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Multi-parameter Mechanism Design under Budget and Matroid Constraints

  • Monika Henzinger
  • Angelina Vidali
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6942)

Abstract

The design of truthful auctions that approximate the optimal expected revenue is a central problem in algorithmic mechanism design. 30 years after Myerson’s characterization of Bayesian optimal auctions in single-parameter domains [8], characterizing but also providing efficient mechanisms for multi-parameter domains still remains a very important unsolved problem. Our work improves upon recent results in this area, introducing new techniques for tackling the problem, while also combining and extending recently introduced tools.

In particular we give the first approximation algorithms for Bayesian auctions with multiple heterogeneous items when bidders have additive valuations, budget constraints and general matroid feasibility constraints.

Keywords

Optimal Revenue Uniform Matroids Virtual Valuation Valuation Distribution Monotone Hazard Rate 
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 2011

Authors and Affiliations

  • Monika Henzinger
    • 1
  • Angelina Vidali
    • 1
  1. 1.Theory and Applications of Algorithms Research GroupUniversity of ViennaAustria

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