Black-Box Randomized Reductions in Algorithmic Mechanism Design

We give the first black-box reduction from arbitrary approximation algorithms to truthful approximation mechanisms for a non-trivial class of multiparameter 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. Keywords-Mechanism Design; Truthful Approximation Algorithms; Smoothed Analysis

• Type: Journal
• Year: 2010
• Where: FOCS
• Authors: Shaddin Dughmi, Tim Roughgarden
• Keywords: Algorithmic Mechanism Design, Approximation, FOCS 2010, Theoretical Computer Science, Truthful Approximation Algorithms