Professor Erik Brynjolfsson Sponsor: Thomson Reuters
In this research project, we will develop a method for predicting the trajectory and future importance of an academic paper; and an academic dashboard that predicts the trajectories of individual researchers. We plan to utilize data from Thompson Reuters, Google scholar, SSRN, and the Mathematics Genealogy Project. We intend to create an index, called “paper-rank”, that takes into account not only the number of citations for a paper, but also the importance of the papers, who cited the paper, and related metrics, based in part on ideas from the Pagerank algorithm that Google uses to rank websites. We further plan to make predictions about outcomes regarding papers by using information on citations over time with data mining and machine learning methods. For example, we believe we will be able to predict which papers will receive major awards, which papers are destined to become important in an academic field, etc. The key in this task is to look at the overall network of citations and the dynamic evolution of those citations. Our plan is to build a dashboard that predicts the trajectory of individual researchers early in their careers. Such a dashboard could become an important part of some of the major decisions in the life of an academic institution: who to tenure, who can be expected to attain academic stardom, who to hire and correspondingly make available significant resources, and many other decisions. Today such decisions are made using a combination of subjective and objective evidence factors. However, it is our belief that the predictive power of such evidence has not been validated scientifically.