IJCAI 2023poster0 citations

Ordinal Maximin Share Approximation for Goods (Extended Abstract)

Hadi Hosseini, Andrew Searns, Erel Segal-Halevi

Abstract

In fair division of indivisible goods, l-out-of-d maximin share (MMS) is the value that an agent can guarantee by partitioning the goods into d bundles and choosing the l least preferred bundles. Most existing works aim to guarantee to all agents a constant fraction of their 1-out-of-n MMS. But this guarantee is sensitive to small perturbation in agents' cardinal valuations. We consider a more robust approximation notion, which depends only on the agents' ordinal rankings of bundles. We prove the existence of l-out-of-floor((l+1/2)n) MMS allocations of goods for any integer l greater than or equal to 1, and present a polynomial-time algorithm that finds a 1-out-of-ceiling(3n/2) MMS allocation when l = 1. We further develop an algorithm that provides a weaker ordinal approximation to MMS for any l > 1.

Game Theory and Economic Paradigms: GTEP: Fair divisionGame Theory and Economic Paradigms: GTEP: OtherAI Ethics, Trust, Fairness: ETF: Fairness and diversity
BibTeX
@inproceedings{ijcai2023p778,
  title     = {Ordinal Maximin Share Approximation for Goods (Extended Abstract)},
  author    = {Hosseini, Hadi and Searns, Andrew and Segal-Halevi, Erel},
  booktitle = {Proceedings of the Thirty-Second International Joint Conference on
               Artificial Intelligence, {IJCAI-23}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Edith Elkind},
  pages     = {6894--6899},
  year      = {2023},
  month     = {8},
  note      = {Journal Track},
  doi       = {10.24963/ijcai.2023/778},
  url       = {https://doi.org/10.24963/ijcai.2023/778},
}