2010 IEEE 51st Annual Symposium on Foundations of Computer Science (2010)

Las Vegas, Nevada USA

Oct. 23, 2010 to Oct. 26, 2010

ISSN: 0272-5428

ISBN: 978-0-7695-4244-7

pp: 775-784

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/FOCS.2010.79

ABSTRACT

We give the first black-box reduction from arbitrary approximation algorithms to truthful approximation mechanisms for a non-trivial class of multi-parameter problems. Specifically, we prove that every packing problem that admits an FPTAS also admits a truthful-in-expectation randomized mechanism that is an FPTAS. Our reduction makes novel use of smoothed analysis, by employing small perturbations as a tool in algorithmic mechanism design. We develop a “duality'' between linear perturbations of the objective function of an optimization problem and of its feasible set, and use the “primal'' and “dual'' viewpoints to prove the running time bound and the truthfulness guarantee, respectively, for our mechanism.

INDEX TERMS

Mechanism Design, Truthful Approximation Algorithms, Smoothed Analysis

CITATION

T. Roughgarden and S. Dughmi, "Black-Box Randomized Reductions in Algorithmic Mechanism Design,"

*2010 IEEE 51st Annual Symposium on Foundations of Computer Science(FOCS)*, Las Vegas, Nevada USA, 2010, pp. 775-784.

doi:10.1109/FOCS.2010.79

CITATIONS