Graduate-Faculty Seminar
Monday, October 25, 2021, 1:30pm, Physics 119
Gregory Herschlag (Department of Mathematics, Mathematics)
Quantifying Gerrymandering and Sampling Balanced Graph Partitions
Abstract:- Gerrymandering is the process of manipulating political districts either to amplify the power of a political group or suppress the representation of certain demographic groups. However, demonstrating whether or not a map has been gerrymandered had remained an elusive problem. Over the past several years, we have recast the idea of gerrymandering in terms of outlier analysis over a probability distribution on redistricting plans; i.e., if a given map is extreme in comparison with a representative collection of non-partisan redistricting maps, then we might conclude the given map is gerrymandered. Formally, this policy question is recast by using Monte Carlo techniques to sample from a distribution of balanced graph partitions.
In this talk, I will discuss advances in sampling balanced graph partitions, and some thoughts on how to formulate a distribution on the space. I will also discuss how we have employed these methods within the context of understanding gerrymandering, along with how our work has been successfully employed in litigation as a remedy to extreme gerrymandering.
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