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

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 irrepr...

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Vydané v:	PloS one Ročník 14; číslo 5; s. e0216125
Hlavní autori:	Devezer, Berna, Nardin, Luis G., Baumgaertner, Bert, Buzbas, Erkan Ozge
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Public Library of Science 15.05.2019 Public Library of Science (PLoS)
Predmet:	Biology and Life Sciences Computer simulation Discovery and exploration Ecology and Environmental Sciences Epistemology Experiments Hypotheses Information theory Markov chains Markov models Markov processes Mathematical models MATHEMATICS AND COMPUTING Models, Theoretical Monte Carlo methods Monte Carlo simulation People and Places Philosophy of science Physical Sciences Properties (attributes) Replication studies Reproducibility Reproducibility of Results Research and Analysis Methods Research Design Science & Technology - Other Topics Science - methods Science Policy Scientific discoveries Scientists Society Space exploration Species diversity Statistical data Statistical theories Stochastic processes Stochasticity Studies United States > US
ISSN:	1932-6203, 1932-6203
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Popis
Shrnutí:	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.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 USDOE AC07-05ID14517 Competing Interests: The authors have declared that no competing interests exist.
ISSN:	1932-6203 1932-6203
DOI:	10.1371/journal.pone.0216125