Algorithm for optimal winner determination in combinatorial auctions
Artificial Intelligence
Truth revelation in approximately efficient combinatorial auctions
Journal of the ACM (JACM)
Truthful approximation mechanisms for restricted combinatorial auctions: extended abstract
Eighteenth national conference on Artificial intelligence
Combinatorial Auctions
Truthful and Near-Optimal Mechanism Design via Linear Programming
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Truthful randomized mechanisms for combinatorial auctions
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
Computationally feasible VCG mechanisms
Journal of Artificial Intelligence Research
Taming the computational complexity of combinatorial auctions: optimal and approximate approaches
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Average-case analysis of VCG with approximate resource allocation algorithms
Decision Support Systems
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Mechanism design provides a useful practical paradigm for competitive resource allocation when agent preferences are uncertain. Vickrey-Clarke-Groves (VCG) mechanism offers a general technique for resource allocation with payments, ensuring allocative efficiency while eliciting truthful information about preferences. However, VCG relies on exact computation of optimal allocation of resources, a problem which is often computationally intractable. Using approximate allocation algorithms in place of exact algorithms gives rise to a VCG-based mechanism, which, unfortunately, no longer guarantees truthful revelation of preferences. Our main result is an average-case bound, which uses information about average, rather than worst-case, performance of an algorithm. We show how to combine the resulting bound with simulations to obtain probabilistic confidence bounds on agent incentives to misreport their preferences and illustrate the technique using combinatorial auction data. One important consequence of our analysis is an argument that using state-of-the-art algorithms for solving combinatorial allocation problems essentially eliminates agent incentives to misreport their preferences.