The US political system is built on representatives chosen by geographically localized regions. This presents the government with the problem of designing these districts. Every ten years, the US census counts the population and new political districts must be drawn. The practice of harnessing this administrative process for partisan political gain is often referred to as gerrymandering.
How does one identify and understand gerrymandering? Can we really recognize gerrymandering when we see it? If one party wins over 50% of the vote, is it fair that it wins less than 50% of the seats? What do we mean by fair? How can math help illuminate these questions?
How does the geopolitical geometry of the state (where which groups live and the shape of the state) inform these answers?
For me, these questions began with an undergraduate research program project in 2013 and have led me to testify twice in two cases: Common Cause v. Rucho (that went to the US Supreme Court) and Common Cause v. Lewis. This work has partially resulted in the redrawing of the NC State Legislative district maps and NC congressional maps. The resulting new maps will be used in our upcoming 2020 elections.
The legal discussion has been informed by the mathematical framework, but the problem of understanding gerrymandering has also prompted the development of a number of new computational algorithms that come with new mathematical questions.
As the reapportionment which will company the 2020 census a number of new questions arise? Can one design mechanisms to produce “Fairer Maps”? Can we agree on what that means? Are courts the main recourse to stop Gerrymandering? Are there plausible legislative How will the new census methodologies interact with the ability to Gerrymander or create representative districts?
This talk will be accessible to all; some experience with looking at statistics, summarizing data, or thinking about probabilities will be useful at a few points but not required.
The lecture will be followed by a panel discussion of the public policy issues and how mathematical, computational, and experimental techniques might help address them. How should these techniques influence new districts that will be drawn based on the 2020 census?
- Philip Klein, Brown University
- Kosuke Imai, Harvard University
- John Marion, Common Cause Rhode Island
- Elisabeth Theodore, Arnold & Porter
4:00-4:45: Public Lecture
4:45-5:30: Panel discussion
5:30-5:45: Open question period
About the Speaker
Jonathan Christopher Mattingly grew up in Charlotte. He graduated from the NC School of Science and Mathematics and received a BS is Applied Mathematics with a concentration in physics from Yale University. After two years abroad with a year spent at ENS Lyon studying nonlinear and statistical physics on a Rotary Fellowship, he returned to the US to attend Princeton University where he obtained a PhD in Applied and Computational Mathematics in 1998 under the supervision of Yakov Sinai. After 4 years as a Szego assistant professor at Stanford University and a year as a member of the IAS in Princeton, he moved to Duke in 2003. He is currently a James B. Duke Professor of Mathematics and a Professor of Statistical Science. The is the recipient of an NSF CAREER award, a Presidential Early Career Award for Scientists and Engineers (PECASE), and a Sloan Foundation Faculty Fellowship. He is a fellow of the Institute for Mathematical Statistics (IMS) and the American Mathematics Society (AMS) and has served on the advisory boards for a number of NSF institutes. Mattingly's work centers on the long time behavior of random dynamical systems and stochastic partial differential equations in particular. In particular he has definitive works on the ergodic theory of the two-dimensional Navier-Stokes equations. He as also worked on the scaling limits and consistency of various stochastic numerical methods including Markov Chain Monte Carlo and methods to simulate stochastic differential equations. In addition he as worked on a number of biologically motivated problems including fluctuations in cell biochemical networks, the evolution and spread of influenza and the averaging of evolutionary trees. Since 2013 he has also been working to undersand and quantify gerrymandering and its interaction of a regions geopolitical landscape. This has lead him to testify in a number of court cases including Common Cause v. Rucho which went all the way to the US Supreme Court. He was also involved with a sequence of North Carolina state court cases which lead to the NC congressional and both NC legislative maps being deemed unconstitutional and replaced for the 2020 elections. He was awarded the Defender of Freedom award by the Common Cause for his work on Quantifying Gerrymandering.