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

• 成果类型: Article
• 署名作者: Dubova, Marina; Chandramouli, Suyog; Gigerenzer, Gerd; Grunwaldf, Peter; Holmes, William; Lombrozo, Tania; Marelli, Marco; Musslick, Sebastian; Nicenboim, Bruno; Ross, Lauren N.; Shiffrin, Richard; White, Martha; Wagenmakers, Eric-Jan; Buerkner, Paul-Christian; Sloman, Sabina J.
• 署名单位: Indiana University System; Indiana University Bloomington; Aalto University; University of Alberta; Leiden University; Leiden University - Excl LUMC; Princeton University; University of Milano-Bicocca; University Osnabruck; Brown University; Tilburg University; University of California System; University of California Irvine; Indiana University System; Indiana University Bloomington; University of Amsterdam; Dortmund University of Technology; University of Manchester
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-13858
• DOI: 10.1073/pnas.2401230121
• 发表日期: 2025-02-04
• 关键词: occams razor; explanation; time; predictability; simplicity; CHOICE; rules; fit

摘要

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.