James Evans (University of Chicago)
Abstract. Science is a complex system: apparently complicated, shaped by strong interactions between diverse components, and displaying emergent, often unexpected collective outcomes. In this talk, I explore how the complex network of science provides a substrate on which a scientist—and indeed science as a whole—thinks. Using contents from millions of scientific articles and patents, I show how science moves between problems posed and questions answered in one year and those examined in the next. This process reveals an “essential tension” between the professional demand for productivity and a conflicting drive toward risky innovation. It also demonstrates how scientist swarm around funds and well-published claims; and migration between topics via methods. Performing massive supercomputer experiments, we compare the efficiency of typical research strategies with thousands of alternatives in the context of biomedical chemistry. Existing strategies of chemical discovery are efficient only for initial exploration of the network of chemical relationships. Much more efficient strategies for mature fields involve more individual risk-taking than the structure of modern scientific careers supports and I show how publication of experimental failures and investment in alternative paths of discovery could substantially improve the speed of discovery. I explore the implications of these findings for institutional redesign and machine science—the expanded use of computation from analysis to hypothesis generation and scientific imagination.
Bio: James Evans is Senior Fellow at the Computation Institute and Director of Knowledge Lab (knowledgelab.org), Associate Professor of Sociology at the University of Chicago, member of the Committee on the Conceptual and Historical Studies of Science. His work explores the sources, structure, dynamics and consequences of modern knowledge. Evans is particularly interested in the relation of markets to science and knowledge more broadly, and how evolutionary and generative models can inform our understanding of collective representations, experiences and certainty. He has studied how industry collaboration shapes the ethos, secrecy and organization of academic science; the web of individuals and institutions that produce innovations; and markets for ideas and their creators. Evans has also examined the impact of the Internet on knowledge in society. His work uses natural language processing, the analysis of social and semantic networks, statistical modeling, and field-based observation and interviews. Evans’ research is funded by the National Science Foundation, the National Institutes of Health, DARPA, DOE, the Mellon and Templeton Foundations and has been published in Science, PLOS, American Journal of Sociology, Social Studies of Science, Administrative Science Quarterly and other journals. His work has been featured in Nature, the Economist, Atlantic Monthly, Wired, NPR, BBC, El Pais, CNN and many other outlets.