Abstract: Convergence science is an intrepid form of interdisciplinarity defined by the US National Research Council as “the coming together of insights and approaches from originally distinct fields” to strategically address grand challenges. This paradigm has been promoted extensively in the last decade, becoming a model for designing flagship research programs that strategically address grand challenges. Despite its increasing relevance to science policy and institutional design, there is still no practical framework for measuring convergence. We address this gap by developing a measure of disciplinary distance based upon disciplinary boundaries delineated by hierarchical ontologies. We apply this approach using two widely used ontologies – the Classification of Instructional Programs (CIP) and the Medical Subject Headings (MeSH) – each comprised of thousands of entities that facilitate classifying two distinct research dimensions, respectively. The social dimension codifies the disciplinary pedigree of individual scholars, connoting core expertise associated with traditional modes of mono-disciplinary graduate education. The conceptual dimension codifies the knowledge, methods, and equipment fundamental to a given target problem, which together may exceed the researchers’ core expertise. Considered in tandem, this decomposition facilitates measuring social-conceptual alignment and optimizing team assembly around domain-spanning problems – a key aspect that eludes other approaches. We demonstrate the utility of this framework in a case study of the human brain science (HBS) ecosystem, a relevant convergence nexus that highlights several practical considerations for designing, evaluating, institutionalizing and accelerating convergence. Econometric analysis of 655,386 publications derived from 9,121 distinct HBS scholars reveals a 11.4% article-level citation premium attributable to research featuring full topical convergence, and an additional 2.7% citation premium if the social (disciplinary) configuration of scholars is maximally aligned with the topical configuration of the research.

Alex Petersen is Associate Professor in the Management of Complex Systems department and a founding member of the Ernest and Julio Gallo Management of Innovation Sustainability and Technology (MIST) program at the University of California – Merced, where he leads data-oriented research focusing on the evolution of large multiscale socio-economic systems by applying concepts and methods from complex systems, statistical physics, management and innovation science. Teaming up with collaborators from a variety of disciplines, his innovation research has been published in various multi-disciplinary journals such as Science, PNAS and Research Policy.

To be added soon after the seminar.