Auctions between Regret-Minimizing Agents

Yoav Kolumbus, Noam Nisan

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

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
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

Keywords

  • Auctions
  • Regret Minimization
  • Repeated Games

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

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