Abstract

To address complex problems, scholars are increasingly faced with challenges of integrating diverse domains. We analyzed the evolution of this convergence paradigm in the ecosystem of brain science, a research frontier that provides a contemporary testbed for evaluating two modes of cross-domain integration: (a) cross-disciplinary collaboration among experts from academic departments associated with disparate disciplines; and (b) cross-topic knowledge recombination across distinct subject areas. We show that research involving both modes features a 16% citation premium relative to a mono-domain baseline. We further show that the cross-disciplinary mode is essential for integrating across large epistemic distances. Yet we find research utilizing cross-topic exploration alone—a convergence shortcut—to be growing in prevalence at roughly 3% per year, significantly outpacing the more essential cross-disciplinary convergence mode. By measuring shifts in the prevalence and impact of different convergence modes in the 5-year intervals up to and after 2013, we find that shortcut patterns may relate to competitive pressures associated with Human Brain funding initiatives launched that year. Without policy adjustments, flagship funding programs may unintentionally incentivize suboptimal integration patterns, thereby undercutting convergence science’s potential in tackling grand challenges.

Highlights

  • The history of scientific development is characterized by a pattern of convergence-divergence cycles (Roco, 2013)

  • Assuming the HBS ecosystem to be representative of other competitive science frontiers, our results suggest that the two operational modes of convergence evolve as substitutes rather than complements

  • In order to provide a timely assessment of convergence science, we addressed RQ1—how to measure convergence?—by developing a generalizable framework that differentiates between diversity in team expertise and research topics

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Summary

Introduction

The history of scientific development is characterized by a pattern of convergence-divergence cycles (Roco, 2013). Originally distinct disciplines synergistically interact to accelerate breakthrough discovery in complex problems (National Research Council, 2014), thereby representing an intrepid form of interdisciplinarity in terms of the number, distance, and novelty of the disciplinary configurations entailed (Nissani, 1995). The cognitive and social dimensions of interdisciplinarity (Wagner, 2011) are augmented in classic convergence. In addition to the cognitive challenges arising from integrating distinct knowledge domains and overcoming their different communication styles and ontologies, there are bureaucratic and socio-political burdens associated with assembling and harnessing the expertise of scholars from different disciplines (Barry et al, 2008). The widely documented tensions underlying interdisciplinarity are compounded in convergence science, owing to the intellectual and organizational challenges associated with the number of and distance between the disciplines being integrated (Bromham et al, 2016, Fealing, 2011, National Research Council, 2005). Contemporary convergence meets large team science (Börner, 2010, Milojevic, 2014, Pavlidis et al, 2014, Wuchty et al, 2007), where collaboration on a grand scale across distinct academic cultures and units faces unique behavioral (Van Rijnsoever and Hessels, 2011), institutional, and cross-sectoral barriers (National Research Council, 2014)

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