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Committee selection with multimodal preferences

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TLDR
This work designs efficient algorithms for certain cases of committee selection with multimodal preferences and discusses applications of the model and the computational complexity of several generalizations of known committee scoring rules to this setting.
Abstract
We study committee selection with multimodal preferences: Assuming a set of candidatesA, a set of voters V , and ` layers, where each voter v ∈ V has ordinal preferences over the alternatives for each layer separately, the task is to select a committee S ⊆ A of size k. We discuss applications of our model and study the computational complexity of several generalizations of known committee scoring rules (specifically, k-Borda and Chamberlin–Courant) to our setting, as well as discuss domain restrictions for our model. While most problems we encounter are computationally intractable in general, we nevertheless design efficient algorithms for certain cases.

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Citations
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Proceedings ArticleDOI

Position-Based Matching with Multi-Modal Preferences

TL;DR: In this article , the authors introduce three position-based matching models, which minimize the "dissatisfaction score", which measures matchings from different perspectives, and present diverse complexity results for these three models, among others, polynomial-time solvability for the first model.
Proceedings ArticleDOI

Stable Matching with Multilayer Approval Preferences: Approvals can be Harder than Strict Preferences

TL;DR: In this paper , the complexity of stable matching problems with multilayer approval preferences was studied and eleven stability notions derived from three well-established stability notions for stable matchings with ties and four adaptions proposed by Chen et al.
Proceedings ArticleDOI

Distortion in Attribute Approval Committee Elections

TL;DR: In this article , the distortion in attribute approval committee elections is studied, where each candidate satisfies a variety of attributes in different categories (e.g., academic degree, work experience, lo-cation).
Journal ArticleDOI

Multi-votes Election Control by Selecting Rules

Aizhong Zhou, +1 more
- 30 Jun 2023 - 
TL;DR: In this paper , the authors considered a new model of election control that by assigning different rules to the votes from different layers, makes the special candidate p being the winner of the election (a rule can be assigned to different layers).
References
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Proceedings Article

Multi-attribute proportional representation

TL;DR: This work considers the following problem in which a given number of items has to be chosen from a predefined set, and studies the properties of the associated subset selection rules, as well as their computation complexity.
Posted Content

Single-Peakedness and Total Unimodularity: New Polynomial-Time Algorithms for Multi-Winner Elections

TL;DR: This technique gives efficient algorithms for finding optimal committees under Proportional Approval Voting (PAV) and the Chamberlin–Courant rule with single-peaked preferences, as well as for certain OWA-based rules.
Proceedings ArticleDOI

Egalitarian Committee Scoring Rules

TL;DR: This work introduces and study the class of egalitarian variants of committee scoring rules, where instead of summing up the scores that voters assign to committees, the score of a committee is taken to be the lowest score assigned to it by any voter.
Proceedings Article

Single-Peakedness and Total Unimodularity: New Polynomial-Time Algorithms for Multi-Winner Elections.

TL;DR: In this paper, a new technique called carefully chosen integer linear programming (IP) formulations for certain voting problems admit an LP relaxation which is totally unimodular if preferences are single-peaked, and thus admits an integral optimal solution.
Journal ArticleDOI

Achieving fully proportional representation by clustering voters

TL;DR: In this article, the authors developed several approximation algorithms for the Chamberlin-Courant and Monroe voting rules and experimentally analyzed their performance, and found that their algorithms are computationally efficient and, in many cases, are able to provide solutions which are very close to optimal.