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Optimal Bounds on the Price of Fairness for Indivisible Goods

Barman, S and Bhaskar, U and Shah, N (2020) Optimal Bounds on the Price of Fairness for Indivisible Goods. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7-11 December 2020, Beijing; China, pp. 356-369.

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Official URL: https://dx.doi.org/10.1007/978-3-030-64946-3_25

Abstract

In the allocation of resources to a set of agents, how do fairness guarantees impact social welfare? A quantitative measure of this impact is the price of fairness, which measures the worst-case loss of social welfare due to fairness constraints. While initially studied for divisible goods, recent work on the price of fairness also studies the setting of indivisible goods. In this paper, we resolve the price of two well-studied fairness notions in the context of indivisible goods: envy-freeness up to one good (EF1) and approximate maximin share (MMS ). For both EF1 andwe show, via different techniques, that the price of fairness is O(n), where n is the number of agents. From previous work, it follows that these guarantees are tight. We, in fact, obtain the price-of-fairness results via efficient algorithms. Forour bound holds for additive valuations, whereas for EF1, it holds for the more general class of subadditive valuations. This resolves an open problem posed by Bei et al. (2019). © 2020, Springer Nature Switzerland AG.

Item Type: Conference Paper
Publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher: Springer Science and Business Media Deutschland GmbH
Additional Information: cited By 0; Conference of 16th International Conference on Web and Internet Economics, WINE 2020 ; Conference Date: 7 December 2020 Through 11 December 2020; Conference Code:252859
Keywords: Computer science; Computers, Fairness constraints; Fairness guarantee; General class; Indivisible good; Maximin; Optimal bounds; Quantitative measures; Social welfare, Artificial intelligence
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 08 Jan 2021 11:17
Last Modified: 08 Jan 2021 11:17
URI: http://eprints.iisc.ac.in/id/eprint/67546

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