open access

https://hdl.handle.net/2115/88721

Deep False-Name-Proof Auction Mechanisms

False-name-proofness

2019-10-21

Springer Cham

PRIMA 2019: Principles and Practice of Multi-Agent Systems

11873

594-601

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.

学術雑誌論文

Accepted Manuscript

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: http://dx.doi.org/10.1007/978-3-030-33792-6_45

0302-9743