Weighted EF1 allocations for indivisible chores

Xiaowei Wu, Cong Zhang, Shengwei Zhou

Abstract: We study how to fairly allocate a set of indivisible chores to a group of agents, where each agent i has a non-negative weight wi that represents her obligation for undertaking the chores. We consider the fairness notion of weighted envy-freeness up to one item (WEF1) and propose an efficient picking sequence algorithm for computing WEF1 allocations. Our analysis is based on a natural and powerful continuous interpretation for the picking sequence algorithms in the weighted setting, which might be of independent interest. Using this interpretation, we establish the necessary and sufficient conditions under which picking sequence algorithms can guarantee other fairness notions in the weighted setting. We also study the best-of-both-worlds setting and propose a lottery that guarantees ex-ante WEF and ex-post WEF(1,1). Then we study the existence of fair and efficient allocations and propose efficient algorithms for computing WEF1 and PO allocations for bi-valued instances. Our result generalizes that of Garg et al. (AAAI 2022) and Ebadian et al. (AAMAS 2022) to the weighted setting. Our work also studies the price of fairness for WEF1, and the implications of WEF1 to other fairness notions.