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Representation Matters: Characterisation and Impossibility Results for Interval Aggregation

Endriss, Ulle; Novaro, Arianna; Terzopoulou, Zoi (2022), Representation Matters: Characterisation and Impossibility Results for Interval Aggregation, Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI 2022), IJCAI : Vienne, p. 286-292. 10.24963/ijcai.2022/41

Type
Communication / Conférence
Lien vers un document non conservé dans cette base
https://www.ijcai.org/proceedings/2022/41
Date
2022
Titre du colloque
31st International Joint Conference on Artificial Intelligence (IJCAI-22)
Date du colloque
2022-07
Ville du colloque
Vienne
Pays du colloque
Austria
Titre de l'ouvrage
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI 2022)
Éditeur
IJCAI
Ville d’édition
Vienne
Isbn
978-1-956792-00-3
Nombre de pages
5959
Pages
286-292
Identifiant publication
Métadonnées
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Auteur(s)
Endriss, Ulle
Institute for Logic, Language and Computation [ILLC]
Novaro, Arianna
Centre d'économie de la Sorbonne [CES]
Terzopoulou, Zoi cc
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Résumé (EN)
In the context of aggregating intervals reflecting the views of several agents into a single interval, we investigate the impact of the form of representation chosen for the intervals involved. Specifically, we ask whether there are natural rules we can define both as rules that aggregate separately the left and right endpoints of intervals and as rules that aggregate separately the left endpoints and the interval widths. We show that on discrete scales it is essentially impossible to do so, while on continuous scales we can characterise the rules meeting these requirements as those that compute a weighted average of the endpoints of the individual intervals.
Mots-clés
Agent-based and Multi-agent Systems; Computational Social Choice