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Deep False-Name-Proof Auction Mechanisms

  • Yuko SakuraiEmail author
  • Satoshi Oyama
  • Mingyu Guo
  • Makoto Yokoo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11873)

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.

Keywords

Mechanism design Deep learning False-name-proofness 

Notes

Acknowledgments

This work was partially supported by JSPS KAKENHI Grant Numbers JP17H0 0761, JP17KK0008 and JP18H03337, by the Kayamori Foundation of Informational Science Advancement, and by the Telecommunications Advancement Foundation. We thank Paul Dütting and his coauthors for sharing the source code for the RegretNet framework.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yuko Sakurai
    • 1
    Email author
  • Satoshi Oyama
    • 2
  • Mingyu Guo
    • 3
  • Makoto Yokoo
    • 4
  1. 1.National Institute of Advanced Industrial Science and TechnologyTokyoJapan
  2. 2.Hokkaido University/RIKENSapporoJapan
  3. 3.University of AdelaideAdelaideAustralia
  4. 4.Kyushu University/RIKENFukuokaJapan

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