Analysis, Visualization & Discovery
The video recording can be found HERE
December 9, 3:30pm — PAA A102

From Big Data to Big Questions: Accelerating Science with the Provenance of Knowledge

Associate Professor, Department of Sociology, University of Chicago


High-throughput experiments, observatories and archives have begun to generate Big Data for the sciences, social sciences and humanities in recent years, but this has made the conserved stock of intelligent questions a bottleneck. Where do scientific and scholarly questions in come from? And can we leverage the answer to generate bigger, high-throughput questions equal to our data; questions that accelerate science by helping us overcome missed opportunities, update distorted intuitions, tune objectives, and steer the high-throughput engines of Big Data? I explore this possibility in the context of modern biomedical, physical, social and humanistic sciences. I demonstrate how rich data can be extracted from the enormous published record to reveal its own history. I then show how we can infer institutions from that history that limit the power of research, which machines can account for in analysis and subsequent experiment. For example, I show how biomedical science shifts incrementally from questions asked in one year to those addressed in the next, and the liability of this pattern for collective discovery and some of the outcomes we most demand from science (e.g., improved health from medicine), but how we can use this insight as an instrument to correct these distortions and drive the next experiment. I show other dynamics in other fields, and explore how we can we shift from the age of Big Data to a better era of Big Questions and Big Answers.


James Evans is founding Director of Knowledge Lab, the Computational Social Science Program, senior fellow at the Computation Institute, associate professor of Sociology and the College, and member of the Committee on Conceptual and Historical Studies of Science at the University of Chicago. His research focuses on the collective system of thinking and knowing, ranging from the distribution of attention and intuition, the origin of ideas and shared habits of reasoning to processes of agreement (and dispute), accumulation of certainty (and doubt), and the texture--novelty, ambiguity, topology--of human understanding. Evans is especially interested in innovation--how new ideas and practices emerge--and the role that social and technical institutions (e.g., the Internet, markets, collaborations) play in collective cognition and discovery. Much of Evans work has focused on areas of modern science and technology, but he is also interested in other domains of knowledge--news, law, religion, gossip, hunches and historical modes of thinking and knowing. Evans supports the creation of novel observatories for human understanding and action through crowd sourcing, information extraction from text and images, and the use of distributed sensors (e.g., RFID tags, cell phones). He uses machine learning, generative modeling, social and semantic network representations to explore knowledge processes, scale up interpretive and field-methods, and create alternatives to current discovery regimes. His research is funded by the National Science Foundation, the National Institutes of Health, the Templeton Foundation, DARPA, the Airforce office Scientific Research and other sources, and has been published in Science, PNAS, American Journal of Sociology, American Sociological Review, Social Studies of Science, Administrative Science Quarterly, Nature Biotechnology, PLoS Computational Biology and other journals. His work has been featured in Nature, the Economist, Atlantic Monthly, Wired, NPR, BBC, El Pais, CNN and many other outlets.