Randomized algorithms
On approximating rectangle tiling and packing
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Truthful approximation mechanisms for restricted combinatorial auctions: extended abstract
Eighteenth national conference on Artificial intelligence
Approximation algorithms for combinatorial auctions with complement-free bidders
Proceedings of the thirty-seventh annual ACM symposium on Theory of computing
Towards truthful mechanisms for binary demand games: a general framework
Proceedings of the 6th ACM conference on Electronic commerce
Mechanism design for single-value domains
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
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Running several sub-optimal algorithms and choosing-the optimal one is a common procedure in computer science, most notably in the design of approximation algorithms. This paper deals with one significant flaw of this technique in environments where the inputs are provided by rational agents: such protocols are not necessarily incentive compatible even when the underlying algorithms are. We characterize sufficient and necessary conditions for such best-outcome protocols to be incentive compatible in a general model for agents with one-dimensional private data. We show how our techniques apply in several settings.