Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/105477
PIRA download icon_1.1View/Download Full Text
Title: Budget-feasible maximum Nash social welfare is almost envy-free
Authors: Wu, X
Li, B 
Gan, J
Issue Date: 2021
Source: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, Montreal-themed Virtual Reality, 19th-26th August, 2021, p. 465-471
Abstract: The Nash social welfare (NSW) is a well-known social welfare measurement that balances individual utilities and the overall efficiency. In the context of fair allocation of indivisible goods, it has been shown by Caragiannis et al. (EC 2016 and TEAC 2019) that an allocation maximizing the NSW is envy-free up to one good (EF1). In this paper, we are interested in the fairness of the NSW in a budget-feasible allocation problem, in which each item has a cost that will be incurred to the agent it is allocated to, and each agent has a budget constraint on the total cost of items she receives. We show that a budget-feasible allocation that maximizes the NSW achieves a 1/4-approximation of EF1 and the approximation ratio is tight. The approximation ratio improves gracefully when the items have small costs compared with the agents' budgets; it converges to 1/2 when the budget-cost ratio approaches infinity.
Publisher: International Joint Conferences on Artificial Intelligence
ISBN: 978-0-9992411-9-6 (Online)
DOI: 10.24963/ijcai.2021/65
Rights: Copyright © 2021 International Joint Conferences on Artificial Intelligence
All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher.
Posted with permission of the IJCAI Organization (https://www.ijcai.org/).
The following publication Wu, X., Li, B., & Gan, J.(2021). Budget-feasible maximum nash social welfare is almost envy-free. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, Montreal-themed Virtual Reality, 19th-26th August, 2021, p. 465-471. IJCAL, 2021 is available at https://doi.org/10.24963/ijcai.2021/65.
Appears in Collections:Conference Paper

Files in This Item:
File Description SizeFormat 
0065.pdf185.69 kBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

104
Last Week
4
Last month
Citations as of Nov 9, 2025

Downloads

19
Citations as of Nov 9, 2025

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.