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A theory and methodology to quantify knowledge.

Daniele Fanelli1

  • 1Department of Methodology, London School of Economics and Political Science, London, UK.

Royal Society Open Science
|June 12, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new measure, K, to quantify scientific knowledge and progress. This framework offers insights into research fields, reproducibility, and the demarcation of pseudoscience.

Keywords:
biasknowledgemeta-researchmeta-sciencepseudosciencereproducibility

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Area of Science:

  • Meta-science
  • Philosophy of Science
  • Information Theory

Background:

  • Current meta-scientific questions lack quantitative answers.
  • Existing measures do not fully capture the essence of scientific knowledge or progress.

Purpose of the Study:

  • To propose a quantitative framework for measuring scientific knowledge.
  • To address meta-scientific questions regarding research progress, reproducibility, and the science-pseudoscience demarcation.

Main Methods:

  • Introduced a knowledge measure, K, defined as information compression by an explanans from an explanandum.
  • Justified K based on information finiteness, compatibility with effect size and algorithmic complexity, and physical interpretability as entropic efficiency.
  • Illustrated K's application with diverse examples from mathematics, physics, evolutionary biology, psychology, and astrology.

Main Results:

  • K is proposed as a system-specific property measuring knowledge.
  • The K function demonstrates properties suitable for knowledge quantification.
  • Derived measures from K may aid in classifying sciences and pseudosciences and complement meta-analysis.

Conclusions:

  • The K framework offers a novel, quantitative approach to meta-scientific inquiry.
  • This measure has potential applications in meta-research, philosophy of science, and research policy.
  • Mathematical formulae summarizing K's results may prove practically useful.