Authors:
Emanuele D’ajello
1
;
Davide Formica
2
;
Elio Masciari
1
;
Gaia Mattia
1
;
Arianna Anniciello
1
;
Cristina Moscariello
1
;
Stefano Quintarelli
1
and
Davide Zaccarella
1
Affiliations:
1
University of Napoli Federico II, Napoli, Italy
;
2
Copernicani, Milano, Italy
Keyword(s):
Decision Making, Social Choice, Cluster, Majority Judgement, K-Medoids.
Abstract:
In order to make a decision process that can best represent the will of a group of people who express themselves about something, like the election of a president or any other situation where people make judgements about more than two possibilities, this paper wants to propose the usage of unsupervised learning techniques, in particular cluster techniques, to extend a single-winner voting system Majority Judgement to a multi-winner system which aggregate the preferences of subsets of voters. After an introduction about Majority Judgement, the algorithm used for its clustered version is presented. In the end, a case study will be reported to highlight the differences with the classic Majority Judgment, since sometimes it could be preferable based on the contingencies of the particular election, especially when there is a desire not to neglect minority groups with the same preferences.