  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.
