Location:
This workshop took place at Gross Hall room 330 – Ahmediah Hall on the campus of Duke University.
Description:
There is no question that both Democrats and Republicans have used their legislative authority to redraw voting districts in ways favorable to their own parties whenever they are in power. This is called gerrymandering, and it has been the subject of a number of recent court challenges. From a mathematical standpoint, the novelty is that the methodology for crafting politically advantageous districts has recently become quite sophisticated. This workshop reviewed the technical foundations for creating districts that optimize various criteria. It also addressed the methodology, especially Markov chain Monte Carlo techniques, for quantifying the extent to which gerrymandering puts a thumb on the scales of elections.
Organizers:
Schedule and Supporting Media
Printed Schedule
Speaker Abstracts
Poster Titles
Monday, October 8, 2018
Room 330 Gross Hall, Duke University
| Description | Speaker | Slides |
|---|---|---|
| Registration | ||
| Intro/Welcome Framing Conference | ||
| Using a Neutral Enables to Quantify Gerrymandering | Jonathan Mattingly, Duke University | |
| Redistricting Simulation through Markov Chain Monte Carlo: Progress and Challenges | Kosuke Imai, Harvard University | |
| Significance Tests in Markov Chains | Wesley Pegdan, Carnegie Mellon University | |
| DISCUSSION | ||
| Who is my Neighbor? The Spatial Efficiency of Partisanship | Jonathan Rodden, Stanford University | |
| An Open-Source Approach to Advancing Redistricting Reform: Virginia as Case Study | Will Adler, Princeton University | |
| Evaluating the Extent of Gerrymandering in Maryland | Lisa Lebovici, Duke University | |
| Panel Discussion | Jowei Chen, University of Michigan | |
| Jonathan Rodden, Stanford University | ||
| Poster Session and Reception |
Tuesday, October 9, 2018
Room 330 Gross Hall, Duke University
| Description | Speaker | Slides |
|---|---|---|
| Towards Algorithms for Districting Plans that Perfectly Balance Population and Nearly Minimize Dispersion | Philip Klein, Brown University | |
| Compactness Profiles and Reversible Sampling Methods for Plane and Graph Partitions | Daryl Deford, MIT | |
| Packing or Cracking: Systematic Measurement of Electoral Manipulation through Gerrymandering | Daniel Magleby, Binghamton University (SUNY) | |
| Panel Discussion | Andrew Chin, University of North Carolina at Chapel Hill; Jennifer Bremer, League of Women Voters of North Carolina – Orange-Durham-Chatham; Carolyn Mackie, Poyner Spruill LLP | |
| Questions that Mathematicians Can Answer (Maybe): A View from a Policy Practitioner | Jennifer Bremer, League of Women Voters of North Carolina – Orange-Durham-Chatham | |
| Partisan Gerrymandering Versus Geographic Compactness | Dustin Mixon, Ohio State University | |
| Machine Learning for Fair Redistricting (and hardness) | Soledad Villar, New York University | |
| Fair Division Approaches to Political Districting | Gerdus Benadé, Carnegie Mellon University | |
| Wrap Up Discussion |
Questions: email [email protected]