Talk: Two Perspectives on Fair Political Redistricting

Live stream: https://uwmadison.zoom.us/j/98632650029?pwd=MYYMPFK4K4V9aCkOUwH69OweSBD5qK.1 

Abstract: In the United States, the process by which electoral districts are regularly redrawn can be highly contentious, with enormous ramifications for the balance of legislative power. It is often easy to point to a redistricting map that is clearly unfair to a given political party or group, but it is difficult to rigorously define what fairness should actually mean. In this talk, I will discuss various lenses through which computer scientists, economists, and mathematicians have sought to address this problem, some of which have already seen direct impacts through expert testimony in high-profile litigation. I will focus on some of my recent and ongoing work in two directions, each of which brings a distinct perspective on what it means for a map to be fair:
(1) Sampling "random" redistricting maps from "unbiased" distributions, using random spanning trees
(2) Optimizing over redistricting maps subject to a robust, binary fairness constraint

Bio: Jamie Tucker-Foltz is a final-year computer science PhD student at Harvard University, advised by Ariel Procaccia. His work is supported by a Google PhD Fellowship and an NSF Graduate Research Fellowship. He earned his undergraduate degree in computer science and mathematics from Amherst College, and a master's degree in computer science from the University of Cambridge on a Churchill Scholarship. His research focuses on applying techniques from theoretical computer science to improve fairness in political and economic institutions. He works on a range of topics in fair division, social choice theory, and algorithmic game theory. He is especially interested in algorithms for fair redistricting and gerrymandering detection.