Ordinal Maximin Share Approximation for Goods
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-⌊(ℓ + 1/2)n⌋ MMS allocations of goods for any integer ℓ ≥ 1, and present a polynomial-time algorithm that finds a 1-out-of-⌈3n/2⌉ MMS allocation when ℓ=1. We further develop an algorithm that provides a weaker ordinal approximation to MMS for any ℓ > 1.
Cite
Text
Hosseini et al. "Ordinal Maximin Share Approximation for Goods." Journal of Artificial Intelligence Research, 2022. doi:10.1613/JAIR.1.13317Markdown
[Hosseini et al. "Ordinal Maximin Share Approximation for Goods." Journal of Artificial Intelligence Research, 2022.](https://mlanthology.org/jair/2022/hosseini2022jair-ordinal/) doi:10.1613/JAIR.1.13317BibTeX
@article{hosseini2022jair-ordinal,
title = {{Ordinal Maximin Share Approximation for Goods}},
author = {Hosseini, Hadi and Searns, Andrew and Segal-Halevi, Erel},
journal = {Journal of Artificial Intelligence Research},
year = {2022},
doi = {10.1613/JAIR.1.13317},
volume = {74},
url = {https://mlanthology.org/jair/2022/hosseini2022jair-ordinal/}
}