An Analysis of Approval-Based Committee Rules for 2D-Euclidean Elections

Godziszewski, Michał Tomasz; Batko, P.; Skowron, Piotr; Faliszewski, Piotr

doi:10.1609/aaai.v35i6.16686

Cited by 14 publications

(5 citation statements)

References 21 publications

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“…Multiwinner voting is the process of selecting a fixed number of candidates (a committee) based on the preferences of agents. This general task occurs in a wide range of applications such as group recommendations [Lu and Boutilier, 2011;Gawron and Faliszewski, 2022], blockchain protocols [Cevallos and Stewart, 2021], political elections [Renwick and Pilet, 2016;Brill et al, 2018], and the design of Q&A platforms [Israel and Brill, 2021]. As it is apparent from this diverse list of applications, there is a multitude of desiderata for multiwinner voting rules and their desirability depends on the setting in which they are applied.…”

Section: Introductionmentioning

confidence: 99%

An Experimental Comparison of Multiwinner Voting Rules on Approval Elections

Faliszewski

Lackner

Sornat

et al. 2023

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence

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In this paper, we experimentally compare major approval based multiwinner voting rules. To this end, we define a measure of similarity between two equal sized committees subject to a given election. Using synthetic elections coming from several distributions, we analyze how similar are the committees provided by prominent voting rules. Our results can be visualized as maps of voting rules, which provide a counterpoint to a purely axiomatic classification of voting rules. The strength of our proposed method is its independence from preimposed classifications (such as the satisfaction of concrete axioms), and that it indeed offers a much finer distinction than the current state of axiomatic analysis.

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Section: Introductionmentioning

confidence: 99%

An Experimental Comparison of Multiwinner Voting Rules on Approval Elections

Faliszewski

Lackner

Sornat

et al. 2023

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence

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“…In contrast to the positional scoring rule kapproval, the number of approved candidates is not fixed under AV. In the spatial framework, it is often assumed that each voter v j is associated with an approval radius ρ j ∈ R and approves all candidates that are at a distance of at most ρ j from voter v j 's position (e.g., Godziszewski et al 2021).…”

Section: Partial Spatial Approval Votingmentioning

confidence: 99%

Spatial Voting with Incomplete Voter Information

Imber,

Israel,

Brill

et al. 2024

AAAI

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We consider spatial voting where candidates are located in the Euclidean d-dimensional space, and each voter ranks candidates based on their distance from the voter's ideal point. We explore the case where information about the location of voters' ideal points is incomplete: for each dimension, we are given an interval of possible values. We study the computational complexity of finding the possible and necessary winners for positional scoring rules. Our results show that we retain tractable cases of the classic model where voters have partial-order preferences. Moreover, we show that there are positional scoring rules under which the possible-winner problem is intractable for partial orders, but tractable in the one-dimensional spatial setting. We also consider approval voting in this setting. We show that for up to two dimensions, the necessary-winner problem is tractable, while the possible-winner problem is hard for any number of dimensions.

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“…The abcvoting package has been used in a number of publications (Brill et al, 2022;Fairstein et al, 2022;Godziszewski et al, 2021;Lackner & Skowron, 2020;Szufa et al, 2022). In addition, it contains Python code for many of the examples appearing in the book Multi-winner voting with approval preferences (Lackner & Skowron, 2023).…”

Section: Statement Of Needmentioning

confidence: 99%

abcvoting: A Python package for approval-based multi-winner voting rules

Lackner¹,

Regner²,

Krenn³

2023

JOSS

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The Python package abcvoting is a research tool to explore and analyse approval-based committee (ABC) elections (Faliszewski et al., 2017;Lackner & Skowron, 2023). First and foremost, it contains implementations of major ABC voting rules. These are voting rules that accept as input approval ballots, that is, the (binary) preferences of voters expressing which candidates they like or support. The output is a fixed-size subset of candidates, called a committee. Different ABC voting rules represent different approaches how such a committee should be formed. For example, there is a trade-off between selecting only widely supported candidates and choosing a committee that represent as many voters as possible (Lackner & Skowron, 2020). Much of the recent research has focussed on developing ABC voting rules that reflect the preferences of voters in a proportional fashion.Lackner et al. (2023). abcvoting: A Python package for approval-based multi-winner voting rules.

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An Analysis of Approval-Based Committee Rules for 2D-Euclidean Elections

Cited by 14 publications

References 21 publications

An Experimental Comparison of Multiwinner Voting Rules on Approval Elections

An Experimental Comparison of Multiwinner Voting Rules on Approval Elections

Spatial Voting with Incomplete Voter Information

abcvoting: A Python package for approval-based multi-winner voting rules

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