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The real-world deployment of fair allocation algorithms usually involves aheterogeneous population of users, which makes it challenging for the users toget complete knowledge of the allocation except for their own bundles. Chan etal. [IJCAI 2019] proposed a new fairness notion, maximin-awareness (MMA), whichguarantees that every agent is not the worst-off one, no matter how the itemsthat are not allocated to her are distributed. We adapt and generalize thisnotion to the case of indivisible chores and when the agents may have arbitraryweights. Due to the inherent difficulty of MMA, we also consider its up to oneand up to any relaxations. A string of results on the existence and computationof MMA related fair allocations, and their connections to existing fairnessconcepts is given.

Maximin-Aware Allocations of Indivisible Chores with Symmetric and Asymmetric Agents