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Past Events › IDSS Distinguished Seminar Series

A monthly lecture series featuring prominent global leaders and academics sharing research in areas that are impacted by the emergence of big data.

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May 2018

IDSS Distinguished Seminar – Conflict in Networks: The Rise and Fall of Empires

May 1 @ 4:00 pm - 5:00 pm

Sanjeev Goyal (University of Cambridge)

MIT Building 32, Room 141

Abstract In the study of war, a recurring observation is that conflict between two opponents is shaped by third parties. The actions of these parties are in turn influenced by other proximate players. These considerations lead us to propose a model of conflict in a network. We study the influence of resources, technology, and the network of connections on the dynamics of war and the prospects of peace. Bio Sanjeev Goyal is Professor of Economics at the University of Cambridge…

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April 2018

Computational Social Science: Exciting Progress and Future Challenges

April 3 @ 4:00 pm - 5:00 pm

Duncan Watts (Microsoft Research, NYC)

MIT Building 32, Room 141

 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.” In this talk I highlight two areas of research that would not have been possible just a handful of years ago: first, using “big data” to study social contagion on networks; and second, using virtual labs to extend the scale, duration, and…

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February 2018

Machine Learning and Causal Inference

February 6 @ 4:00 pm - 5:00 pm

Susan Athey (Stanford University)

MIT Building 32, Room 141

Abstract: This talk will review a series of recent papers that develop new methods based on machine learning methods to approach problems of causal inference, including estimation of conditional average treatment effects and personalized treatment assignment policies. Approaches for randomized experiments, environments with unconfoundedness, instrumental variables, and panel data will be considered. Bio: Susan Athey is The Economics of Technology Professor at Stanford Graduate School of Business. She received her bachelor's degree from Duke University and her Ph.D. from Stanford,…

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December 2017

IDSS Distinguished Seminar – Essential Concepts of Causal Inference: A Remarkable History

December 12, 2017 @ 4:30 pm - 5:30 pm

Donald Rubin (Harvard University)

MIT Building 32, Room 141

  Abstract I believe that a deep understanding of cause and effect, and how to estimate causal effects from data, complete with the associated mathematical notation and expressions, only evolved in the twentieth century. The crucial idea of randomized experiments was apparently first proposed in 1925 in the context of agricultural field trails but quickly moved to be applied also in studies of animal breeding and then in industrial manufacturing. The conceptual understanding, to me at least, was tied to…

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November 2017

Social Network Experiments – Nicholas Christakis (Yale University)

November 7, 2017 @ 4:30 pm - 5:30 pm

MIT Building 32, Room 141

The Institute of Data, Systems, and Society host monthly talks by academic and industry leaders from around the world for the IDSS Distinguished Lecture series.

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October 2017

IDSS Distinguished Seminar Series: Latanya Sweeney (Harvard University)

October 3, 2017 @ 4:30 pm - 5:30 pm

MIT Building 32, Room 141

How Technology Design will Dictate Our Civic Future

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September 2017

Fast and Slow Learning from Reviews

September 12, 2017 @ 4:30 pm - 5:30 pm

MIT Building 32, Room 141

Speaker: Daron Acemoglu (MIT) Many online platforms present summaries of reviews by previous users. Even though such reviews could be useful, previous users leaving reviews are typically a selected sample of those who have purchased the good in question, and may consequently have a biased assessment. In this paper, we construct a simple model of dynamic Bayesian learning and profit-maximizing behavior of online platforms to investigate whether such review systems can successfully aggregate past information and the incentives of the…

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