Speaker: Nisarg Shah, Carnegie Mellon University
Title: Optimal Social Decision Making
How can computers help ordinary people make collective decisions about real-life dilemmas, like which restaurant to go to with friends, or even how to divide an inheritance? I will present an optimization-driven approach that draws on ideas from AI, theoretical computer science, and economic theory, and illustrate it through my research in computational social choice and computational fair division. In both areas, I will make a special effort to demonstrate how fundamental theoretical questions underlie the design and implementation of deployed services that are already used by tens of thousands of people (spliddit.org <http://spliddit.org>), as well as upcoming services (robovote.org <http://robovote.org>).
Nisarg Shah is a Ph.D. candidate in the Computer Science Department at Carnegie Mellon University, advised by Ariel Procaccia. His broad research agenda in algorithmic economics includes topics such as computational social choice, fair division, game theory (both cooperative and noncooperative), and prediction markets. He focuses on designing theoretically grounded methods that have practical implications. Shah is the winner of the 2013-2014 Hima and Jive Graduate Fellowship and the 2014-2015 Facebook Fellowship.