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Scientific discovery in a model-centric framework: Reproducibility, innovation, and epistemic diversity

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  • Berna Devezer
  • Luis G Nardin
  • Bert Baumgaertner
  • Erkan Ozge Buzbas

Abstract

Consistent confirmations obtained independently of each other lend credibility to a scientific result. We refer to results satisfying this consistency as reproducible and assume that reproducibility is a desirable property of scientific discovery. Yet seemingly science also progresses despite irreproducible results, indicating that the relationship between reproducibility and other desirable properties of scientific discovery is not well understood. These properties include early discovery of truth, persistence on truth once it is discovered, and time spent on truth in a long-term scientific inquiry. We build a mathematical model of scientific discovery that presents a viable framework to study its desirable properties including reproducibility. In this framework, we assume that scientists adopt a model-centric approach to discover the true model generating data in a stochastic process of scientific discovery. We analyze the properties of this process using Markov chain theory, Monte Carlo methods, and agent-based modeling. We show that the scientific process may not converge to truth even if scientific results are reproducible and that irreproducible results do not necessarily imply untrue results. The proportion of different research strategies represented in the scientific population, scientists’ choice of methodology, the complexity of truth, and the strength of signal contribute to this counter-intuitive finding. Important insights include that innovative research speeds up the discovery of scientific truth by facilitating the exploration of model space and epistemic diversity optimizes across desirable properties of scientific discovery.

Suggested Citation

  • Berna Devezer & Luis G Nardin & Bert Baumgaertner & Erkan Ozge Buzbas, 2019. "Scientific discovery in a model-centric framework: Reproducibility, innovation, and epistemic diversity," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-23, May.
  • Handle: RePEc:plo:pone00:0216125
    DOI: 10.1371/journal.pone.0216125
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    Cited by:

    1. Eric W Bridgeford & Shangsi Wang & Zeyi Wang & Ting Xu & Cameron Craddock & Jayanta Dey & Gregory Kiar & William Gray-Roncal & Carlo Colantuoni & Christopher Douville & Stephanie Noble & Carey E Prieb, 2021. "Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics," PLOS Computational Biology, Public Library of Science, vol. 17(9), pages 1-20, September.
    2. Bart Penders & J. Britt Holbrook & Sarah de Rijcke, 2019. "Rinse and Repeat: Understanding the Value of Replication across Different Ways of Knowing," Publications, MDPI, vol. 7(3), pages 1-15, July.
    3. Tobias Thomas & Dominik Straub & Fabian Tatai & Megan Shene & Tümer Tosik & Kristian Kersting & Constantin A. Rothkopf, 2024. "Modelling dataset bias in machine-learned theories of economic decision-making," Nature Human Behaviour, Nature, vol. 8(4), pages 679-691, April.

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