Interrelations among scientific fields and their relative influence revealed by input-output analysis

TL;DR

This paper adapts input-output analysis to scientific disciplines, revealing the influence and importance of subfields like statistical physics and quantum mechanics through eigenvalue-based metrics.

Contribution

It introduces a novel eigenvalue-based input-output method to quantify influence among scientific subfields, applied here to physics.

Findings

Statistical physics has high influence despite not being the largest subfield.

Mechanical control of atoms strongly influences quantum mechanics.

The method effectively identifies influential and interconnected subfields.

Abstract

In this paper, we try to answer two questions about any given scientific discipline: First, how important is each subfield and second, how does a specific subfield influence other subfields? We modify the well-known open-system Leontief Input-Output Analysis in economics into a closed-system analysis focusing on eigenvalues and eigenvectors and the effects of removing one subfield. We apply this method to the subfields of physics. This analysis has yielded some promising results for identifying important subfields (for example the field of statistical physics has large influence while it is not among the largest subfields) and describing their influences on each other (for example the subfield of mechanical control of atoms is not among the largest subfields cited by quantum mechanics, but our analysis suggests that these fields are strongly connected). This method is potentially applicable to more general systems that have input-output relations among their elements.