Keep Your Distance: Land Division With Separation
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 168-174.
https://doi.org/10.24963/ijcai.2021/24
Keywords
- Agent-based and Multi-agent Systems: Resource Allocation
- Agent-based and Multi-agent Systems: Computational Social Choice
Abstract
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.
@inproceedings{ijcai2021p24,
title = {Keep Your Distance: Land Division With Separation},
author = {Elkind, Edith and Segal-Halevi, Erel and Suksompong, Warut},
booktitle = {Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, {IJCAI-21}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Prof. Zhi-Hua Zhou},
pages = {168--174},
year = {2021},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2021/24},
url = {https://doi.org/10.24963/ijcai.2021/24},
}
TY - CPAPER TI - Keep Your Distance: Land Division With Separation AU - Elkind, Edith AU - Segal-Halevi, Erel AU - Suksompong, Warut T2 - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2021 SP - 168 EP - 174 DO - 10.24963/ijcai.2021/24 UR - https://doi.org/10.24963/ijcai.2021/24 ER -Copied
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