Auctions between Regret-Minimizing Agents

Yoav Kolumbus, Noam Nisan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

We analyze a scenario in which software agents implemented as regret-minimizing algorithms engage in a repeated auction on behalf of their users. We study first-price and second-price auctions, as well as their generalized versions (e.g., as those used for ad auctions). Using both theoretical analysis and simulations, we show that, surprisingly, in second-price auctions the players have incentives to misreport their true valuations to their own learning agents, while in first-price auctions it is a dominant strategy for all players to truthfully report their valuations to their agents.

Original languageAmerican English
Title of host publicationWWW '22
Subtitle of host publicationProceedings of the ACM Web Conference 2022
PublisherAssociation for Computing Machinery, Inc
Pages100-111
Number of pages12
ISBN (Electronic)9781450390965
DOIs
StatePublished - 25 Apr 2022
Event31st ACM World Wide Web Conference, WWW 2022 - Virtual, Online, France
Duration: 25 Apr 202229 Apr 2022

Publication series

NameWWW 2022 - Proceedings of the ACM Web Conference 2022

Conference

Conference31st ACM World Wide Web Conference, WWW 2022
Country/TerritoryFrance
CityVirtual, Online
Period25/04/2229/04/22

Bibliographical note

Funding Information:
This work was supported in part by Science and Technology Innovation 2030 –“New Generation Artificial Intelligence” Major Project No. 2018AAA0100905, in part by China NSF grant No. 61902248, 62025204, 62072303, 61972252 and 61972254, and in part by Alibaba Group through Alibaba Innovation Research Program, and in part by Shanghai Science and Technology fund 20PJ1407900. The opinions, findings, conclusions, and recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the funding agencies or the government. *Zhenzhe Zheng is the corresponding author.

Publisher Copyright:
© 2022 ACM.

Keywords

  • Auctions
  • Regret Minimization
  • Repeated Games

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