Auctions between Regret-Minimizing Agents

Yoav Kolumbus; Noam Nisan

doi:10.1145/3485447.3512055

Auctions between Regret-Minimizing Agents

Yoav Kolumbus, Noam Nisan

The Rachel and Selim Benin School of Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 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 language	English
Title of host publication	WWW '22
Subtitle of host publication	Proceedings of the ACM Web Conference 2022
Publisher	Association for Computing Machinery, Inc
Pages	100-111
Number of pages	12
ISBN (Electronic)	9781450390965
DOIs	https://doi.org/10.1145/3485447.3512055
State	Published - 25 Apr 2022
Event	31st ACM World Wide Web Conference, WWW 2022 - Virtual, Online, France Duration: 25 Apr 2022 → 29 Apr 2022

Publication series

Name	WWW 2022 - Proceedings of the ACM Web Conference 2022

Conference

Conference	31st ACM World Wide Web Conference, WWW 2022
Country/Territory	France
City	Virtual, Online
Period	25/04/22 → 29/04/22

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

Access to Document

10.1145/3485447.3512055

Cite this

@inproceedings{56d4061408c64518afd8b16923f43fed,

title = "Auctions between Regret-Minimizing Agents",

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.",

keywords = "Auctions, Regret Minimization, Repeated Games",

author = "Yoav Kolumbus and Noam Nisan",

note = "Publisher Copyright: {\textcopyright} 2022 ACM.; 31st ACM World Wide Web Conference, WWW 2022 ; Conference date: 25-04-2022 Through 29-04-2022",

year = "2022",

month = apr,

day = "25",

doi = "10.1145/3485447.3512055",

language = "אנגלית",

series = "WWW 2022 - Proceedings of the ACM Web Conference 2022",

publisher = "Association for Computing Machinery, Inc",

pages = "100--111",

booktitle = "WWW '22",

}

TY - GEN

T1 - Auctions between Regret-Minimizing Agents

AU - Kolumbus, Yoav

AU - Nisan, Noam

PY - 2022/4/25

Y1 - 2022/4/25

N2 - 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.

AB - 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.

KW - Auctions

KW - Regret Minimization

KW - Repeated Games

UR - http://www.scopus.com/inward/record.url?scp=85126645279&partnerID=8YFLogxK

U2 - 10.1145/3485447.3512055

DO - 10.1145/3485447.3512055

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

AN - SCOPUS:85126645279

T3 - WWW 2022 - Proceedings of the ACM Web Conference 2022

SP - 100

EP - 111

BT - WWW '22

PB - Association for Computing Machinery, Inc

T2 - 31st ACM World Wide Web Conference, WWW 2022

Y2 - 25 April 2022 through 29 April 2022

ER -

Auctions between Regret-Minimizing Agents

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this