# Rank Aggregation Using Scoring Rules

arXiv: https://arxiv.org/abs/2209.08856 · HTML: https://mlsanity.com/paper/2209.08856/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2022.

## Built on by (most cited first, 2 of 2)

- [SP-Rank: A Dataset for Ranked Preferences with Secondary Information](https://mlsanity.com/paper/2601.05253/) (2025)
- [Structural Fusion of Bayesian Networks with Limited Treewidth Using Genetic Algorithms](https://mlsanity.com/paper/2609.10276/) (2026)

## Abstract (arXiv)

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.