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Tideman, Nicolaus; Richardson, Daniel

doi:10.1023/a:1005082925477

Cited by 31 publications

(3 citation statements)

References 3 publications

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“…If Q is non-empty, we add those candidates to the committee and multiply the weight of all votes cast for each candidate c ∈ Q by q/t c . If Q is empty, we add the last-ranked candidate to D. The process is repeated until |S| ≥ k, and the result is the set of all k-subsets of S [29,54].…”

Section: Voting and Allocation Rulesmentioning

confidence: 99%

Spoiler Susceptibility in Multi-District Party Elections

Boratyn¹,

Słomczyński²,

Stolicki³

et al. 2022

Preprint

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Electoral spoilers are such agents that there exists a coalition of agents whose total gain when a putative spoiler is eliminated exceeds that spoiler's share in the election outcome. So far spoiler effects have been analyzed primarily in the context of single-winner electoral systems. We consider this problem in the context of multi-district party elections. We introduce a formal measure of a party's excess electoral impact, treating "spoilership" as a manner of degree. This approach allows us to compare multi-winner social choice rules according to their degree of spoiler susceptibility. We present experimental results, as well as analytical results for toy models, for seven classical rules (k-Borda, Chamberlin-Courant, Harmonic-Borda, Jefferson-D'Hondt, PAV, SNTV, and STV). Since the probabilistic models commonly used in computational social choice have been developed for non-party elections, we extend them to be able to generate multi-district party elections.

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Section: Voting and Allocation Rulesmentioning

confidence: 99%

Spoiler Susceptibility in Multi-District Party Elections

Boratyn¹,

Słomczyński²,

Stolicki³

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…STV is now widespread and used for elections in many countries such as Australia, Ireland, India, and Pakistan. Moreover, it has been shown that STV achieves fair outcomes because it is one of the few rules that satisfies proportionality for solid coalitions (Woodall, 1994;Tideman and Richardson, 2000). As a consequence of its wide applicability, many works have focused on better understanding STV (e.g.…”

Section: Proportional Representationmentioning

confidence: 99%

The expanding approvals rule: improving proportional representation and monotonicity

Aziz

Lee

2019

Soc Choice Welf

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Proportional representation (PR) is often discussed in voting settings as a major desideratum. For the past century or so, it is common both in practice and in the academic literature to jump to single transferable vote (STV) as the solution for achieving PR. Some of the most prominent electoral reform movements around the globe are pushing for the adoption of STV. It has been termed a major open problem to design a voting rule that satisfies the same PR properties as STV and better monotonicity properties. In this paper, we first present a taxonomy of proportional representation axioms for general weak order preferences, some of which generalise and strengthen previously introduced concepts. We then present a rule called Expanding Approvals Rule (EAR) that satisfies properties stronger than the central PR axiom satisfied by STV, can handle indifferences in a convenient and computationally efficient manner, and also satisfies better candidate monotonicity properties. In view of this, our proposed rule seems to be a compelling solution for achieving proportional representation in voting settings.

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“…Many of concrete STV rules have been considered in the literature (see the works by the authors of [18,19] for a history and a summary of many important STV rules). However, for simplicity, in this survey, we discuss only STV rules where initially all voters have weight 1, and the uniform reweighting approach is used in Step 2.…”

Section: Complexity Of Computing Complexity Of Testingmentioning

confidence: 99%

Fairness in Algorithmic Decision-Making: Applications in Multi-Winner Voting, Machine Learning, and Recommender Systems

Shrestha

Yang

2019

Algorithms

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Algorithmic decision-making has become ubiquitous in our societal and economic lives. With more and more decisions being delegated to algorithms, we have also encountered increasing evidence of ethical issues with respect to biases and lack of fairness pertaining to algorithmic decision-making outcomes. Such outcomes may lead to detrimental consequences to minority groups in terms of gender, ethnicity, and race. As a response, recent research has shifted from design of algorithms that merely pursue purely optimal outcomes with respect to a fixed objective function into ones that also ensure additional fairness properties. In this study, we aim to provide a broad and accessible overview of the recent research endeavor aimed at introducing fairness into algorithms used in automated decision-making in three principle domains, namely, multi-winner voting, machine learning, and recommender systems. Even though these domains have developed separately from each other, they share commonality with respect to decision-making as an application, which requires evaluation of a given set of alternatives that needs to be ranked with respect to a clearly defined objective function. More specifically, these relate to tasks such as (1) collectively selecting a fixed number of winner (or potentially high valued) alternatives from a given initial set of alternatives; (2) clustering a given set of alternatives into disjoint groups based on various similarity measures; or (3) finding a consensus ranking of entire or a subset of given alternatives. To this end, we illustrate a multitude of fairness properties studied in these three streams of literature, discuss their commonalities and interrelationships, synthesize what we know so far, and provide a useful perspective for future research.

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Untitled

Cited by 31 publications

References 3 publications

Spoiler Susceptibility in Multi-District Party Elections

Spoiler Susceptibility in Multi-District Party Elections

The expanding approvals rule: improving proportional representation and monotonicity

Fairness in Algorithmic Decision-Making: Applications in Multi-Winner Voting, Machine Learning, and Recommender Systems

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