Abstract

Direct democracy, where each voter case one vote, fails when the average voter competence (classifier accuracy) falls below 50%. This can happen in noisy settings where voters have only limited information, or when there are multiple topics and the average voter competence may not be high enough for some topics. Representative democracy, where voters choose representatives to vote, can be an elixir in both these situations. We introduce a mathematical model for studying representative democracy, in particular understanding the parameters of a representative democracy that gives maximum decision making capability. Under our models we characterize the optimal representative democracy under general and natural conditions.

Joint work with Malik Magdon-Ismail

Time

10月22日（周一）14:00-15:00

Speaker

Lirong Xia is an assistant professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI). Prior to joining RPI in 2013, he was a CRCS fellow and NSF CI Fellow at the Center for Research on Computation and Society at Harvard University. He received his Ph.D. in Computer Science and M.A. in Economics from Duke University. His research focuses on the intersection of computer science and microeconomics. He is an associate editor of Mathematical Social Sciences and is on the editorial board of Journal of Artificial Intelligence Research and Artificial Intelligence Journal. He is the recipient of an NSF CAREER award, a Simons-Berkeley Research Fellowship, and was named as one of "AI's 10 to watch" by IEEE Intelligent Systems.

Venue

信息管理与工程学院  602会议室

上海财经大学

上海市杨浦区武东路100号