A Practical Procedure for Detecting a Partisan Gerrymander

ArringtonTheodore, S

doi:10.1089/elj.2016.0383

Cited by 2 publications

(1 citation statement)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has the potential to cause social harm in the form of disenfranchisement to political and demographic groups [30]. Many studies in the quantitative sciences have attempted to characterize gerrymandering in terms of optimization criteria such as geographic compactness and proportional representation with respect to political parties [3,10,29,31], and many algorithms have been proposed to generate districts that optimize one or more of these criteria [4,5,11,13].…”

Section: Introductionmentioning

confidence: 99%

Auditing for Gerrymandering by Identifying Disenfranchised Individuals

Lin

Chen

Chmielewski

et al. 2022

2022 ACM Conference on Fairness, Accountability, and Transparency

View full text Add to dashboard Cite

Gerrymandering is the practice of drawing congressional districts to advantage or disadvantage particular electoral outcomes or population groups. We study the problem of computationally auditing a districting for evidence of gerrymandering. Our approach is novel in its emphasis on identifying individual voters disenfranchised by packing and cracking in local fine-grained geographic regions. We define a local score based on comparison with a representative sample of alternative districtings and use simulated annealing to algorithmically generate a witness districting to show that the score can be substantially reduced by simple local alterations. Unlike commonly studied metrics for gerrymandering such as proportionality and compactness, our framework is inspired by the legal context for voting rights in the United States. We demonstrate the use of our framework to analyze the congressional districting of the state of North Carolina in 2016. We identify a substantial number of geographically localized disenfranchised individuals, mostly Democrats in the central and north-eastern parts of the state. Our simulated annealing algorithm is able to generate a witness districting with a roughly 50% reduction in the number of disenfranchised individuals, suggesting that the 2016 districting was not predetermined by North Carolina's spatial structure. CCS CONCEPTS• Theory of computation → Random search heuristics; • Applied computing → Law.

show abstract

Section: Introductionmentioning

confidence: 99%

Auditing for Gerrymandering by Identifying Disenfranchised Individuals

Lin

Chen

Chmielewski

et al. 2022

2022 ACM Conference on Fairness, Accountability, and Transparency

View full text Add to dashboard Cite

show abstract

Automated Congressional Redistricting

Levin

Friedler

2019

ACM J. Exp. Algorithmics

View full text Add to dashboard Cite

Every 10 years, when states are forced to redraw their congressional districts, the process is intensely partisan, and the outcome is rarely fair and democratic. In the past few decades, the growing capabilities of computers have offered the promise of objective, computerized redistricting. Unfortunately, the redistricting problem can be shown to be NP-Complete, but there are a number of heuristics that are effective. We specifically define the redistricting problem and analyze several variations of a new divide and conquer algorithm, comparing the compactness and population deviation of our new algorithm to existing algorithms and the actual districts. We offer a comparative component-based analysis that demonstrates the strengths and weaknesses of each algorithm component and the type of input. The comparative analysis shows that there are several ways to produce valid redistricting plans, but each approach has benefits and consequences. Our new algorithm produces valid results to the redistricting problem in almost every state that undergoes congressional redistricting, offering a new solution to this challenging real-world problem. In one version, the algorithm produces plans with the optimal population deviation in 42 out of 43 multi-district states, which is better than most algorithms in the literature. While compactness scores vary, this approach offers new opportunities to improve population deviation. Our output files comply with the accepted format at most government hearings and redistricting competitions, so the results would be compatible with most public participation efforts in 2020.

show abstract

A Practical Procedure for Detecting a Partisan Gerrymander

Cited by 2 publications

References 76 publications

Auditing for Gerrymandering by Identifying Disenfranchised Individuals

Auditing for Gerrymandering by Identifying Disenfranchised Individuals

Automated Congressional Redistricting

Contact Info

Product

Resources

About