Discovering the Extent of Estimable Prediction (DEEP) in Science and Technology
Abstract
We proposed to establish mathematical and empirical foundations regarding the nature and extent of quantifiable prediction in science and technology (S and T), the central question of science policy, a fundamental challenge for complex systems research, with the potential to dramatically improve the productivity and focus of science. Research based on this program yielded a wave of relevant discoveries published in Nature, Science, PNAS, Nature subfield journals, every major sociology outlet, and top venues in research policy, social, computer and information science. Moreover, we have drafted two forthcoming books from Cambridge University Press and Princeton University Press, and many more articles that will be published in the coming year. These works review the state of the art in science and technology prediction, but also probe and exceed those limits by predicting science and technology success and failure, career and team productivity and influence, the disruptiveness and popularity of novel idea and technology combinations, team and community conflict, and a host of indicators that predict future focus and impact.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 12, 2019
- Accession Number
- AD1103103
Entities
People
- Dashun Wang
- James A. Evans
Organizations
- University of Chicago