Apportionment with Weighted Seats

Open Access
Authors
Publication date 2025
Host editors
  • InĂªs Lynce
  • Nello Murano
  • Mauro Vallati
  • Serena Villata
  • Federico Chesani
  • Michela Milano
  • Andrea Omicini
  • Mehdi Dastani
Book title ECAI 2025
Book subtitle 28th European Conference on Artificial Intelligence, 25-30 October2025, Bologna, Italy : including 14th Conference on Prestigious Applications of Intelligent Systems (PAIS 2025) : proceedings
ISBN (electronic)
  • 9781643686318
Series Frontiers in Artificial Intelligence and Applications
Event 28th European Conference on Artificial Intelligence, ECAI 2025, including 14th Conference on Prestigious Applications of Intelligent Systems, PAIS 2025
Pages (from-to) 3831-3838
Number of pages 8
Publisher Amsterdam: IOS Press
Organisations
  • Interfacultary Research - Institute for Logic, Language and Computation (ILLC)
Abstract

Apportionment is the task of assigning resources to entities with different entitlements in a fair manner, and specifically a manner that is as proportional as possible. The best-known application is the assignment of parliamentary seats to political parties based on their share in the popular vote. Here we enrich the standard model of apportionment by associating each seat with a weight representing the (objective) value of that seat. A seat's weight reflects the fact that different seats might come with different roles, such as chair or treasurer. We define several apportionment methods and natural fairness requirements for this new setting, and we study the extent to which our methods satisfy these requirements. Our findings show that full fairness is harder to achieve than in the standard apportionment setting. Yet, for several natural relaxations of those requirements we can achieve stronger results than in the more expressive model of fair division with entitlements, where the values of objects are subjective.

Document type Conference contribution
Language English
Published at https://doi.org/10.3233/FAIA251265
Other links https://www.scopus.com/pages/publications/105024447181
Downloads
FAIA-413-FAIA251265 (Final published version)
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