Multi-Attribute Proportional Representation
This addresses the need for fair and representative selection in applications like committee formation, but it is incremental as it extends single-attribute apportionment methods to multiple attributes.
The paper tackles the problem of selecting a subset of items to match desired distributions across multiple attributes, such as forming a committee with specific demographic proportions. It analyzes the properties and computational complexity of selection rules for this multi-attribute proportional representation problem.
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. 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 collapses to the apportionment problem for party-list proportional representation systems (in such 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 computation complexity.