Multi-attribute proportional representation
Lang, Jérôme; Skowron, Piotr (2018), Multi-attribute proportional representation, Artificial Intelligence, 263, p. 74-106. 10.1016/j.artint.2018.07.005
TypeArticle accepté pour publication ou publié
Journal nameArtificial Intelligence
MetadataShow full item record
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Abstract (EN)We consider the following problem in which a given number of items has to be chosen from a predefined set. Each item is described by a vector of attributes and for each attribute there is a desired distribution that the selected set should have. We look for a set that fits as much as possible the desired distributions on all attributes. An example of application is the choice of members for a representative committee, where candidates are described by attributes such as gender, age and profession, and where we look for a committee that for each attribute offers a certain representation, i.e., a single committee that contains a certain number of young and old people, certain number of men and women, certain number of people with different professions, etc. Another example of application is the selection of a common set of items to be used by a group of users, where items are labelled by attribute values. With a single attribute the problem collapses to the apportionment problem for party-list proportional representation systems (in such a case the value of the single attribute would be a political affiliation of a candidate). We study the properties of the associated subset selection rules, as well as their computational complexity.
Subjects / KeywordsProportional representation; Diversity; Multiwinner elections; Apportionment; Recommendation systems; Algorithms; Computational complexity; Approximation algorithms
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