Rank Aggregation Using Scoring Rules

Author:

Boehmer Niclas,Bredereck Robert,Peters Dominik

Abstract

To aggregate rankings into a social ranking, one can use scoring systems such as Plurality, Veto, and Borda. We distinguish three types of methods: ranking by score, ranking by repeatedly choosing a winner that we delete and rank at the top, and ranking by repeatedly choosing a loser that we delete and rank at the bottom. The latter method captures the frequently studied voting rules Single Transferable Vote (aka Instant Runoff Voting), Coombs, and Baldwin. In an experimental analysis, we show that the three types of methods produce different rankings in practice. We also provide evidence that sequentially selecting winners is most suitable to detect the "true" ranking of candidates. For different rules in our classes, we then study the (parameterized) computational complexity of deciding in which positions a given candidate can appear in the chosen ranking. As part of our analysis, we also consider the Winner Determination problem for STV, Coombs, and Baldwin and determine their complexity when there are few voters or candidates.

Publisher

Association for the Advancement of Artificial Intelligence (AAAI)

Subject

General Medicine

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

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2. Structural Fusion of Bayesian Networks with Limited Treewidth Using Genetic Algorithms;2024 IEEE Congress on Evolutionary Computation (CEC);2024-06-30

3. An Experimental Study of Unsupervised Rank Aggregation Methods in World University Rankings;2023 International Conference on Intelligent Education and Intelligent Research (IEIR);2023-11-05

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