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 $2\,094$ 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.

翻译：科学家如何在利用先验知识实现专业化与适应不断演变的研究机遇之间寻求平衡？本文借鉴文化演化等多种适应性视角，提出一种基于最优传输的无监督贝叶斯方法，用以模拟科学家研究组合随领域变革的演化过程。该模型基于186,162篇科学摘要与作者数据，通过分析2000年至2019年间2,094位高能物理学者的学术轨迹，评估了智力资源、社会资源与制度资源对科学家发展路径的影响。借助逆最优传输方法，研究揭示了科研投入的重新配置受到学习成本的制约，从而提升了科学家群体间科学资本的效用效率。社会资本的"多样性"与"权力"两个维度对学者研究方向转变幅度呈现相反作用："多样性"与更显著的研究方向转变及研究组合扩展相关，而"权力"则与更稳定的研究议程相关联。在认知距离较远的研究领域间转换时，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、测度与建模集体适应性现象提供了新的方法论路径。