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research article

Regret Minimization and Separation in Multi-Bidder Multi-Item Auctions

Kocyigit, Cagil  
•
Kuhn, Daniel  
•
Rujeerapaiboon, Napat
March 4, 2024
INFORMS Journal on Computing

We study a robust auction design problem with a minimax regret objective, where a seller seeks a mechanism for selling multiple items to multiple anonymous bidders with additive values. The seller knows that the bidders' values range over a box uncertainty set but has no information about their probability distribution. This auction design problem can be viewed as a zero-sum game between the seller, who chooses a mechanism, and a fictitious adversary or `nature,' who chooses the bidders' values from within the uncertainty set with the aim to maximize the seller's regret. We characterize the Nash equilibrium of this game analytically. The Nash strategy of the seller is a mechanism that sells each item via a separate auction akin to a second price auction with a random reserve price. The Nash strategy of nature is mixed and constitutes a probability distribution on the uncertainty set under which each bidder's values for the items are comonotonic.

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Type
research article
DOI
10.1287/ijoc.2022.0275
Author(s)
Kocyigit, Cagil  

University of Luxembourg

Kuhn, Daniel  

EPFL

Rujeerapaiboon, Napat

National University of Singapore

Date Issued

2024-03-04

Published in
INFORMS Journal on Computing
Volume

36

Issue

6

Start page

1543

End page

1561

Subjects

Auction

•

Mechanism design

•

Robust optimization

Note

Available from Optimization Online

URL

View record in Optimization Online

http://www.optimization-online.org/DB_HTML/2020/06/7863.html
Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
RAO  
RelationRelated workURL/DOI

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[Software] 2022.0275 - Regret Minimization and Separation in Multi-Bidder Multi-Item Auctions

https://github.com/INFORMSJoC/2022.0275
Available on Infoscience
December 30, 2024
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/169707.2
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