Gautheron, Lucas (2025) Balancing Specialization and Adaptation in a Transforming Scientific Landscape. EPJ Data Science.
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Abstract
How do scientists navigate between the need to capitalize on their prior knowledge through specialization, and the urge to adapt to evolving research opportunities? Drawing from diverse perspectives on adaptation, this paper proposes an unsupervised Bayesian approach motivated by Optimal Transport of the evolution of scientists’ research portfolios in response to transformations in their field. The model relies on 186,162 scientific abstracts and authorship data to evaluate the influence of intellectual, social, and institutional resources on scientists’ trajectories within a cohort of 2108 high-energy physicists between 2000 and 2019. Using Inverse Optimal Transport, the reallocation of research efforts is shown to be shaped by learning costs, thus enhancing the utility of the scientific capital disseminated among scientists. Two dimensions of social capital, namely “diversity” and “power”, have opposite associations with the magnitude of change in scientists’ research interests: while “diversity” is associated with greater change and expansion of research portfolios, “power” is associated with more stable research agendas. Social capital plays a more crucial role in shifts between cognitively distant research areas. More generally, this work suggests new approaches for understanding, measuring and modeling collective adaptation using Optimal Transport.
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