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A theory and methodology to quantify knowledge

Fanelli, Daniele (2019) A theory and methodology to quantify knowledge. Royal Society Open Science, 6 (4). ISSN 2054-5703

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This article proposes quantitative answers to meta-scientific questions including “how much knowledge is attained by a research field?”, “how rapidly is a field making progress?”, “what is the expected reproducibility of a result?”, “how much knowledge is lost from scientific bias and misconduct?” “what do we mean by soft science?”, “what demarcates a pseudoscience?”. Knowledge is suggested to be a system-specific property measured by K, a quantity determined by how much of the information contained in an explanandum is compressed by an explanans, which is composed of an information “input” and a “theory/methodology” conditioning factor. This approach is justified on three grounds: 1) K is derived from postulating that information is finite and knowledge is information compression; 2) K is compatible and convertible to ordinary measures of effect size and algorithmic complexity; 3) K is physically interpretable as a measure of entropic efficiency. Moreover, the K function has useful properties that support its potential as a measure of knowledge. Examples given to illustrate the possible uses of K include: the knowledge value of proving Fermat’s last theorem; the accuracy of measurements of the mass of the electron; the half life of predictions of solar eclipses; the usefulness of evolutionary models of reproductive skew; the significance of gender differences in personality; the sources of irreproducibility in psychology; the impact of scientific misconduct and questionable research practices; the knowledge value of astrology. Furthermore, measures derived from K may complement ordinary meta-analysis and may give rise to a universal classification of sciences and pseudosciences. Simple and memorable mathematical formulae that summarize the theory’s key results may find practical uses in meta-research, philosophy and research policy.

Item Type: Article
Additional Information: © 2019 The Author
Divisions: Methodology
Subjects: Q Science
Date Deposited: 19 Feb 2019 10:45
Last Modified: 22 May 2019 09:15

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