PrefLib: A Library for Preferences http://www.preflib.org

Mattei, Nicholas; Walsh, Toby

doi:10.1007/978-3-642-41575-3_20

Cited by 148 publications

(148 citation statements)

References 13 publications

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“…For the sampling step, we built on the implementations of Mallows-φ and an urn model from Mattei and Walsh (2013) to generate preference profiles of which we considered the majority relation. The computation of the various tournament solutions was done via counting (CO), matrix multiplication (UC , UC ∞ ), depth-first-search (TC ), linear programming (BP , MC ), eigenvalue decomposition (MA), branch-and-bound (SL), or tailored algorithms (BA, TEQ).…”

Section: Resultsmentioning

confidence: 99%

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On the Discriminative Power of Tournament Solutions

Brandt

Seedig

2016

Operations Research Proceedings

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Tournament solutions constitute an important class of social choice functions that only depend on the pairwise majority comparisons between alternatives. Recent analytical results have shown that several concepts with appealing axiomatic properties such as the Banks set or the minimal covering set tend to not discriminate at all when the tournaments are chosen from the uniform distribution. This is in sharp contrast to empirical studies which have found that real-world preference profiles often exhibit Condorcet winners, i.e., alternatives that all tournament solutions select as the unique winner. In this work, we aim to fill the gap between these extremes by examining the distribution of the number of alternatives returned by common tournament solutions for empirical data as well as data generated according to stochastic preference models such as impartial culture, impartial anonymous culture, Mallows mixtures, spatial models, and Pólya-Eggenberger urn models.

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Section: Resultsmentioning

confidence: 99%

“…In the preference library PREFLIB (Mattei and Walsh, 2013), scholars have contributed data sets from real world scenarios ranging from preferences over movies or sushi via Formula 1 championship results to real election data. At the time of writing, PREFLIB contained 354 tournaments induced from pairwise majority comparisons.…”

Section: Empirical Datamentioning

confidence: 99%

On the Discriminative Power of Tournament Solutions

Brandt

Seedig

2016

Operations Research Proceedings

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“…Test Data. We use Preflib [20] as a well-known source for real-world elections. Since Preflib contains only few elections with approval preferences (provided through linear orderings with ties containing exactly two groups of tied candidates each), we used elections with strict linear-order preferences and for each voter we uniformly at random chose how many of the top candidates this voter approves of.…”

Section: Preliminary Empirical Evaluationmentioning

confidence: 99%

Elections with Few Candidates: Prices, Weights, and Covering Problems

Bredereck

Faliszewski

Niedermeier

et al. 2015

Algorithmic Decision Theory

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Abstract. We show that a number of election-related problems with prices (such as, for example, bribery) are fixed-parameter tractable (in FPT) when parameterized by the number of candidates. For bribery, this resolves a nearly 10-year old family of open problems. Our results follow by a general technique that formulates voting problems as covering problems and extends the classic approach of using integer linear programming and the algorithm of Lenstra [19]. In this context, our central result is that WEIGHTED SET MULTICOVER parameterized by the universe size is fixed-parameter tractable. Our approach is also applicable to weighted electoral control for Approval voting. We improve previously known XP-memberships to FPT-memberships. Our preliminary experiments on real-world-based data show the practical usefulness of our approach for instances with few candidates.

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“…In this section, we evaluate their average-case performance on simulated as well as real data, and compare them against nine well-known voting rules: plurality, approval voting, Borda count, STV, Kemeny's rule, the maximin rule, Copeland's rule, Bucklin's rule, and Tideman's rule. 4 We perform three experiments: (i) choosing a utility profile uniformly at random from the simplex of all utility profiles, (ii) drawing a real-world utility profile from the Jester datasets (Goldberg, Roeder, Gupta, & Perkins, 2001), and (iii) drawing a real-world preference profile from the PrefLib datasets (Mattei & Walsh, 2013), and choosing a consistent utility profile uniformly at random. For each experiment, we have 8 voters and 10 alternatives, and test for k ∈ [4].…”

Section: Empirical Comparisonsmentioning

confidence: 99%

Subset Selection Via Implicit Utilitarian Voting

Caragiannis¹,

Nath²,

Procaccia³

et al. 2017

jair

101

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How should one aggregate ordinal preferences expressed by voters into a measurably superior social choice? A well-established approach -which we refer to as implicit utilitarian voting -assumes that voters have latent utility functions that induce the reported rankings, and seeks voting rules that approximately maximize utilitarian social welfare. We extend this approach to the design of rules that select a subset of alternatives. We derive analytical bounds on the performance of optimal (deterministic as well as randomized) rules in terms of two measures, distortion and regret. Empirical results show that regret-based rules are more compelling than distortion-based rules, leading us to focus on developing a scalable implementation for the optimal (deterministic) regret-based rule. Our methods underlie the design and implementation of RoboVote.org, a not-for-profit website that helps users make group decisions via AI-driven voting methods.

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PrefLib: A Library for Preferences http://www.preflib.org

Cited by 148 publications

References 13 publications

On the Discriminative Power of Tournament Solutions

On the Discriminative Power of Tournament Solutions

Elections with Few Candidates: Prices, Weights, and Covering Problems

Subset Selection Via Implicit Utilitarian Voting

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