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Deep False-Name-Proof Auction Mechanisms

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Title: Deep False-Name-Proof Auction Mechanisms
Authors: Sakurai, Yuko Browse this author →KAKEN DB
Oyama, Satoshi Browse this author →KAKEN DB
Guo, Mingyu Browse this author
Yokoo, Makoto Browse this author
Keywords: Mechanism design
Deep learning
Issue Date: 21-Oct-2019
Publisher: Springer Cham
Journal Title: PRIMA 2019: Principles and Practice of Multi-Agent Systems
Volume: 11873
Start Page: 594
End Page: 601
Publisher DOI: 10.1007/978-3-030-33792-6_45
Abstract: We explore an approach to designing false-name-proof auction mechanisms using deep learning. While multi-agent systems researchers have recently proposed data-driven approaches to automatically designing auction mechanisms through deep learning, false-name-proofness, which generalizes strategy-proofness by assuming that a bidder can submit multiple bids under fictitious identifiers, has not been taken into account as a property that a mechanism has to satisfy. We extend the RegretNet neural network architecture to incorporate false-name-proof constraints and then conduct experiments demonstrating that the generated mechanisms satisfy false-name-proofness.
Rights: This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at:
Type: article (author version)
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 小山 聡

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