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Ordinal Maximin Share Approximation for Goods

Authors :
Hosseini, Hadi
Searns, Andrew
Segal-Halevi, Erel
Source :
Journal of Artificial Intelligence Research (JAIR), 74:353-391, 2022. https://jair.org/index.php/jair/article/view/13317
Publication Year :
2021

Abstract

In fair division of indivisible goods, $\ell$-out-of-$d$ maximin share (MMS) is the value that an agent can guarantee by partitioning the goods into $d$ bundles and choosing the $\ell$ 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' \emph{ordinal} rankings of bundles. We prove the existence of $\ell$-out-of-$\lfloor(\ell+\frac{1}{2})n\rfloor$ MMS allocations of goods for any integer $\ell\geq 1$, and present a polynomial-time algorithm that finds a $1$-out-of-$\lceil\frac{3n}{2}\rceil$ MMS allocation when $\ell = 1$. We further develop an algorithm that provides a weaker ordinal approximation to MMS for any $\ell > 1$.<br />Comment: Accepted to JAIR

Details

Database :
arXiv
Journal :
Journal of Artificial Intelligence Research (JAIR), 74:353-391, 2022. https://jair.org/index.php/jair/article/view/13317
Publication Type :
Report
Accession number :
edsarx.2109.01925
Document Type :
Working Paper
Full Text :
https://doi.org/10.1613/jair.1.13317