How scientists navigate between the need to capitalize on their prior knowledge by specializing, and the urge to adapt to evolving research opportunities? Drawing on from diverse perspectives on adaptation, in particular from institutional change and cultural evolution, this paper proposes an unsupervised Bayesian model of the evolution of scientists' research portfolios in response to transformations in their field. The model relies on 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 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 effects on the magnitude of change in scientists' research interests: while ``diversity'' disrupts and expands research interests, ``power'' stabilizes physicists' research agendas -- as does institutional stability. Social capital plays a more crucial role in shifts between cognitively distant research areas. Overall, this contribution provides new approaches for understanding and modeling collective adaptation.

翻译：科学家如何在通过专精利用既有知识与适应不断变化的研究机遇之间取得平衡？借鉴适应性的多元视角，特别是制度变迁与文化演化领域的观点，本文提出了一种无监督贝叶斯模型，用以描述科学家研究组合随领域变革的演化过程。该模型利用科学摘要与作者数据，评估智力资源、社会资源与制度资源对2000年至2019年间2195名高能物理学家研究轨迹的影响。通过最优传输方法，研究表明研究精力的重新分配受学习成本塑造，从而提升了科学家之间科学资本效用的扩散。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产生相反效应：“多样性”扰乱并扩展研究兴趣，而“权力”则稳定物理学家的研究议程——制度稳定性亦具有类似作用。在社会资本对认知距离较远的研究领域之间的转移过程中，其作用更为关键。总体而言，本研究为理解与建模集体适应性提供了新路径。