Daniele Fanelli: Low reproducibility as divergent information: A K-theory analysis of reproducibility studies

This talk will illustrate how reproducilbility and replication in science can be studied using and a new theory and methodology called K-theory. Based on a classic and algorithmic information theory, K-theory is a candidate theoretical framework for metascience, which offers elegant mathematical answers to “big” 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?’, and
• ‘what demarcates a pseudoscience?’
  (see https://doi.org/10.1098/rsos.181055).

We will briefly introduce K-theory, then look at how the theory understands reproducibility and what predictions it makes, and then present results of a “K-analysis” of reproducibility data in psychology.

Daniele Fanelli

Fellow in quantitative methodology, London School of Economics and Political Science

Daniele Fanelli is a fellow in Quantitative Methodology at the London School of Economics, UK, where he teaches research methods and investigates the nature of science and possible issues with scientific evidence. He graduated in Natural Sciences, giving exams in all fundamental disciplines, then obtained a PhD studying the behaviour and genetics of social wasps, and subsequently worked for two years as a science writer. All of his postdoctoral work has been devoted to studying the nature of science itself, and the mis-behaviours of scientists. His empirical research has been instrumental in quantifying the prevalence and causes of problems that may affect research across the natural and social sciences, and it has helped develop remedies and preventive measures.In addition to his scientific work, Daniele co-chairs the Research Integrity Sub-Committee within the Research Ethics and Bioethics Advisory Committee of Italy’s National Research Council, for which he developed the first research integrity guidelines. He is also a member of the Research Integrity Committee of the Luxembourg Agency for Research Integrity (LARI), was formerly a member of Canada’s Tri-Council Expert Panel on Research integrity, and is currently rapporteur for a European Mutual Learning Exercise on Research Integrity.Before joining the London School of Economics, Daniele worked at the University of Edinburgh, UK, at the University of Montreal, CA, and at Stanford University, USA, in the Meta-Research Innovation Center @ Stanford (METRICS).