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Leveraging Possibilistic Beliefs in Unrestricted Combinatorial Auctions

Author

Listed:
  • Jing Chen

    (Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA)

  • Silvio Micali

    (Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA)

Abstract

In unrestricted combinatorial auctions, we put forward a mechanism that guarantees a meaningful revenue benchmark based on the possibilistic beliefs that the players have about each other’s valuations. In essence, the mechanism guarantees, within a factor of two, the maximum revenue that the “best informed player” would be sure to obtain if he/she were to sell the goods to his/her opponents via take-it-or-leave-it offers. Our mechanism is probabilistic and of an extensive form. It relies on a new solution concept, for analyzing extensive-form games of incomplete information, which assumes only mutual belief of rationality. Moreover, our mechanism enjoys several novel properties with respect to privacy, computation and collusion.

Suggested Citation

  • Jing Chen & Silvio Micali, 2016. "Leveraging Possibilistic Beliefs in Unrestricted Combinatorial Auctions," Games, MDPI, vol. 7(4), pages 1-19, October.
  • Handle: RePEc:gam:jgames:v:7:y:2016:i:4:p:32-:d:81384
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    References listed on IDEAS

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