Rank Aggregation Using Scoring Rules


  • Niclas Boehmer Algorithmics and Computational Complexity, Technische Universität Berlin
  • Robert Bredereck Institut für Informatik, TU Clausthal
  • Dominik Peters LAMSADE, Université Paris Dauphine-PSL




GTEP: Social Choice / Voting


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.




How to Cite

Boehmer, N., Bredereck, R., & Peters, D. (2023). Rank Aggregation Using Scoring Rules. Proceedings of the AAAI Conference on Artificial Intelligence, 37(5), 5515-5523. https://doi.org/10.1609/aaai.v37i5.25685



AAAI Technical Track on Game Theory and Economic Paradigms