2015/02/28 by Sonia Amodio, Antonio D’Ambrosio, Antonio D'Ambrosio +1 · 69 citations
Computer Science · Decision Sciences · Economics, Econometrics and Finance · Mathematics · #Aggregate (composite) #Aggregation problem #Algorithm #Artificial intelligence #Axiom #Computer science #Field (mathematics) #Game Theory and Voting Systems #Mathematical economics #Mathematics #Multi-Criteria Decision Making #Preference #Ranking (information retrieval) #Rough Sets and Fuzzy Logic #Social choice theory #Statistics #acm:62C99 #acm:90B50 #acm:91B06 #msc:62C99 #msc:90B50 #msc:91B06 #stat.CO
paper · pdf · doi:10.1016/j.ejor.2015.08.048
published in European Journal of Operational Research 249(2), 667-676 (Elsevier BV)
arxiv created 2015/09/03 · arxiv updated 2015/09/04 · openalex publication_date 2015/09/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Preference rankings virtually appear in all field of science (political sciences, behavioral sciences, machine learning, decision making and so on). The well-know social choice problem consists in trying to find a reasonable procedure to use the aggregate preferences expressed by subjects (usually called judges) to reach a collective decision. This problem turns out to be equivalent to the problem of estimating the consensus (central) ranking from data that is known to be a NP-hard Problem. Emond and Mason in 2002 proposed a branch and bound algorithm to calculate the consensus ranking given n rankings expressed on m objects. Depending on the complexity of the problem, there can be multiple solutions and then the consensus ranking may be not unique. We propose a new algorithm to find the consensus ranking that is equivalent to Emond and Mason's algorithm in terms of at least one of the solutions reached, but permits a really remarkable saving in computational time.