James Evans’ 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 Journal of Sociology, American Sociological Review, Social Studies of Science, and many other journals.