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.
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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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Sakurai, Y., Oyama, S., Guo, M., Yokoo, M. (2019). Deep False-Name-Proof Auction Mechanisms. In: Baldoni, M., Dastani, M., Liao, B., Sakurai, Y., Zalila Wenkstern, R. (eds) PRIMA 2019: Principles and Practice of Multi-Agent Systems. PRIMA 2019. Lecture Notes in Computer Science(), vol 11873. Springer, Cham. https://doi.org/10.1007/978-3-030-33792-6_45
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DOI: https://doi.org/10.1007/978-3-030-33792-6_45
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