TY - JOUR
T1 - Ordinal Maximin Share Approximation for Goods
AU - Hosseini, Hadi
AU - Searns, Andrew
AU - Segal-Halevi, Erel
N1 - Publisher Copyright:
©2022 AI Access Foundation. All rights reserved.
PY - 2022
Y1 - 2022
N2 - In fair division of indivisible goods, `-out-of-d maximin share (MMS) is the value that an agent can guarantee by partitioning the goods into d bundles and choosing the ` 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 `-out-of-b(` + 12 )nc MMS allocations of goods for any integer ` ≥ 1, and present a polynomial-time algorithm that finds a 1-out-of-d32n e MMS allocation when ` = 1. We further develop an algorithm that provides a weaker ordinal approximation to MMS for any ` > 1.
AB - In fair division of indivisible goods, `-out-of-d maximin share (MMS) is the value that an agent can guarantee by partitioning the goods into d bundles and choosing the ` 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 `-out-of-b(` + 12 )nc MMS allocations of goods for any integer ` ≥ 1, and present a polynomial-time algorithm that finds a 1-out-of-d32n e MMS allocation when ` = 1. We further develop an algorithm that provides a weaker ordinal approximation to MMS for any ` > 1.
UR - http://www.scopus.com/inward/record.url?scp=85132023872&partnerID=8YFLogxK
U2 - 10.1613/jair.1.13317
DO - 10.1613/jair.1.13317
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AN - SCOPUS:85132023872
SN - 1076-9757
VL - 74
SP - 353
EP - 391
JO - Journal of Artificial Intelligence Research
JF - Journal of Artificial Intelligence Research
ER -