Comparing Voting Districts with Uncertain Data Envelopment Analysis

• URL: http://arxiv.org/abs/2212.07779v2
• Date: Fri, 28 Jul 2023 16:39:43 GMT
• Title: Comparing Voting Districts with Uncertain Data Envelopment Analysis
• Authors: Casey Garner, Allen Holder
• Abstract summary: Gerrymandering voting districts is one of the most salient concerns of contemporary American society. We show how to use uncertain data envelopment analysis to assess maps on a variety of metrics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Gerrymandering voting districts is one of the most salient concerns of contemporary American society, and the creation of new voting maps, along with their subsequent legal challenges, speaks for much of our modern political discourse. The legal, societal, and political debate over serviceable voting districts demands a concept of fairness, which is a loosely characterized, but amorphous, concept that has evaded precise definition. We advance a new paradigm to compare voting maps that avoids the pitfalls associated with an a priori metric being used to uniformly assess maps. Our evaluative method instead shows how to use uncertain data envelopment analysis to assess maps on a variety of metrics, a tactic that permits each district to be assessed separately and optimally. We test our methodology on a collection of proposed and publicly available maps to illustrate our assessment strategy.

Related papers

• Don't Trust A Single Gerrymandering Metric [0.0]
  We show that each of these metrics is gameable when used as a single, isolated quantity to detect gerrymandering. We do this by using a hill-climbing method to generate district plans that are constrained by the bounds on the metric but also maximize or nearly maximize the number of districts won by a party. One clear consequence of these results is that they demonstrate the folly of specifying a priori bounds on a metric that a redistricting commission must meet in order to avoid gerrymandering.
  arXiv  Detail & Related papers  (2024-09-25T02:40:09Z)
• Representation Bias in Political Sample Simulations with Large Language Models [54.48283690603358]
  This study seeks to identify and quantify biases in simulating political samples with Large Language Models. Using the GPT-3.5-Turbo model, we leverage data from the American National Election Studies, German Longitudinal Election Study, Zuobiao dataset, and China Family Panel Studies.
  arXiv  Detail & Related papers  (2024-07-16T05:52:26Z)
• Efficient Weighting Schemes for Auditing Instant-Runoff Voting Elections [57.67176250198289]
  AWAIRE involves adaptively weighted averages of test statistics, essentially "learning" an effective set of hypotheses to test. We explore schemes and settings more extensively, to identify and recommend efficient choices for practice. A limitation of the current AWAIRE implementation is its restriction to a small number of candidates.
  arXiv  Detail & Related papers  (2024-02-18T10:13:01Z)
• Better Understanding Differences in Attribution Methods via Systematic Evaluations [57.35035463793008]
  Post-hoc attribution methods have been proposed to identify image regions most influential to the models' decisions. We propose three novel evaluation schemes to more reliably measure the faithfulness of those methods. We use these evaluation schemes to study strengths and shortcomings of some widely used attribution methods over a wide range of models.
  arXiv  Detail & Related papers  (2023-03-21T14:24:58Z)
• Towards Better Understanding Attribution Methods [77.1487219861185]
  Post-hoc attribution methods have been proposed to identify image regions most influential to the models' decisions. We propose three novel evaluation schemes to more reliably measure the faithfulness of those methods. We also propose a post-processing smoothing step that significantly improves the performance of some attribution methods.
  arXiv  Detail & Related papers  (2022-05-20T20:50:17Z)
• Mathematically Quantifying Non-responsiveness of the 2021 Georgia Congressional Districting Plan [3.097163558730473]
  We use a Metropolized-sampling technique through a parallel tempering method combined with ReCom. We develop these improvements through the first case study of district plans in Georgia. Our analysis projects that any election in Georgia will reliably elect 9 Republicans and 5 Democrats under the enacted plan.
  arXiv  Detail & Related papers  (2022-03-13T02:58:32Z)
• Implications of Distance over Redistricting Maps: Central and Outlier Maps [6.757783454836096]
  In representative democracy, a redistricting map is chosen to partition an electorate into a collection of districts each of which elects a representative. A valid redistricting map must satisfy a collection of constraints such as being compact, contiguous, and of almost equal population. This fact introduces a difficulty in drawing redistricting maps and it also enables a partisan legislature to possibly gerrymander by choosing a map which unfairly favors it.
  arXiv  Detail & Related papers  (2022-03-02T04:59:30Z)
• Identification of Subgroups With Similar Benefits in Off-Policy Policy Evaluation [60.71312668265873]
  We develop a method to balance the need for personalization with confident predictions. We show that our method can be used to form accurate predictions of heterogeneous treatment effects.
  arXiv  Detail & Related papers  (2021-11-28T23:19:12Z)
• Mundus vult decipi, ergo decipiatur: Visual Communication of Uncertainty in Election Polls [56.8172499765118]
  We discuss potential sources of bias in nowcasting and forecasting. Concepts are presented to attenuate the issue of falsely perceived accuracy. One key idea is the use of Probabilities of Events instead of party shares.
  arXiv  Detail & Related papers  (2021-04-28T07:02:24Z)
• Knapsack Voting for Participatory Budgeting [4.853751680856816]
  We introduce a novel scheme tailored to participatory budgeting called "Knapsack Voting" We show that it is strategy-proof under a natural model of utility. We extend Knapsack Voting to more general settings with revenues, deficits or surpluses, and prove a similar strategy-proofness result.
  arXiv  Detail & Related papers  (2020-09-15T03:58:03Z)
• A Computational Approach to Measuring Vote Elasticity and Competitiveness [24.88022242349515]
  competitiveness metrics evaluate a districting plan based on the extent to which district-level outcomes are in play or are likely to be closely contested. This paper examines 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. We highlight situation-specific difficulties in creating good competitiveness metrics and show that optimizing competitiveness can produce unintended consequences on other partisan metrics.
  arXiv  Detail & Related papers  (2020-05-26T14:01:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.