Filter the library
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.
He earned S.B. degrees in both Economics and Mathematics from MIT, received a Ph.D. in Economics from Harvard University, where he was a National Science Foundation Fellow, and has been a visiting professor at Princeton University and Stanford University. Ted’s main research focus is African economic development, including work on the economic causes and consequences of violence; the impact of ethnic divisions on local collective action; interactions between health, education, environment, and productivity for the poor; and methods for transparency in social science research. He has conducted field work in Kenya, Sierra Leone, Tanzania, and India. He has published over 80 articles and chapters in leading academic journals and collected volumes, and his work has been cited over 20,000 times according to Google Scholar.
Andrew Gelman is a professor of statistics and political science at Columbia University. He has published research articles on statistical theory, methods, and computation, with applications in social science and public health. He and his colleagues have written several books, including Bayesian Data Analysis, Teaching Statistics: A Bag of Tricks, Regression and Other Stories, A Quantitative Tour of the Social Sciences, and Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do. His ideas on metascience include type M and type S errors, the folk theorem of statistical computing, the freshman fallacy, the time-reversal heuristic, the Armstrong principle, the Javert paradox, Eureka bias, Clarke’s law, the piranha problem, and the garden of forking paths.
Steven Goodman, MD, MHS, PhD is Associate Dean of Clinical and Translational Research and Professor of Medicine and Epidemiology in the Stanford School of Medicine. He is chief of the Division of Epidemiology and directs a newly established office in the School of Medicine to improve “researcher readiness” and the reproducibility of laboratory and clinical research. He is co-founder and co-director of the Meta-research Innovation Center at Stanford (METRICS), a group dedicated to examining and improving the reproducibility, integrity and efficiency of biomedical research. His research is in the methods and philosophical foundations of statistical inference, particularly the proper measurement, conceptualization and synthesis of research evidence, with an emphasis on Bayesian approaches. He also has worked on the connections between ethics and scientific methods, particularly in interventional research. Finally, he has a strong interest in developing curricula and new models for teaching the foundations of good scientific practice. Among his current national positions and recognitions included chairing the Methodology Committee of PCORI (Patient Centered Outcomes Research Institute), being awarded the 2016 Spinoza Chair in Medicine from the University of Amsterdam for his work in scientific and statistical inference, serving as scientific advisor to the national Blue Cross-Blue Shield technology assessment program and being senior statistical editor at the Annals of Internal Medicine, since 1987. Before coming to Stanford in 2011, he was at the Johns Hopkins Schools of Medicine and Public Health, where he directed their cancer center’s Division of Biostatistics and Bioinformatics and the Dept. of Epidemiology’s doctoral program.
Adam Russell joined DARPA as a program manager in July 2015. He is interested in new experimental platforms and tools to facilitate discovery, quantification, and “big validation” of fundamental measures in social science, behavioral science, and human performance. Russell has broad technical and management experience across a number of disciplines, ranging from cognitive neuroscience and physiology to cultural psychology and social anthropology. Before joining DARPA, he was a program manager at the Intelligence Advanced Research Projects Activity, where he developed and managed a number of high-risk, high-payoff research projects for the Office of the Director of National Intelligence. Prior to IARPA, Russell was in the industry, where he was a senior scientist and principal investigator on a wide range of human performance and social science research projects and strategic assessments for a number of different government organizations. Russell holds a Bachelor of Arts in Cultural Anthropology from Duke University and an M.Phil. and a D.Phil. in Social Anthropology from Oxford University, where he was a Rhodes Scholar.
Daniele Fanelli is a fellow in Quantitative Methodology at the London School of Economics, UK, where he teaches research methods and investigates the nature of science and possible issues with scientific evidence. He graduated in Natural Sciences, giving exams in all fundamental disciplines, then obtained a PhD studying the behaviour and genetics of social wasps, and subsequently worked for two years as a science writer. All of his postdoctoral work has been devoted to studying the nature of science itself, and the mis-behaviours of scientists. His empirical research has been instrumental in quantifying the prevalence and causes of problems that may affect research across the natural and social sciences, and it has helped develop remedies and preventive measures. In addition to his scientific work, Daniele co-chairs the Research Integrity Sub-Committee within the Research Ethics and Bioethics Advisory Committee of Italy’s National Research Council, for which he developed the first research integrity guidelines. He is also a member of the Research Integrity Committee of the Luxembourg Agency for Research Integrity (LARI), was formerly a member of Canada’s Tri-Council Expert Panel on Research integrity, and is currently rapporteur for a European Mutual Learning Exercise on Research Integrity. Before joining the London School of Economics, Daniele worked at the University of Edinburgh, UK, at the University of Montreal, CA, and at Stanford University, USA, in the Meta-Research Innovation Center @ Stanford (METRICS).
My empirical work focuses on computational approaches to the sociology of science. I blend network analysis, complex systems thinking, and data-driven probabilistic modeling with the qualitative insights of the science studies literature to probe the strategies, dispositions, and social processes that shape the production and persistence of scientific ideas. I also develop formal models of scientific behavior and the evolutionary dynamics of ideas and institutions. Fundamentally, I aim to understand the social world as constituted by, and constitutive of, ideas, beliefs, and practices. Science provides an excellent “model organism” for this endeavor. My approach is strongly informed by research on complex systems and biological and cultural evolution.
Melissa A. Schilling is the Herzog Family Professor of Management at New York University Stern School of Business. She received her Bachelor of Science in business administration from the University of Colorado at Boulder. She received her Doctor of Philosophy in strategic management from the University of Washington. Professor Schilling’s research focuses on innovation and strategy in high technology industries such as smartphones, video games, pharmaceuticals, biotechnology, electric vehicles, and renewable energies. She is particularly interested in platform dynamics, networks, creativity, and breakthrough innovation. Her textbook, Strategic Management of Technological Innovation (now in its fifth edition), is the number one innovation strategy text in the world. She is also coauthor of Strategic Management: An integrated approach (now in its twelfth edition).
Roberta Sinatra is Assistant Professor at IT University of Copenhagen, and holds visiting positions at ISI (Turin, Italy) and Complexity Science Hub (Vienna, Austria). Her research is at the forefront of network science, data science and computational social science. Currently, she spends particular attention on the analysis and modeling of dynamics that lead to the collective phenomenon of success, with focus on science and art. Roberta completed her undergraduate and graduate studies in Physics at the University of Catania, Italy, and was first a postdoctoral fellow, then a research faculty at the Center for Complex Network Research of Northeastern University (Boston MA, USA). Her research has been published in general audience journals such as Nature and Science, and has been featured in The New York Times, Forbes, The Economist, The Guardian, The Washington Post, among other major media outlets.