Deep False-Name-Proof Auction Mechanisms

Yuko Sakurai, Satoshi Oyama, Mingyu Guo, Makoto Yokoo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationPRIMA 2019
Subtitle of host publicationPrinciples and Practice of Multi-Agent Systems - 22nd International Conference, Proceedings
EditorsMatteo Baldoni, Mehdi Dastani, Beishui Liao, Yuko Sakurai, Rym Zalila Wenkstern
Number of pages8
ISBN (Print)9783030337919
Publication statusPublished - 2019
Event22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2019 - Turin, Italy
Duration: Oct 28 2019Oct 31 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11873 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2019

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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