Multi-Attribute Proportional Representation

Abstract

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 fit. We look for a set that fits as much as possible the desired distributions on all attributes. Examples of applications include choosing members of a representative committee, where candidates are described by attributes such as sex, 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. With a single attribute the problem boils down to the apportionment problem for party-list proportional representation systems (in such case the value of the single attribute is the political affiliation of a candidate). We study some properties of the associated subset selection rules, and address their computation.

Cite

Text

Lang and Skowron. "Multi-Attribute Proportional Representation." AAAI Conference on Artificial Intelligence, 2016. doi:10.1609/AAAI.V30I1.10024

Markdown

[Lang and Skowron. "Multi-Attribute Proportional Representation." AAAI Conference on Artificial Intelligence, 2016.](https://mlanthology.org/aaai/2016/lang2016aaai-multi/) doi:10.1609/AAAI.V30I1.10024

BibTeX

@inproceedings{lang2016aaai-multi,
  title     = {{Multi-Attribute Proportional Representation}},
  author    = {Lang, Jérôme and Skowron, Piotr Krzysztof},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2016},
  pages     = {530-536},
  doi       = {10.1609/AAAI.V30I1.10024},
  url       = {https://mlanthology.org/aaai/2016/lang2016aaai-multi/}
}