Dynamic Proportional Rankings

Israel, Jonas; Brill, Markus

doi:10.48550/arxiv.2105.08043

Cited by 2 publications

(2 citation statements)

References 11 publications

(22 reference statements)

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“…Recent studies address fairness in online versions of other public decision-making problems, such as dynamic proportional rankings [Israel and Brill, 2021] and participatory budgeting [Lackner et al, 2021].…”

Section: Related Workmentioning

confidence: 99%

Online Approval Committee Elections

Do¹,

Hervouin²,

Lang³

et al. 2022

Preprint

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Assume k candidates need to be selected. The candidates appear over time. Each time one appears, it must be immediately selected or rejected-a decision that is made by a group of individuals through voting. Assume the voters use approval ballots, i.e., for each candidate they only specify whether they consider it acceptable or not. This setting can be seen as a voting variant of choosing k secretaries. Our contribution is twofold. (1) We assess to what extent the committees that are computed online can proportionally represent the voters. (2) If a prior probability over candidate approvals is available, we show how to compute committees with maximal expected score.

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Section: Related Workmentioning

confidence: 99%

Online Approval Committee Elections

Do¹,

Hervouin²,

Lang³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Sonar et al [35] showed that even checking whether a given committee is optimal when using these two rules is hard. Finally, the hardness of problems involving restricted voter preferences and committee selection rule have been studied [36,37] and so has the proportional representation in dynamic ranking [38].…”

Section: Fairness In Ranking and Set Selectionmentioning

confidence: 99%

DiRe Committee : Diversity and Representation Constraints in Multiwinner Elections

Relia¹

2021

Preprint

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The study of fairness in multiwinner elections focuses on settings where candidates have attributes. However, voters may also be divided into predefined populations under one or more attributes (e.g., "California" and "Illinois" populations under the "state" attribute), which may be same or different from candidate attributes. The models that focus on candidate attributes alone may systematically under-represent smaller voter populations. Hence, we develop a model, DiRe Committee Winner Determination (DRCWD), which delineates candidate and voter attributes to select a committee by specifying diversity and representation constraints and a voting rule. We show the generalizability of our model, and analyze its computational complexity, inapproximability, and parameterized complexity. We develop a heuristic-based algorithm, which finds the winning DiRe committee in under two minutes on 63% of the instances of synthetic datasets and on 100% of instances of real-world datasets. We present an empirical analysis of the running time, feasibility, and utility traded-off. Overall, DRCWD motivates that a study of multiwinner elections should consider both its actors, namely candidates and voters, as candidate-specific "fair" models can unknowingly harm voter populations, and vice versa. Additionally, even when the attributes of candidates and voters coincide, it is important to treat them separately as having a female candidate on the committee, for example, is different from having a candidate on the committee who is preferred by the female voters, and who themselves may or may not be female.

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Dynamic Proportional Rankings

Cited by 2 publications

References 11 publications

Online Approval Committee Elections

Online Approval Committee Elections

DiRe Committee : Diversity and Representation Constraints in Multiwinner Elections

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