mlsanityData to 7 Oct 2026

Weighted Notions of Fairness with Binary Supermodular Chores

arXiv 2303.06212 ↗First posted 10 Mar 2023cs.GT cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

We study the problem of allocating indivisible chores among agents with binary supermodular cost functions. In other words, each chore has a marginal cost of 0 or 1 and chores exhibit increasing marginal costs (or decreasing marginal utilities). In this note, we combine the techniques of Viswanathan and Zick (2022) and Barman et al. (2023) to present a general framework for fair allocation with this class of valuation functions. Our framework allows us to generalize the results of Barman et al. (2023) and efficiently compute allocations which satisfy weighted notions of fairness like weighted leximin or min weighted p-mean malfare for any p ≥ 1.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 2 of its 6 references that are in the map. First posted on arXiv 10 Mar 2023.

Builds on

2 of 6 references.

References · 2

  1. 2021An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learningcited by 18
  2. 2023Fair Chore Division under Binary Supermodular Costscited by 2

Built on by

Not cited yet.