Implementing Partisan Symmetry: Problems and Paradoxes

DeFord, Daryl R.; Dhamankar, Natasha; Duchin, Moon; Gupta, Varun; McPike, Mackenzie; Schoenbach, Gabe; Sim, Ki Wan

doi:10.1017/pan.2021.49

Cited by 15 publications

(14 citation statements)

References 16 publications

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“…They consider multi-member districts in which a district can cast votes for multiple candidates, and find that single transferable vote (STV) with independent commissions can achieve proportional outcomes in every state. (DeFord et al 2021) apply the partisan symmetry metric defined by (Katz, King, and Rosenblatt 2020) as a new fairness metric in designing voting districts in states of Utah, Texas and North Carolina. They find that using partisan symmetry alone can lead to unforeseen consequences in the election results.…”

Section: Real Life Examplesmentioning

confidence: 99%

Crowdsourcing Perceptions of Gerrymandering

Kelly¹,

Inwon²,

Xia³

2022

HCOMP

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Gerrymandering is the manipulation of redistricting to influence the results of a set of elections for local representatives. Gerrymandering has the potential to drastically swing power in legislative bodies even with no change in a population’s political views. Identifying gerrymandering and measuring fairness using metrics of proposed district plans is a topic of current research, but there is less work on how such plans will be perceived by voters. Gathering data on such perceptions presents several challenges such as the ambiguous definitions of ‘fair’ and the complexity of real world geography and district plans. We present a dataset collected from an online crowdsourcing platform on a survey asking respondents to mark which of two maps of equal population distribution but different districts appear more ‘fair’ and the reasoning for their decision. We performed preliminary analysis on this data and identified which of several commonly suggested metrics are most predictive of the responses. We found that the maximum perimeter of any district was the most predictive metric, especially with participants who reported that they made their decision based on the shape of the districts.

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Section: Real Life Examplesmentioning

confidence: 99%

Crowdsourcing Perceptions of Gerrymandering

Kelly¹,

Inwon²,

Xia³

2022

HCOMP

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“…Because the importance of a new electoral system rests solely on the consequences it may have for future elections to be held under its rules, past (and thus observed) election results may help with estimation but cannot define a reasonable notion of an electoral system's overall fairness. DeFord et al (2021) does not separate quantities of interest from empirical measures, and either ignores future elections or assumes that past election outcomes are exactly equal to future results. The article includes no definitions of quantities of interest, separation of these quantities from empirical measures, uncertainty estimates, estimators, or formal statistical properties of proposed measures.…”

Section: Overviewmentioning

confidence: 99%

“…Choosing this noninferential path enables DeFord et al (2021) to calculate many interesting descriptive statistics but rules out learning from the resulting calculations about the fairness of electoral systems that by definition depend on unobserved characteristics of the future. In this regard, every such deterministic claim in the article-using jarring terms to a social scientist or statistician about "mathematical guarantees," "deterministic" results, "locked out of Congressional representation," or "elementary mathematical manipulation[s]"-has no bearing on a measure's potential usefulness in evaluating the fairness of electoral systems or redistricting plans.…”

Section: Overviewmentioning

confidence: 99%

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The Essential Role of Statistical Inference in Evaluating Electoral Systems: A Response to DeFord et al.

Katz¹,

King²,

Rosenblatt³

2021

Polit. Anal.

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Katz, King, and Rosenblatt (2020) introduces a theoretical framework for understanding redistricting and electoral systems, built on basic statistical and social science principles of inference. DeFord et al. (Forthcoming, 2021) instead focuses solely on descriptive measures, which lead to the problems identified in our article. In this paper, we illustrate the essential role of these basic principles and then offer statistical, mathematical, and substantive corrections required to apply DeFord et al.'s calculations to social science questions of interest, while also showing how to easily resolve all claimed paradoxes and problems. We are grateful to the authors for their interest in our work and for this opportunity to clarify these principles and our theoretical framework.

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“…In a similar vein, there is growing evidence that the M M score is not performing as advertised to signal meaningful partisan advantage[16]. Nonetheless, we use it here to highlight that competitiveness may not be independent of other popular partisan indicators.…”

mentioning

confidence: 95%

A Computational Approach to Measuring Vote Elasticity and Competitiveness

DeFord¹,

Duchin²,

Solomon³

2020

Preprint

Self Cite

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The recent wave of attention to partisan gerrymandering has come with a push to refine or replace the laws that govern political redistricting around the country. A common element in several states' reform efforts has been the inclusion of competitiveness metrics, or scores that evaluate a districting plan based on the extent to which district-level outcomes are in play or are likely to be closely contested.In this paper, we examine several classes of competitiveness metrics motivated by recent reform proposals and then evaluate their potential outcomes across large ensembles of districting plans at the Congressional and state Senate levels. This is part of a growing literature using MCMC techniques from applied statistics to situate plans and criteria in the context of valid redistricting alternatives. Our empirical analysis focuses on five states-Utah, Georgia, Wisconsin, Virginia, and Massachusetts-chosen to represent a range of partisan attributes. We highlight situation-specific difficulties in creating good competitiveness metrics and show that optimizing competitiveness can produce unintended consequences on other partisan metrics. These results demonstrate the importance of (1) avoiding writing detailed metric constraints into long-lasting constitutional reform and (2) carrying out careful mathematical modeling on real geo-electoral data in each redistricting cycle.

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Implementing Partisan Symmetry: Problems and Paradoxes

Cited by 15 publications

References 16 publications

Crowdsourcing Perceptions of Gerrymandering

Crowdsourcing Perceptions of Gerrymandering

The Essential Role of Statistical Inference in Evaluating Electoral Systems: A Response to DeFord et al.

A Computational Approach to Measuring Vote Elasticity and Competitiveness

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