Theory-to-Application Transition in Mathematics and Computer Science Studies

Basic and applied research have been a pair of popular yet vaguely-defined concepts in scientific philosophy and technology management. This classification gets further challenged as once-theoretical studies like Artificial Intelligence became applicational in less than a decade. As such, a quantitative approach to evaluate the ongoing changes in and around computer science domain has both theoretical and practical implications. This study applies Topic-Aware Level Score, a science-of-science measure of application to mathematics and computer science domains and draws the following findings: On the theory side, methodological theories like graph algorithms and mathematical modeling are gaining application colors, but core and fundamental theories like equations and linear algebra remain steady as pure theories. On the application side, the most applied studies identified in this research have all been moving towards application in the past 3 decades, but some at a much faster pace than others. The study also identified neural networks as a representative case where the given study was completely theoretical in the 1990s but are now among the studies with highest application levels, making them potential showcases for technology incubation. This analysis of basic and applied research helps policymakers and research administrators to determine research directions and formulate funding strategies effectively.