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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Fair Division Through Information Withholding

February 1, 2023

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Authors

Hadi Hosseini

Rochester Institute of Technology


Sujoy Sikdar

Washington University St. Louis


Rohit Vaish

Rensselaer Polytechnic Institute


Hejun Wang

Rensselaer Polytechnic Institute


Lirong Xia

Rensselaer Polytechnic Institute


DOI:

10.1609/aaai.v34i02.5573


Abstract:

Envy-freeness up to one good (EF1) is a well-studied fairness notion for indivisible goods that addresses pairwise envy by the removal of at most one good. In the worst case, each pair of agents might require the (hypothetical) removal of a different good, resulting in a weak aggregate guarantee. We study allocations that are nearly envy-free in aggregate, and define a novel fairness notion based on information withholding. Under this notion, an agent can withhold (or hide) some of the goods in its bundle and reveal the remaining goods to the other agents. We observe that in practice, envy-freeness can be achieved by withholding only a small number of goods overall. We show that finding allocations that withhold an optimal number of goods is computationally hard even for highly restricted classes of valuations. In contrast to the worst-case results, our experiments on synthetic and real-world preference data show that existing algorithms for finding EF1 allocations withhold a close-to-optimal amount of information.

Topics: AAAI

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HOW TO CITE:

Hadi Hosseini||Sujoy Sikdar||Rohit Vaish||Hejun Wang||Lirong Xia Fair Division Through Information Withholding Proceedings of the AAAI Conference on Artificial Intelligence (2020) 2014-2021.

Hadi Hosseini||Sujoy Sikdar||Rohit Vaish||Hejun Wang||Lirong Xia Fair Division Through Information Withholding AAAI 2020, 2014-2021.

Hadi Hosseini||Sujoy Sikdar||Rohit Vaish||Hejun Wang||Lirong Xia (2020). Fair Division Through Information Withholding. Proceedings of the AAAI Conference on Artificial Intelligence, 2014-2021.

Hadi Hosseini||Sujoy Sikdar||Rohit Vaish||Hejun Wang||Lirong Xia. Fair Division Through Information Withholding. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.2014-2021.

Hadi Hosseini||Sujoy Sikdar||Rohit Vaish||Hejun Wang||Lirong Xia. 2020. Fair Division Through Information Withholding. "Proceedings of the AAAI Conference on Artificial Intelligence". 2014-2021.

Hadi Hosseini||Sujoy Sikdar||Rohit Vaish||Hejun Wang||Lirong Xia. (2020) "Fair Division Through Information Withholding", Proceedings of the AAAI Conference on Artificial Intelligence, p.2014-2021

Hadi Hosseini||Sujoy Sikdar||Rohit Vaish||Hejun Wang||Lirong Xia, "Fair Division Through Information Withholding", AAAI, p.2014-2021, 2020.

Hadi Hosseini||Sujoy Sikdar||Rohit Vaish||Hejun Wang||Lirong Xia. "Fair Division Through Information Withholding". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.2014-2021.

Hadi Hosseini||Sujoy Sikdar||Rohit Vaish||Hejun Wang||Lirong Xia. "Fair Division Through Information Withholding". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 2014-2021.

Hadi Hosseini||Sujoy Sikdar||Rohit Vaish||Hejun Wang||Lirong Xia. Fair Division Through Information Withholding. AAAI[Internet]. 2020[cited 2023]; 2014-2021.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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