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, including cultural evolution, 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 $2\,195$ 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" disrupts and expands research interests, "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.

翻译：科学家如何在利用先前知识进行专业化与适应新兴研究机会之间做出权衡？本文借鉴文化进化等关于适应性的多元视角，提出了一种基于最优传输理论的无监督贝叶斯方法，用于刻画科学家研究组合随领域变革的演化过程。该模型基于186,162篇科学摘要和作者数据，评估了2000年至2019年间2,195名高能物理学家的智力、社会及制度资源对其职业轨迹的影响。利用逆最优传输方法，研究表明研究努力的重新分配受学习成本影响，从而提升了科学家间科学资本的效用。社会资本的两个维度——"多样性"与"权力"——对科学家研究兴趣的变化幅度呈现相反关联："多样性"会打破并扩展研究兴趣，而"权力"则与更稳定的研究议程相关。在认知距离较远的研究领域之间切换时，社会资本发挥着更为关键的作用。总体而言，本研究为利用最优传输理论理解、测量和建模集体适应性提供了新思路。