Mathematically Quantifying Non-responsiveness of the 2021 Georgia Congressional Districting Plan
Zhanzhan Zhao, Cyrus Hettle, Swati Gupta, Jonathan Mattingly, Dana, Randall, Gregory Herschlag

TL;DR
This paper introduces a computationally efficient method combining parallel tempering and ReCom to sample redistricting plans, demonstrating its effectiveness on Georgia's district maps and revealing significant non-responsiveness in the enacted plan.
Contribution
It develops and validates a novel parallel tempering algorithm for policy-informed redistricting sampling at statewide scale.
Findings
The Georgia district plan reliably elects 9 Republicans and 5 Democrats regardless of public opinion shifts.
Only 0.12% of sampled plans show similar non-responsiveness as the enacted plan.
The method effectively addresses computational challenges in sampling complex redistricting distributions.
Abstract
To audit political district maps for partisan gerrymandering, one may determine a baseline for the expected distribution of partisan outcomes by sampling an ensemble of maps. One approach to sampling is to use redistricting policy as a guide to precisely codify preferences between maps. Such preferences give rise to a probability distribution on the space of redistricting plans, and Metropolis-Hastings methods allow one to sample ensembles of maps from the specified distribution. Although these approaches have nice theoretical properties and have successfully detected gerrymandering in legal settings, sampling from commonly-used policy-driven distributions is often computationally difficult. As of yet, there is no algorithm that can be used off-the-shelf for checking maps under generic redistricting criteria. In this work, we mitigate the computational challenges in a…
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Taxonomy
TopicsElectoral Systems and Political Participation · Game Theory and Voting Systems
