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Leximin Allocations in the Real World

Authors:

David Kurokawa,

Ariel D. Procaccia,

Nisarg ShahAuthors Info & Claims

ACM Transactions on Economics and Computation (TEAC), Volume 6, Issue 3-4

Article No.: 11, Pages 1 - 24

https://doi.org/10.1145/3274641

Published: 23 October 2018 Publication History

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Abstract

As part of a collaboration with a major California school district, we study the problem of fairly allocating unused classrooms in public schools to charter schools. Our approach revolves around the randomized leximin mechanism. We extend previous work to show that the leximin mechanism is proportional, envy-free, Pareto optimal, and group strategyproof, not only in our classroom allocation setting, but in a general framework that subsumes a number of settings previously studied in the literature. We also prove that the leximin mechanism provides a (worst-case) 4-approximation to the maximum number of classrooms that can possibly be allocated. Our experiments, which are based on real data, show that a non-trivial implementation of the leximin mechanism scales gracefully in terms of running time (even though the problem is intractable in theory), and performs extremely well with respect to a number of efficiency objectives. We establish the practicability of our approach, and discuss issues related to its deployment.

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Leximin Allocations in the Real World

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Published In

cover image ACM Transactions on Economics and Computation

ACM Transactions on Economics and Computation Volume 6, Issue 3-4

Special Issue on EC'15

November 2018

249 pages

ISSN:2167-8375

EISSN:2167-8383

DOI:10.1145/3281297

Editors:
David Pennock
Microsoft
,
Ilya Segal
Stanford University

Issue’s Table of Contents

Copyright © 2018 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 October 2018

Accepted: 01 May 2018

Revised: 01 July 2017

Received: 01 December 2015

Published in TEAC Volume 6, Issue 3-4

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Cited By

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https://dl.acm.org/doi/10.5555/3635637.3663082
Chaudhury BGarg JMehlhorn K(2024)EFX Exists for Three AgentsJournal of the ACM10.1145/361600971:1(1-27)Online publication date: 11-Feb-2024
https://dl.acm.org/doi/10.1145/3616009
Barman SNarayan VVerma PAgmon NAn BRicci AYeoh W(2023)Fair Chore Division under Binary Supermodular CostsProceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems10.5555/3545946.3599104(2863-2865)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.5555/3545946.3599104
Roussel SPicard GPralet CMaqrot S(2023)Conflicting Bundle Allocation with Preferences in Weighted Directed Acyclic Graphs: Application to Orbit Slot Allocation ProblemsSystems10.3390/systems1106029711:6(297)Online publication date: 9-Jun-2023
https://doi.org/10.3390/systems11060297
Shah NElkind E(2023)Pushing the limits of fairness in algorithmic decision-makingProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/806(7051-7056)Online publication date: 19-Aug-2023
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Allouah AKroer CZhang XAvadhanula VBohanon NDania AGocmen CPupyrev SShah PStier-Moses NTaarup KSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Fair Allocation Over Time, with Applications to Content ModerationProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599340(25-35)Online publication date: 6-Aug-2023
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Airiau SAziz HCaragiannis IKruger JLang JPeters D(2023)Portioning using ordinal preferences: Fairness and efficiencyArtificial Intelligence10.1016/j.artint.2022.103809314(103809)Online publication date: Jan-2023
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Cousins CViswanathan VZick Y(2023)Dividing Good and Great Items Among Agents with Bivalued Submodular ValuationsWeb and Internet Economics10.1007/978-3-031-48974-7_13(225-241)Online publication date: 31-Dec-2023
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Peters DProcaccia AZhu DKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Robust rent divisionProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3601278(13864-13876)Online publication date: 28-Nov-2022
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