An ability to accurately assess and predict the productivity and impact of working scientists impacts everything from science policy to promotions, hiring, assignment of grants and individual awards. Despite its extraordinary important, we lack a quantitative understanding of the mechanisms that drive individual scientific impact. The goal of this project is to test and discover, in a mathematically rigorous fashion, mechanistic models of citation dynamics of individual scientists. In doing so, the proposed program aims to achieve simultaneously four outcomes, each of which holds direct implications for science policy: (i) Empirical insights towards dynamical and reproducible patterns behind productivity and impact of individual scientists; (ii) Mechanistic models of citation dynamics of individual scientists that incorporate key underlying mechanisms driving individual productivity and their impact; (iii) Computational tools to predict future impact (and uncertainty) of a scientist based on his/her early publication and citation history; (iv) Theoretical insights that decompose predictable components of current metrics and help imagine and create potentially more reliable and predictive metrics. The proposed program not only promises a qualitative shift in our approach to science policy, from funding science to assessing and nurturing scientists to evaluating science and innovation policies, it also helps improve our understanding of fundamental patterns underlying complex systems by pushing forward observations, models, inference, and predictions of individual careers.
|Effective start/end date||2/1/17 → 1/31/20|
- Air Force Office of Scientific Research (FA9550-17-1-0089)
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