Skip to main navigation Skip to search Skip to main content

A QPTAS For Up-To-ϵ Revenue Maximization With Multiple Constant-Demand Bidders Over Independent Items

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We study revenue maximization in multi-dimensional auctions with n bidders and m items. When the bidders are constant-demand and either the number of bidders or the number of items is a constant, we give a quasi-polynomial time algorithm that computes an ϵ-Bayesian Incentive Compatible (ϵ-BIC) mechanism that obtains at least a (1 - ϵ) faction of the expected revenue of the optimal Bayesian Incentive Compatible (BIC) mechanism. We obtain this guarantee even when the value distribution of each bidder is unbounded, extending the main result of [Kothari et al., 2019] from a single bidder to multiple bidders.

Original languageEnglish (US)
Title of host publicationEC 2025 - Proceedings of the 26th ACM Conference on Economics and Computation
PublisherAssociation for Computing Machinery, Inc
Pages946-974
Number of pages29
ISBN (Electronic)9798400719431
DOIs
StatePublished - Jul 2 2025
Event26th ACM Conference on Economics and Computation, EC 2025 - Stanford, United States
Duration: Jul 7 2025Jul 10 2025

Publication series

NameEC 2025 - Proceedings of the 26th ACM Conference on Economics and Computation

Conference

Conference26th ACM Conference on Economics and Computation, EC 2025
Country/TerritoryUnited States
CityStanford
Period7/7/257/10/25

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Computer Science (miscellaneous)
  • Economics and Econometrics
  • Computational Mathematics

Keywords

  • approximation schemes
  • mechanism design
  • revenue maximization

Fingerprint

Dive into the research topics of 'A QPTAS For Up-To-ϵ Revenue Maximization With Multiple Constant-Demand Bidders Over Independent Items'. Together they form a unique fingerprint.

Cite this