The Center for Science of Science & Innovation (CSSI) is a multidisciplinary community for thought leaders in such fields as computational social science, network science, artificial intelligence and complex systems. More than ever, modern science is playing a critical role in driving the growth and innovation of businesses and economies around the world. Our team uses this proliferation of data to understand successes in innovation and teamwork, while also predicting scientific impact and knowledge production.
CSSI collaborators take scientific methods and turn them upon science itself to establish a systematic, quantitative framework that can make sense of mass quantities of data and identify emerging patterns. Armed with big data spanning all phases of scientific production, a defining feature of our work is a mechanistic approach to developing models that can uncover fundamental patterns in science. By combining our diverse expertise and approaches, the results of this center will lead to a qualitative shift in the way knowledge is discovered, science is funded, scientists are trained and nurtured, and excellence is recognized and rewarded.
Discover our faculty's latest explorations in the field of science of science. Published in Kellogg Insight, the Kellogg School of Management's magazine on faculty research and expertise, this collection of work delves into such areas as teamwork, collaboration, innovation, careers and gender dynamics.
One of the most universal trends in science and technology today is the growth of large teams in all areas, as solitary researchers and small teams diminish in prevalence.
Members of the CSSI team discuss how today’s practices, policies and resources are still rooted in traditions and intuitions rather than evidence, and why we must work to do better.
Here we find that, for subjects ranging from mobile handsets to automobiles and from smartphone apps to scientific fields, early growth patterns follow a power law with non-integer exponents.
The CSSI team developed a mechanistic model to explore the long-term predictability of citation patterns, the results of which indicate that all papers tend to follow the same universal temporal pattern.
As artificial intelligence (AI) applications see wider deployment, it becomes increasingly important to study the social and societal implications of AI adoption.
An analysis of Web of Science data spanning more than 100 years reveals the rapid growth and increasing multidisciplinarity of physics — as well its internal map of subdisciplines.