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Activity Number: 112 - Statistics and Legislative Redistricting
Type: Invited
Date/Time: Monday, August 9, 2021 : 1:30 PM to 3:20 PM
Sponsor: Social Statistics Section
Abstract #316779
Title: Monte Carlo Methods for Revealing Gerrymandering
Author(s): Gregory J Herschlag* and Jonathan C. Mattingly
Companies: Department of Mathematics and Duke University
Keywords: Gerrymandering; Redistricting; MCMC; Fairness; High-Dimensional
Abstract:

A powerful method for detecting gerrymandering centers around generating large collections of alternative non-partisan redistricting plans. Novel Monte Carlo algorithms are continually being developed to generate these collections. In this talk, I will discuss several of these methods, as well as discuss how the selection of method and constraints on the space may affect the overall conclusions on whether or not a given plan has been gerrymandered. Examples will include non-reversible Markov chains and hierarchical/multi-scale methods. Time permitting, I will also discuss several investigations into the high-dimensional structure of the space of redistricting plans.


Authors who are presenting talks have a * after their name.

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