Mathematically Quantifying Non-responsiveness of the 2021 Georgia Congressional Districting Plan-Reference-Cited by-同舟云学术

Mathematically Quantifying Non-responsiveness of the 2021 Georgia Congressional Districting Plan

Published:2022-10-06 Issue: Volume: Page:
ISSN:
Container-title:Equity and Access in Algorithms, Mechanisms, and Optimization
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Author:

Zhao Zhanzhan¹,Hettle Cyrus²,Gupta Swati³,Mattingly Jonathan Christopher⁴,Randall Dana¹,Herschlag Gregory Joseph⁵

Affiliation:

1. School of Computer Science, Georgia Institute of Technology, USA

2. School of Mathematics, Georgia Institute of Technology, USA

3. School of Industrial and Systems Engineering, Georgia Institute of Technology, USA

4. Department of Mathematics and Department of Statistical Science, Duke University, USA

5. Department of Mathematics, Duke University, USA

Funder

NSF

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3551624.3555300

Reference38 articles.

1. Eric Autry Daniel Carter Gregory Herschlag Zach Hunter and Jonathan C. Mattingly. 2021. Metropolized Forest Recombination for Monte Carlo Sampling of Graph Partitions. arxiv:1911.01503 [cs.DS] Eric Autry Daniel Carter Gregory Herschlag Zach Hunter and Jonathan C. Mattingly. 2021. Metropolized Forest Recombination for Monte Carlo Sampling of Graph Partitions. arxiv:1911.01503 [cs.DS]

2. Metropolized Multiscale Forest Recombination for Redistricting

3. Gerrymandering and Compactness: Implementation Flexibility and Abuse

4. Computational Redistricting and the Voting Rights Act

5. Unintentional Gerrymandering: Political Geography and Electoral Bias in Legislatures

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