Approximate mechanism design without money
Proceedings of the 10th ACM conference on Electronic commerce
Methods for boosting revenue in combinatorial auctions
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Approximating revenue-maximizing combinatorial auctions
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Automated design of multistage mechanisms
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
STACS'99 Proceedings of the 16th annual conference on Theoretical aspects of computer science
Approximate privacy: foundations and quantification (extended abstract)
Proceedings of the 11th ACM conference on Electronic commerce
Asymptotically optimal strategy-proof mechanisms for two-facility games
Proceedings of the 11th ACM conference on Electronic commerce
Winner-imposing strategyproof mechanisms for multiple facility location games
WINE'10 Proceedings of the 6th international conference on Internet and network economics
Strategy-proof mechanisms for facility location games with many facilities
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
Complexity of mechanism design
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Mechanism design on discrete lines and cycles
Proceedings of the 13th ACM Conference on Electronic Commerce
TreeMatrix: A Hybrid Visualization of Compound Graphs
Computer Graphics Forum
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We consider the mechanism design problem for agents with single-peaked preferences over multi-dimensional domains when multiple alternatives can be chosen. Facility location and committee selection are classic embodiments of this problem. We propose a class of percentile mechanisms, a form of generalized median mechanisms, that are strategy-proof, and derive worst-case approximation ratios for social cost and maximum load for L1 and L2 cost models. More importantly, we propose a sample-based framework for optimizing the choice of percentiles relative to any prior distribution over preferences, while maintaining strategy-proofness. Our empirical investigations, using social cost and maximum load as objectives, demonstrate the viability of this approach and the value of such optimized mechanisms vis-à-vis mechanisms derived through worst-case analysis.