Keep Your Distance: Land Division With Separation

Keep Your Distance: Land Division With Separation

Edith Elkind, Erel Segal-Halevi, Warut Suksompong

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Main Track. Pages 168-174. https://doi.org/10.24963/ijcai.2021/24

This paper is part of an ongoing endeavor to bring the theory of fair division closer to practice by handling requirements from real-life applications. We focus on two requirements originating from the division of land estates: (1) each agent should receive a plot of a usable geometric shape, and (2) plots of different agents must be physically separated. With these requirements, the classic fairness notion of proportionality is impractical, since it may be impossible to attain any multiplicative approximation of it. In contrast, the ordinal maximin share approximation, introduced by Budish in 2011, provides meaningful fairness guarantees. We prove upper and lower bounds on achievable maximin share guarantees when the usable shapes are squares, fat rectangles, or arbitrary axes-aligned rectangles, and explore the algorithmic and query complexity of finding fair partitions in this setting.
Keywords:
Agent-based and Multi-agent Systems: Resource Allocation
Agent-based and Multi-agent Systems: Computational Social Choice