Computational Social Sciences Initiative
Please join us for the Division of the Social Sciences 2014 - 2015 Computational Social Sciences Initiative Distinguished Lecture Series. Each Lecture will feature a panel of discussants and will be followed by a reception with drinks and hors d'oeuvres.
When: May 15th, 2015 at 4pm
Where: Main Dining Hall of the Quadrangle Club
Speaker: Alex 'Sandy' Pentland,
Alex `Sandy’ Pentland has helped create and direct MIT’s Media Lab, the Media Lab Asia, and the Center for Future Health. He chairs the World Economic Forum's Data Driven Development council, is Academic Director of the Data-Pop Alliance, and is a member of the Advisory Boards for Google, Nissan, Telefonica, the United Nations Secretary General, Monument Capital, and the Minerva Schools.
In 2012 Forbes named Sandy one of the 'seven most powerful data scientists in the world’, along with Google founders and the CTO of the United States, and in 2013 he won the McKinsey Award from Harvard Business Review. He is among the most-cited computational scientists in the world, and a pioneer in computational social science, organizational engineering, wearable computing (Google Glass), image understanding, and modern biometrics. His research has been featured in Nature, Science, and Harvard Business Review, as well as being the focus of TV features on BBC World, Discover and Science channels. His most recent book is `Social Physics,' published by Penguin Press.
Title: "Shaping Intelligence"
Abstract: Using custom-built sensors, mobile phone software, and on-line financial markets, we have been able to make novel measurements of how the pattern of fine-grained interpersonal interaction shapes human performance from in situations ranging from individuals, to small groups, to divisions of companies, to millions of on-line day traders. Analysis of this novel data suggests that the pattern of social interaction, separate from the content, is a major determinant of individual and group performance.
Fall 2014 Lecture
When: December 5th, 2014 at 4pm
Where: Library of the Quadrangle Club
Speaker: Duncan Watts,
Duncan Watts is a principal researcher at Microsoft Research and a founding member of the MSR-NYC lab. From 2000-2007, he was a professor of Sociology at Columbia University, and then, prior to joining Microsoft, a principal research scientist at Yahoo! Research, where he directed the Human Social Dynamics group . He has also served on the external faculty of the Santa Fe Institute and is currently a visiting fellow at Columbia University and at Nuffield College, Oxford.
Duncan's research on social networks and collective dynamics has appeared in a wide range of journals, from Nature, Science, and Physical Review Letters to the American Journal of Sociology and Harvard Business Review. He is also the author of three books: Six Degrees: The Science of a Connected Age (W.W. Norton, 2003) and Small Worlds: The Dynamics of Networks between Order and Randomness (Princeton University Press, 1999), and most recently Everything is Obvious: Once You Know The Answer (Crown Business, 2011)
He holds a B.Sc. in Physics from the Australian Defence Force Academy, from which he also received his officer’s commission in the Royal Australian Navy, and a Ph.D. in Theoretical and Applied Mechanics from Cornell University. He lives in New York City.
Title: Computational Social Science: Exciting Progress and Grand Challenges
Abstract: The past 15 years have witnessed a remarkable increase in both the scale and scope of social and behavioral data available to researchers, leading some to herald the emergence of a new field: “computational social science.” Against these exciting developments stands a stubborn fact: that in spite of many thousands of published papers, there has been surprisingly little progress on the “big” questions that motivated the field in the first place—questions concerning systemic risk in financial systems, problem solving in complex organizations, and the dynamics of epidemics or social movements, among others. In this talk I highlight some examples of research that would not have been possible just a handful of years ago and that illustrate the promise of CSS. At the same time, they illustrate its limitations. I then conclude with some thoughts on how CSS can bridge the gap between its current state and its potential.
Panel: Ronald S. Burt (Hobart W. Williams Professor of Sociology and Strategy, Booth Chicago), John Levi Martin (Professor, Department of Sociology, Chicago), and Stephen Stigler (Ernest DeWitt Burton Distinguished Service Professor, Department of Statistics, Chicago)
Winter 2015 Lecture
When: March 13th, 2015 at 4pm
Where: Main Dining Hall of the Quadrangle Club
Speaker: Sendhil Mullainathan
Sendhil Mullainathan is a Professor of Economics at Harvard University. His real passion is behavioral economics. His work runs a wide gamut: the impact of poverty on mental bandwidth; whether CEO pay is excessive; using fictitious resumes to measure discrimination; showing that higher cigarette taxes makes smokers happier; modeling how competition affects media bias; and a model of coarse thinking. His latest research focuses on using machine learning and data mining techniques to better understand human behavior.
He enjoys writing, having recently co-authored Scarcity: Why Having too Little Means so Much and writes regularly for the New York Times.
He helped co-found a non-profit to apply behavioral science (ideas42), co-founded a center to promote the use of randomized control trials in development (the Abdul Latif Jameel Poverty Action Lab), serves on the board of the MacArthur Foundation, and has worked in government in various roles, including most recently as Assistant Director of Research at the Consumer Financial Protection Bureau.
He is a recipient of the MacArthur “genius” Award, has been designated a “Young Global Leader” by the World Economic Forum, labeled a “Top 100 Thinker” by Foreign Policy Magazine, and named to the “Smart List: 50 people who will change the world” by Wired Magazine (UK).
Abstract: I describe work using machine learning for policy. This work touches on two essential issues. First, how can a technique that provides few causal guarantees be used to make policy (which typically relies on causality)? Second, and more psychologically, how are we to understand human versus machine predictions? Put differently, what are the contrasts between machine and human intelligence? I work through an extended application to judges setting bail. I conclude by arguing that---consistent with the literature on judgmental biases--that there are many important instances where machine learning techniques can improve policy decisions.
Panel: John Lafferty (Louis Block Professor, Statistics, Computer Sciences, and the College), Reid Hastie (Ralph and Dorothy Keller Distinguished Service Professor of Behavioral Science), and TBA