Classifying modeling and simulation as a scientific discipline

Ross Gore (), Saikou Diallo () and Jose Padilla ()
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Ross Gore: Old Dominion University
Saikou Diallo: Old Dominion University
Jose Padilla: Old Dominion University

Scientometrics, 2016, vol. 109, issue 2, No 1, 615-628

Abstract: Abstract The body of knowledge related to modeling and simulation (M&S) comes from a variety of constituents: (1) practitioners and users, (2) tool developers and (3) theorists and methodologists. Previous work has shown that categorizing M&S as a concentration in an existing, broader disciple is inadequate because it does not provide a uniform basis for research and education across all institutions. This article presents an approach for the classification of M&S as a scientific discipline and a framework for ensuing analysis. The novelty of the approach lies in its application of machine learning classification to documents containing unstructured text (e.g. publications, funding solicitations) from a variety of established and emerging disciplines related to modeling and simulation. We demonstrate that machine learning classification models can be trained to accurately separate M&S from related disciplines using the abstracts of well-index research publication repositories. We evaluate the accuracy of our trained classifiers using cross-fold validation. Then, we demonstrate that our trained classifiers can effectively identify a set of previously unseen M&S funding solicitations and grant proposals. Finally, we use our approach to uncover new funding trends in M&S and support a uniform basis for education and research.

Keywords: Simulation; Research; History of OR; Machine learning (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s11192-016-2050-y

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