Desirable Tie-breaking Rules in Collective Decision Making

Pigozzi, Gabriella; Slavkovik, Marija; van der Torre, Leon

Reference : Desirable Tie-breaking Rules in Collective Decision Making


	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a journal
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/25054

Title :	Desirable Tie-breaking Rules in Collective Decision Making
Language :	English
Author, co-author :	Pigozzi, Gabriella [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Slavkovik, Marija [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	van der Torre, Leon [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	2008
Journal title :	Annales du LAMSADE
Pages :	171–174
Peer reviewed :	Yes
Audience :	International
Event name :	Annales du Lamsade, Proceedings of the DIMACS-LAMSADE Workshop on Algorithmic Decision Theory
Event date :	2008
Keywords :	[en] Judgment aggregation, ; group decision-making
Abstract :	[en] Judgment aggregation (JA) studies how to aggregate individual judgments to form collective decisions. Examples are expert panels, legal courts, boards, and councils. The problems investigated in this new field are relevant and common to many situations. Nevertheless, the existing procedures are idealized and, likewise the related problems of preference aggregation in social choice theory, the field is plagued by impossibility theorems. In this paper, we extend standard JA in order to offer a more realistic framework and to escape the impossibility results. We propose to distinguish between abstainers and neutral judgment as well as to model the notion of confidence a group member may have in the decision rule by assigning to each criterion a normalized weight. We then show how this new framework may help us to avoid indecision in most cases.
Target :	Researchers ; Professionals ; Students ; General public ; Others
Permalink :	http://hdl.handle.net/10993/25054
Other URL :	http://www.pigozzi.org/DIMACS_Benamara_Kaci_Pigozzi.pdf

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