Auctions between Regret-Minimizing Agents

Yoav Kolumbus, Noam Nisan

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


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
Number of pages12
ISBN (Electronic)9781450390965
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


Conference31st ACM World Wide Web Conference, WWW 2022
CityVirtual, Online


  • Auctions
  • Regret Minimization
  • Repeated Games

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software


Dive into the research topics of 'Auctions between Regret-Minimizing Agents'. Together they form a unique fingerprint.

Cite this