Decision Making with Clustered Majority Judgment

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

2022

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.

Paper Citation

in Harvard Style

D’ajello E., Formica D., Masciari E., Mattia G., Anniciello A., Moscariello C., Quintarelli S. and Zaccarella D. (2022). Decision Making with Clustered Majority Judgment. In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 3: KMIS; ISBN 978-989-758-614-9, SciTePress, pages 134-140. DOI: 10.5220/0011524600003335

in Bibtex Style

@conference{kmis22,
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={Decision Making with Clustered Majority Judgment},
booktitle={Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 3: KMIS},
year={2022},
pages={134-140},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011524600003335},
isbn={978-989-758-614-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 3: KMIS
TI - Decision Making with Clustered Majority Judgment
SN - 978-989-758-614-9
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 - 134
EP - 140
DO - 10.5220/0011524600003335
PB - SciTePress