How Science Thinks: Big Data, Machine Learning and the Acceleration of Reason

Date and Time: 15th September 2016, 3 pm

Venue: LH111

Title: How Science Thinks: Big Data, Machine Learning and the Acceleration of Reason

Abstract:

I explore how computationally modeling and predicting the scientific process can create opportunities for improving it. I begin by demonstrating how the complex network of modern biomedical science provides a substrate on which a scientist--and indeed science as a whole--thinks, and its consequences for ongoing scientific discovery and human health. Using millions of scientific articles from MEDLINE, I show how science moves conservatively from problems posed and questions answered in one year to those examined in the next. Along the way, I show how contemporary science "changes its mind"; how it has become more risk-averse and less efficient at discovery; how the atmosphere of its own internal puzzles have largely decoupled it from health needs. We use this as an opportunity to demonstrate how much more efficient strategies can be found for mature fields, which involve greater individual risk-taking than the structure of modern scientific careers supports, and propose institutional alternatives that maximize a range of valuable objectives, from scientific discovery to robust understanding to technological advance. I will discuss exciting opportunities to apply machine learning in not only the science of science, the social sciences, and an emerging science of data.

Bio:

James Evans is Professor of Sociology at the University of Chicago, Senior Fellow at the Computation Institute, Director of Knowledge Lab (knowledgelab.org) and Director of the Computational Social Science program (macss.uchicago.edu). 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, the Mellon and Templeton Foundations and has been published in Science, PNAS, American Journal of Sociology, American Sociological Review, 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.