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Revenue Maximization for Market Intermediation with Correlated Priors

  • Matthias Gerstgrasser
  • Paul W. Goldberg
  • Elias Koutsoupias
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9928)

Abstract

We study the computational challenge faced by a intermediary who attempts to profit from trade with a small number of buyers and sellers of some item. In the version of the problem that we study, the number of buyers and sellers is constant, but their joint distribution of the item’s value may be complicated. We consider discretized distributions, where the complexity parameter is the support size, or number of different prices that may occur. We show that maximizing the expected revenue is computationally tractable (via an LP) if we are allowed to use randomized mechanisms. For the deterministic case, we show how an optimal mechanism can be efficiently computed for the one-seller/one-buyer case, but give a contrasting NP-completeness result for the one-seller/two-buyer case.

Keywords

Optimal Mechanism Reverse Auction Optimal Auction Balance Inventory Incentive Compatible Mechanism 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of OxfordOxfordUK

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