TY - GEN
T1 - Weighted Envy Freeness With Bounded Subsidies
AU - Elmalem, Noga Klein
AU - Gonen, Rica
AU - Segal-Halevi, Erel
N1 - Publisher Copyright:
© 2025 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org).
PY - 2025
Y1 - 2025
N2 - We explore solutions for fairly allocating indivisible items among agents assigned weights representing their entitlements. Our fairness goal is weighted-envy-freeness (WEF), where each agent deems their allocated portion relative to their entitlement at least as favorable as any other's relative to their own. In many cases, achieving WEF necessitates monetary transfers, which can be modeled as third-party subsidies. The goal is to attain WEF with bounded subsidies. Previous work in the unweighted setting of subsidies relied on basic characterizations of EF that fail in the weighted settings. This makes our new setting challenging and theoretically intriguing. We present polynomial-time algorithms that compute WEF-able allocations with an upper bound on the subsidy per agent in three distinct additive valuation scenarios: (1) general, (2) identical, and (3) binary. When all weights are equal, our bounds reduce to the bounds derived in the literature for the unweighted setting. The full version is available at [20].
AB - We explore solutions for fairly allocating indivisible items among agents assigned weights representing their entitlements. Our fairness goal is weighted-envy-freeness (WEF), where each agent deems their allocated portion relative to their entitlement at least as favorable as any other's relative to their own. In many cases, achieving WEF necessitates monetary transfers, which can be modeled as third-party subsidies. The goal is to attain WEF with bounded subsidies. Previous work in the unweighted setting of subsidies relied on basic characterizations of EF that fail in the weighted settings. This makes our new setting challenging and theoretically intriguing. We present polynomial-time algorithms that compute WEF-able allocations with an upper bound on the subsidy per agent in three distinct additive valuation scenarios: (1) general, (2) identical, and (3) binary. When all weights are equal, our bounds reduce to the bounds derived in the literature for the unweighted setting. The full version is available at [20].
KW - Entitlements
KW - Envy-Freeness
UR - https://www.scopus.com/pages/publications/105009818478
M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???
AN - SCOPUS:105009818478
T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
SP - 2504
EP - 2506
BT - Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2025
A2 - Vorobeychik, Yevgeniy
A2 - Das, Sanmay
A2 - Nowe, Ann
T2 - 24th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2025
Y2 - 19 May 2025 through 23 May 2025
ER -