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Robust Repeated First Price Auctions

Authors:

Shipra Agrawal,

Eric Balkanski,

Vahab Mirrokni,

Balasubramanian SivanAuthors Info & Claims

EC '21: Proceedings of the 22nd ACM Conference on Economics and Computation

Page 4

https://doi.org/10.1145/3465456.3467590

Published: 18 July 2021 Publication History

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Abstract

We study dynamic mechanisms for optimizing revenue in repeated auctions, that are robust to heterogeneous forward-looking and learning behavior of the buyers. Typically it is assumed that the buyers are either all myopic or are all infinite lookahead, and that buyers understand and trust the mechanism. These assumptions raise the following question: is it possible to design approximately revenue optimal mechanisms when the buyer pool is heterogeneous? We provide this fresh perspective on the problem by considering a heterogeneous population of buyers with an unknown mixture of k-lookahead buyers, myopic buyers, no-regret-learners and no-policy-regret learners. Facing this population, we design a simple state-based mechanism that achieves a constant fraction of the optimal achievable revenue.

Index Terms

Robust Repeated First Price Auctions
1. Theory of computation
  1. Theory and algorithms for application domains
    1. Algorithmic game theory and mechanism design
      1. Algorithmic mechanism design
      2. Computational pricing and auctions

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Information & Contributors

Information

Published In

EC '21: Proceedings of the 22nd ACM Conference on Economics and Computation

July 2021

950 pages

ISBN:9781450385541

DOI:10.1145/3465456

General Chair:
Péter Biró
Hungarian Academy of Sciences
,
Program Chairs:
Shuchi Chawla
University of Wisconsin-Madison and University of Texas at Austin
,
Federico Echenique
California Institute of Technology

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 July 2021

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Extended-abstract

Funding Sources

National Science Foundation

Conference

EC '21

Sponsor:

SIGecom

EC '21: The 22nd ACM Conference on Economics and Computation

July 18 - 23, 2021

Budapest, Hungary

Acceptance Rates

Overall Acceptance Rate 664 of 2,389 submissions, 28%

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EC '25

Sponsor:
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The 25th ACM Conference on Economics and Computation

July 7 - 11, 2025

Stanford , CA , USA

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Recommendations

A dynamic axiomatic approach to first-price auctions

A dynamic axiomatic approach to first-price auctions

Incentive-Compatible Learning of Reserve Prices for Repeated Auctions