CS Events


Mechanism Design and Data Science


Download as iCal file

Friday, October 23, 2020, 10:00am - 11:30pm


Rutgers University Computer Science Department

Speaker: Jason Hartline, Northwestern University


Prof. Hartline’s research introduces design and analysis methodologies from computer science to understand and improve outcomes of economic systems. Optimal behavior and outcomes in complex environments are complex and, therefore, should not be expected; instead, the theory of approximation can show that simple and natural behaviors are approximately optimal in complex environments. This approach is applied to auction theory and mechanism design in his graduate textbook Mechanism Design and Approximation which is under preparation.

Prof. Hartline received his Ph.D. in 2003 from the University of Washington under the supervision of Anna Karlin. He was a postdoctoral fellow at Carnegie Mellon University under the supervision of Avrim Blum; and subsequently a researcher at Microsoft Research in Silicon Valley. He joined Northwestern University in 2008 where he is currently a professor of computer science. He was on sabbatical at Harvard University in the Economics Department during the 2014 calendar year and visiting Microsoft Research, New England for the Spring of 2015. Prof. Hartline codirects the Institute for Data, Econometrics, Algorithms, and Learning (an NSF HDR TRIPODS institute) and is a cofounder of virtual conference organizing platform Virtual Chair.

 Presented in association with the DATA-INSPIRE TRIPODS Institute

Location : Join this event via Webex- Details on how to join are listed below

Event Type: Seminar

Abstract: Computer systems have become the primary mediator of social and economic interactions. A defining aspect of such systems is that the participants have preferences over system outcomes and will manipulate their behavior to obtain outcomes they prefer. Such manipulation interferes with data-driven methods for designing and testing system improvements. A standard approach to resolve this interference is to infer preferences from behavioral data and employ the inferred preferences to evaluate novel system designs.In this talk, Prof. Hartline will describe a method for estimating and comparing the performance of novel systems directly from behavioral data from the original system. This approach skips the step of estimating preferences and is more accurate. Estimation accuracy can be further improved by augmenting the original system; its accuracy then compares favorably with ideal controlled experiments, a.k.a., A/B testing, which are often infeasible. A motivating example will be the paradigmatic problem of designing an auction for the sale of advertisements on an Internet search engine.


TRIPODS (Transdisciplinary Research in Principles of Data Science) Seminar Series Sponsored by the TRIPODS DATA-INSPIRE Institute, a joint collaboration of DIMACS and the Rutgers Departments of Computer Science, Mathematics, and Statistics

Contact  Faculty Host: David Pennock

Webex link: https://rutgers.webex.com/webappng/sites/rutgers/meeting/download/282acded92694dad99e4ce4cd167a9d6?siteurl=rutgers&MTID=m91129a1d0a9346497e0972f31e23f5ca

Meeting number (access code): 120 226 4218

Meeting password: 1234