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Essie Reynolds
Summary Sentence: The 2014-2015 SCS Distinguished Lecture Series will be hosted by the College of Computing and will continue to bring lauded educators, researchers, lecturers and industry leaders to the Georgia Tech campus, including Professor Michael Kearns.
Full Summary: No summary paragraph submitted.
Michael KearnsA reception will precede the lecture and will be located in the Scheller CoB Atrium at 10:30 A.M.
Lecture: 11 A.M. in Scheller College of Business, Room 100
Biography: Michael Kearns is currently a professor in the Computer and Information Science Department at the University of Pennsylvania. He serves as the National Center Chair. Kearns also serves secondary appointments in the Statistics and Operations and Information Management (OPIM) departments of the Wharton School. Prior to his professorship at the University of Pennsylvania, Kearns worked in AI and machine learning for Bell Labs and AT&T Labs. His postdoctoral work was completed at the Laboratory for Computer Science at M.I.T. and the International Computer Science Institute in Berkeley.
Kearns' research specializations include machine learning, artificial intelligence, algorithmic game theory, social networks, and computational finance. His recent research has been in conducting human-subject experiments on strategic and economic interaction in social networks.
He is the founder of the University of Pennsylvania's undergraduate Networked and Social Systems Engineering (NETS) program. Kearns is a Fellow of both the Association for the Advancement of Artificial Intelligence and the American Academy of Arts and Sciences.
Abstract: Beginning with the introduction of graphical games and related models, there is now a rich body of algorithmic connections between probabilistic inference, game theory and microeconomics. Strategic analogues of belief propogation and other inference techniques have been developed for the computation of Nash, correlated and market equilibria, and have played a significant role in the evolution of algorithmic game theory over the past decade.
There are also important points of departure between probabilistic and strategic graphical models -- perhaps most notably that in the latter, vertices are not random variables, but self-interested humans or organizations. It is thus natural to wonder how social network structures might influence equilibrium outcomes such as social welfare or the relative wealth and power of individuals. One logical path that such questions leads to is human-subject experiments on strategic interaction in social networks.
