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Large Teams Develop and Small Teams Disrupt Science and Technology

Date & Time:
17:15 - 18:15,
Monday 25 February, 2019


One of the most universal shifts in science and technology today is the growth of large teams in all areas as solitary researchers and small teams diminish. Increases in team size are attributed to the specialization of scientific activities, communication technology, and the unproven claim that modern problems are complex and require interdisciplinary teams to solve. This fundamental shift raises an important question: How do large and small teams differ in the character of the science and technology they produce? Here, analyzing teamwork from more than 65 million papers, patents, and software products, 1954-2014, we demonstrate across this period that smaller teams tend to disrupt science and technology with new ideas and opportunities, while larger teams tend to develop existing ones. Work from larger teams builds on more recent, popular developments, and attention to that work comes immediately, while contributions by smaller teams search more deeply into the past, are viewed as disruptive to science and technology and succeed further into the future, if at all. Observed differences between small and large teams magnify with impact—small teams have become known for disruptive work and large teams for developing work.  Differences in topic and research design account for a small part of the relationship between team size and disruption, but most of the effect occurs within people, as they move between smaller and larger teams. These results demonstrate that both small and large teams are essential to a flourishing ecology of science and technology, which suggests that science policy support both small and large teams for the sustainable vitality of science and technology.

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  • Name: James Evans
  • Affiliation: University of Chicago
  • Role:
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  • Bio: James Evans is Professor of Sociology, Director of Knowledge Lab, and Founding Faculty Director of Computational Social Science at the University of Chicago. His research uses large-scale data, machine learning and generative models to understand how collectives think and what they know. This involves inquiry into the emergence of ideas, shared patterns of reasoning, and processes of attention, communication, agreement, and certainty. Thinking and knowing collectives like science, Wikipedia or the Web involve complex networks of diverse human and machine intelligences, collaborating and competing to achieve overlapping aims. Evans’ work connects the interaction of these agents with the knowledge they produce and its value for themselves and the system.


    Evans designs observatories for understanding that fuse data from text, images and other sensors with results from interactive crowd sourcing and online experiments. Much of Evans’ work has investigated modern science and technology to identify collective biases, generate new leads taking these into account, and imagine alternative discovery regimes. He has identified R&D institutions that generate more and less novelty, precision, density and robustness. Evans also explores thinking and knowing in other domains ranging from political ideology to popular culture. His work has been published in Nature, Science, PNAS, American Sociological Review, American Journal of Sociology and many other outlets.