Electoral David vs Goliath: How does the Spatial Concentration of Electors affect District-based Elections?

• URL: http://arxiv.org/abs/2006.11865v1
• Date: Sun, 21 Jun 2020 18:17:57 GMT
• Title: Electoral David vs Goliath: How does the Spatial Concentration of Electors affect District-based Elections?
• Authors: Adway Mitra
• Abstract summary: district-based elections where there is a "seat" for each district in the governing body. In each district, the party whose candidate gets the maximum number of votes wins the corresponding seat. locations of the electors and boundaries of the districts may severely affect the election result even if the proportion of popular support of different parties remains unchanged. This has led to significant amount of research on whether the districts may be redrawn or electors may be moved to maximize seats for a particular party.
• Score: 0.5076419064097732
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many democratic countries use district-based elections where there is a "seat" for each district in the governing body. In each district, the party whose candidate gets the maximum number of votes wins the corresponding seat. The result of the election is decided based on the number of seats won by the different parties. The electors (voters) can cast their votes only in the district of their residence. Thus, locations of the electors and boundaries of the districts may severely affect the election result even if the proportion of popular support (number of electors) of different parties remains unchanged. This has led to significant amount of research on whether the districts may be redrawn or electors may be moved to maximize seats for a particular party. In this paper, we frame the spatial distribution of electors in a probabilistic setting, and explore different models to capture the intra-district polarization of electors in favour of a party, or the spatial concentration of supporters of different parties. Our models are inspired by elections in India, where supporters of different parties tend to be concentrated in certain districts. We show with extensive simulations that our model can capture different statistical properties of real elections held in India. We frame parameter estimation problems to fit our models to the observed election results. Since analytical calculation of the likelihood functions are infeasible for our complex models, we use Likelihood-free Inference methods under the Approximate Bayesian Computation framework. Since this approach is highly time-consuming, we explore how supervised regression using Logistic Regression or Deep Neural Networks can be used to speed it up. We also explore how the election results can change by varying the spatial distributions of the voters, even when the proportions of popular support of the parties remain constant.

Related papers

• ElectionSim: Massive Population Election Simulation Powered by Large Language Model Driven Agents [70.17229548653852]
  We introduce ElectionSim, an innovative election simulation framework based on large language models. We present a million-level voter pool sampled from social media platforms to support accurate individual simulation. We also introduce PPE, a poll-based presidential election benchmark to assess the performance of our framework under the U.S. presidential election scenario.
  arXiv  Detail & Related papers  (2024-10-28T05:25:50Z)
• 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)
• Bounds and Bugs: The Limits of Symmetry Metrics to Detect Partisan Gerrymandering [0.0]
  We consider two symmetry metrics to detect partisan gerrymandering: the Mean-Median Difference (MM) and Partisan Bias (PB) We first assert that the foundation of a partisan gerrymander is to draw a map so that the preferred party wins an extreme number of seats.
  arXiv  Detail & Related papers  (2024-06-18T00:39:30Z)
• Redistricting for Proportionality [0.0]
  American democracy is heavily reliant on plurality in single-member districts, or PSMD, as a system of election. We consider whether it is feasible to bring PSMD into alignment with a proportionality norm by targeting proportional outcomes in the design and selection of districts.
  arXiv  Detail & Related papers  (2023-08-22T15:56:40Z)
• Adaptively Weighted Audits of Instant-Runoff Voting Elections: AWAIRE [61.872917066847855]
  Methods for auditing instant-runoff voting (IRV) elections are either not risk-limiting or require cast vote records (CVRs), the voting system's electronic record of the votes on each ballot. We develop an RLA method that uses adaptively weighted averages of test supermartingales to efficiently audit IRV elections when CVRs are not available.
  arXiv  Detail & Related papers  (2023-07-20T15:55:34Z)
• Design and analysis of tweet-based election models for the 2021 Mexican legislative election [55.41644538483948]
  We use a dataset of 15 million election-related tweets in the six months preceding election day. We find that models using data with geographical attributes determine the results of the election with better precision and accuracy than conventional polling methods.
  arXiv  Detail & Related papers  (2023-01-02T12:40:05Z)
• Identifying Possible Winners in Ranked Choice Voting Elections with Outstanding Ballots [0.0]
  ranked-choice voting (RCV) allows voters to rank their choices, and the results are computed in rounds. RCV election outcomes are not always apparent on election night, and can take several weeks to be published. We present an algorithm for efficiently computing possible winners of RCV elections from partially known ballots.
  arXiv  Detail & Related papers  (2022-06-25T22:08:15Z)
• Agent-based Simulation of District-based Elections [0.5076419064097732]
  In district-based elections, electors cast votes in their respective districts. In each district, the party with maximum votes wins the corresponding seat in the governing body. The election result is based on the number of seats won by different parties.
  arXiv  Detail & Related papers  (2022-05-28T11:19:04Z)
• Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning [58.23459346724491]
  We use the "map of elections" approach of Szufa et al. (AAMAS 2020) to analyze several well-known vote distributions. We draw the "skeleton map" of distributions, evaluate its robustness, and analyze its properties.
  arXiv  Detail & Related papers  (2022-05-16T17:40:22Z)
• Exploring Fairness in District-based Multi-party Elections under different Voting Rules using Stochastic Simulations [0.5076419064097732]
  Many democratic societies use district-based elections, where the region under consideration is geographically divided into districts and a representative is chosen for each district based on the preferences of the electors who reside there. We show that this can lead to situations where many electors are dissatisfied with the election results, which is not desirable in a democracy. Inspired by current literature on fairness of Machine Learning algorithms, we define measures of fairness to quantify the satisfaction of electors, irrespective of their political choices.
  arXiv  Detail & Related papers  (2022-02-25T18:03:03Z)
• 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)

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.