A truthful randomized mechanism for combinatorial public projects via convex optimization
Proceedings of the 12th ACM conference on Electronic commerce
From convex optimization to randomized mechanisms: toward optimal combinatorial auctions
Proceedings of the forty-third annual ACM symposium on Theory of computing
Black-box reductions in mechanism design
APPROX'11/RANDOM'11 Proceedings of the 14th international workshop and 15th international conference on Approximation, randomization, and combinatorial optimization: algorithms and techniques
A truthful mechanism for value-based scheduling in cloud computing
SAGT'11 Proceedings of the 4th international conference on Algorithmic game theory
Truthful Approximation Schemes for Single-Parameter Agents
SIAM Journal on Computing
A universally-truthful approximation scheme for multi-unit auctions
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
Black-box reductions for cost-sharing mechanism design
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
Bayesian incentive compatibility via fractional assignments
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
Bayesian incentive compatibility via matchings
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
On the limits of black-box reductions in mechanism design
STOC '12 Proceedings of the forty-fourth annual ACM symposium on Theory of computing
Proceedings of the 13th ACM Conference on Electronic Commerce
Randomized mechanisms for multi-unit auctions
ICALP'12 Proceedings of the 39th international colloquium conference on Automata, Languages, and Programming - Volume Part II
Truthful mechanism design for multidimensional covering problems
WINE'12 Proceedings of the 8th international conference on Internet and Network Economics
A Truthful Mechanism for Value-Based Scheduling in Cloud Computing
Theory of Computing Systems
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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.