Truth-tracking via Approval Voting: Size Matters

• URL: http://arxiv.org/abs/2112.04387v1
• Date: Tue, 7 Dec 2021 12:29:49 GMT
• Title: Truth-tracking via Approval Voting: Size Matters
• Authors: Tahar Allouche, J\'er\^ome Lang, Florian Yger
• Abstract summary: We consider a simple setting where votes consist of approval ballots. Each voter approves a set of alternatives which they believe can possibly be the ground truth. We define several noise models that are approval voting variants of the Mallows model.
• Score: 3.113227275600838
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Epistemic social choice aims at unveiling a hidden ground truth given votes, which are interpreted as noisy signals about it. We consider here a simple setting where votes consist of approval ballots: each voter approves a set of alternatives which they believe can possibly be the ground truth. Based on the intuitive idea that more reliable votes contain fewer alternatives, we define several noise models that are approval voting variants of the Mallows model. The likelihood-maximizing alternative is then characterized as the winner of a weighted approval rule, where the weight of a ballot decreases with its cardinality. We have conducted an experiment on three image annotation datasets; they conclude that rules based on our noise model outperform standard approval voting; the best performance is obtained by a variant of the Condorcet noise model.

Related papers

• Improving the Computational Efficiency of Adaptive Audits of IRV Elections [54.427049258408424]
  AWAIRE can audit IRV contests with any number of candidates, but the original implementation incurred memory and computation costs that grew superexponentially with the number of candidates. This paper improves the algorithmic implementation of AWAIRE in three ways that make it practical to audit IRV contests with 55 candidates, compared to the previous 6 candidates.
  arXiv  Detail & Related papers  (2024-07-23T13:28:00Z)
• 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)
• The Decisive Power of Indecision: Low-Variance Risk-Limiting Audits and Election Contestation via Marginal Mark Recording [51.82772358241505]
  Risk-limiting audits (RLAs) are techniques for verifying the outcomes of large elections. We define new families of audits that improve efficiency and offer advances in statistical power. New audits are enabled by revisiting the standard notion of a cast-vote record so that it can declare multiple possible mark interpretations.
  arXiv  Detail & Related papers  (2024-02-09T16:23:54Z)
• 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)
• Proportional Aggregation of Preferences for Sequential Decision Making [20.374669324368625]
  We study the problem of fair sequential decision making given voter preferences. In each round, a decision rule must choose a decision from a set of alternatives where each voter reports which of these alternatives they approve. We formalize this aim using axioms based on Proportional Justified Representation.
  arXiv  Detail & Related papers  (2023-06-26T17:10:10Z)
• Multi-winner Approval Voting Goes Epistemic [6.933322579961287]
  We consider contexts where the truth consists of a set of objective winners, knowing a lower and upper bound on its cardinality. A prototypical problem for this setting is the aggre-gation of multi-label annotations with prior knowledge on the size of the ground truth. We posit noisemodels, for which we define rules that output an optimal set of winners.
  arXiv  Detail & Related papers  (2022-01-17T23:07:14Z)
• Obvious Manipulability of Voting Rules [105.35249497503527]
  The Gibbard-Satterthwaite theorem states that no unanimous and non-dictatorial voting rule is strategyproof. We revisit voting rules and consider a weaker notion of strategyproofness called not obvious manipulability.
  arXiv  Detail & Related papers  (2021-11-03T02:41:48Z)
• Bribery as a Measure of Candidate Success: Complexity Results for Approval-Based Multiwinner Rules [58.8640284079665]
  We study the problem of bribery in multiwinner elections, for the case where the voters cast approval ballots (i.e., sets of candidates they approve) We consider a number of approval-based multiwinner rules (AV, SAV, GAV, RAV, approval-based Chamberlin--Courant, and PAV) In general, our problems tend to be easier when we limit out bribery actions on increasing the number of approvals of the candidate that we want to be in a winning committee.
  arXiv  Detail & Related papers  (2021-04-19T08:26:40Z)
• Modeling Voters in Multi-Winner Approval Voting [24.002910959494923]
  We study voting behavior in single-winner and multi-winner approval voting scenarios with varying degrees of uncertainty. We find that people generally manipulate their vote to obtain a better outcome, but often do not identify the optimal manipulation. We propose a novel model that takes into account the size of the winning set and human cognitive constraints.
  arXiv  Detail & Related papers  (2020-12-04T19:24:28Z)
• Evaluating approval-based multiwinner voting in terms of robustness to noise [10.135719343010177]
  We show that approval-based multiwinner voting is always robust to reasonable noise. We further refine this finding by presenting a hierarchy of rules in terms of how robust to noise they are.
  arXiv  Detail & Related papers  (2020-02-05T13:17:43Z)
• Objective Social Choice: Using Auxiliary Information to Improve Voting Outcomes [16.764511357821043]
  How should one combine noisy information from diverse sources to make an inference about an objective ground truth? We propose a multi-arm bandit noise model and count-based auxiliary information set. We find that our rules successfully use auxiliary information to outperform the naive baselines.
  arXiv  Detail & Related papers  (2020-01-27T21:21:19Z)

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.