Multi-attribute proportional representation

作者:

摘要

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.

论文关键词:Proportional representation,Diversity,Multiwinner elections,Apportionment,Recommendation systems,Algorithms,Computational complexity,Approximation algorithms

论文评审过程:Received 10 January 2017, Revised 16 July 2018, Accepted 23 July 2018, Available online 25 July 2018, Version of Record 31 July 2018.

论文官网地址:https://doi.org/10.1016/j.artint.2018.07.005