Counterexamples in Political Redistricting
Speaker: Justin Solomon
Location: 60 Fifth Avenue 150
Date: May 15, 2019, 5:30 p.m.
Efforts to counteract gerrymandering have increased enthusiasm for computational techniques that can design and evaluate voting districts. While algorithms hold the potential to enumerate many ways to district a state and to quantify measures of fairness, partisanship, and geometric quality, careful analysis is needed before we can place trust in software as an unbiased and effective arbiter in redistricting cases. In this talk, I will highlight recent progress in computational aspects of redistricting in parallel with considerations from computational complexity, random sampling, and noisy data processing that should be taken into account as we propose algorithmic options for redistricting. In particular, we will study subtle challenges that arise in methods for measuring district compactness and for gathering aggregate statistics about the "space" of districting plans, as well as potential means of resolving these issues. Particular focus will be put on open problems in need of mathematical, statistical, and algorithmic consideration.
Joint work with R. Barnes, D. DeFord, M. Duchin, H. Lavenant, L. Najt, and Z. Schutzmann.
Justin Solomon is an assistant professor of Electrical Engineering and Computer Science at MIT. He leads the Geometric Data Processing Group in the Computer Science and Artificial Intelligence Laboratory and is a member of the Metric Geometry and Gerrymandering Group.