Clustered Majority Judgement

Emanuele d’Ajello, Davide Formica, Elio Masciari, Gaia Mattia, Arianna Anniciello, Cristina Moscariello, Stefano Quintarelli, Davide Zaccarella

2022

Abstract

In order to overcome the classical methods of judgement, in the literature there is a lot of material about different methodology and their intrinsic limitations. One of the most relevant modern model to deal with votation system dynamics is the Majority Judgement. It was created with the aim of reducing polarization of the electorate in modern democracies and not to alienate minorities, thanks to its use of a highest median rule, producing more informative results than the existing alternatives. Nonetheless, as shown in the literature, in the case of multiwinner elections it can lead to scenarios in which minorities, albeit numerous, are not adequately represented. For this reason our aim is to implement a clustered version of this algorithm, in order to mitigate these disadvantages: it creates clusters taking into account the similarity between the expressed judgements and then for, each of these created groups, Majority Judgement rule is applied to return a ranking over the set of candidates. These traits make the algorithm available for applications in different areas of interest in which a decisional process is involved.

Paper Citation

in Harvard Style

d’Ajello E., Formica D., Masciari E., Mattia G., Anniciello A., Moscariello C., Quintarelli S. and Zaccarella D. (2022). Clustered Majority Judgement. In Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-583-8, pages 512-519. DOI: 10.5220/0011319400003269

in Bibtex Style

@conference{data22,
author={Emanuele d’Ajello and Davide Formica and Elio Masciari and Gaia Mattia and Arianna Anniciello and Cristina Moscariello and Stefano Quintarelli and Davide Zaccarella},
title={Clustered Majority Judgement},
booktitle={Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2022},
pages={512-519},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011319400003269},
isbn={978-989-758-583-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Clustered Majority Judgement
SN - 978-989-758-583-8
AU - d’Ajello E.
AU - Formica D.
AU - Masciari E.
AU - Mattia G.
AU - Anniciello A.
AU - Moscariello C.
AU - Quintarelli S.
AU - Zaccarella D.
PY - 2022
SP - 512
EP - 519
DO - 10.5220/0011319400003269