Quantifying patterns of research-interest evolution

Tao Jia (), Dashun Wang () and Boleslaw K. Szymanski ()
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Tao Jia: College of Computer and Information Science, Southwest University
Dashun Wang: Kellogg School of Management, Northwestern University
Boleslaw K. Szymanski: Social Cognitive Networks Academic Research Center, Rensselaer Polytechnic Institute

Nature Human Behaviour, 2017, vol. 1, issue 4, 1-7

Abstract: Abstract To understand quantitatively how scientists choose and shift their research focus over time is of high importance, because it affects the ways in which scientists are trained, science is funded, knowledge is organized and discovered, and excellence is recognized and rewarded1–9. Despite extensive investigation into various factors that influence a scientist’s choice of research topics8–21, quantitative assessments of mechanisms that give rise to macroscopic patterns characterizing research-interest evolution of individual scientists remain limited. Here we perform a large-scale analysis of publication records, and we show that changes in research interests follow a reproducible pattern characterized by an exponential distribution. We identify three fundamental features responsible for the observed exponential distribution, which arise from a subtle interplay between exploitation and exploration in research-interest evolution5,22. We developed a random-walk-based model, allowing us to accurately reproduce the empirical observations. This work uncovers and quantitatively analyses macroscopic patterns that govern changes in research interests, thereby showing that there is a high degree of regularity underlying scientific research and individual careers.

Date: 2017
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DOI: 10.1038/s41562-017-0078

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