Population modeling of the emergence and development of scientific fields

Abstract

We analyze the temporal evolution of emerging fields within several scientific disciplines in terms of numbers of authors and publications. From bibliographic searches we construct databases of authors, papers, and their dates of publication. We show that the temporal development of each field, while different in detail, is well described by population contagion models, suitably adapted from epidemiology to reflect the dynamics of scientific interaction. Dynamical parameters are estimated and discussed to reflect fundamental characteristics of the field, such as time of apprenticeship and recruitment rate. We also show that fields are characterized by simple scaling laws relating numbers of new publications to new authors, with exponents that reflect increasing or decreasing returns in scientific productivity.

General Critical Properties of the Dynamics of Scientific Discovery

“…The fields vary greatly in size and composition: from relatively modest-sized communities in theoretical physics such as cosmic strings or cosmological inflation, in which authors have similar training; to benchtop biomedical topics like research on scrapie and prions, which incorporate co-authors of varied expertise who work together on a narrowly-defined problem; to huge interdisciplinary fields like nanotechnology and sustainability science, which feature authors from a wide range of specialties. Whereas we have examined other aspects of the growth of several of these fields in previous work [7,17], the two largest fields in our sample -sustainability science and nanotechnology -represent brand-new datasets. Table 1.…”

“…First, as our "mean-field" population-modeling has shown, the total number of authors (nodes), N (t), plays the role of the relevant time-like dynamical variable [7]. For example, we found that the total number of articles grows as a simple power-law of the total number of authors, even as both quantities display more complicated growth patterns when measured with respect to time, t. Thus we may adopt N (t), the number of authors in a given field at time t, as our basic time-like variable, akin to the kinds of rescaling commonly adopted in cosmological models such as the universal scale factor, a(t),…”

“…1 On the search strategies used to construct the datasets for the first five fields listed in Table 1, see the appendix in [7]. To construct the database of all authors and articles in nanotechnology, we implemented the search strategy described in [27] within the Web of Science database.…”

“…There are certainly a variety of emerging methodologies that unify the study of science. These include population dynamical models [1,2,3,4,5,6,7], networks of cocitation [8,9] and collaboration [10,11,12,13,14,15,16,17], disciplinary maps of science [18,19,20,21] and phylogenetic term analyses [22,23], among others. But a more profound question is whether studies using these methodologies and others capture common dynamics across fields.…”

Mixed-indicators model for identifying emerging research areas

“…Luís M. A. Bettencourt et al (2008) analyze the temporal evolution of emerging areas within several scientific disciplines according to numbers of authors and publications using contagion models developed in epidemiology.…”