978 influenza outbreak.Scientific reports · 2025Article
- A complex systems view on physical activity with actionable insights for behaviour change.Nature human behaviour · 2025Review
- Dialogues about the practice of science.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Automating the practice of science: Opportunities, challenges, and implications.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- People Evaluate Agents Based on the Algorithms That Drive Their Behavior.Open mind : discoveries in cognitive science · 2025Article
- A Workflow for Building Computationally Rational Models of Human Behavior.Computational brain & behavior · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.