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

翻译：现实世界中公平分配算法的部署通常涉及异质用户群体，这使得用户除了自身获得的物品外，难以完全了解分配情况。Chan等人[IJCAI 2019]提出了一种新的公平性概念——最大化认知公平（MMA），该概念确保无论未分配给某代理人的物品如何分配，该代理人均不会成为处境最差者。我们将这一概念适配并推广至不可分家务场景，并允许代理具有任意权重。鉴于MMA的内在难度，我们还考虑了其"至多一个"和"至多任意"的松弛版本。本文给出了关于MMA相关公平分配的存在性、计算性及其与现有公平性概念联系的一系列结果。