Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
Mechanism design with incomplete languages
Proceedings of the 3rd ACM conference on Electronic Commerce
Algorithm for optimal winner determination in combinatorial auctions
Artificial Intelligence
Combinatorial Auctions
Implementation with a bounded action space
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
The Long Tail: Why the Future of Business Is Selling Less of More
The Long Tail: Why the Future of Business Is Selling Less of More
Combinatorial auctions with k-wise dependent valuations
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Expressive banner ad auctions and model-based online optimization for clearing
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Computationally feasible VCG mechanisms
Journal of Artificial Intelligence Research
Sponsored search with contexts
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Cost of conciseness in sponsored search auctions
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Complexity of mechanism design
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Expressive banner ad auctions and model-based online optimization for clearing
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Methodology for designing reasonably expressive mechanisms with application to ad auctions
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Expressive auctions for externalities in online advertising
Proceedings of the 19th international conference on World wide web
An expressive mechanism for auctions on the web
Proceedings of the 20th international conference on World wide web
Simplicity-expressiveness tradeoffs in mechanism design
Proceedings of the 12th ACM conference on Electronic commerce
Capturing location-privacy preferences: quantifying accuracy and user-burden tradeoffs
Personal and Ubiquitous Computing
Computing optimal outcomes under an expressive representation of settings with externalities
Journal of Computer and System Sciences
Matelas: a predicate calculus common formal definition for social networking
ABZ'10 Proceedings of the Second international conference on Abstract State Machines, Alloy, B and Z
Towards more expressive cake cutting
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
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A key trend in (electronic) commerce is a demand for higher levels of expressiveness in the mechanisms that mediate interactions. We develop a theory that ties the expressiveness of mechanisms to their efficiency in a domain-independent manner. We introduce two new expressiveness measures, 1) maximum impact dimension, which captures the number of ways that an agent can impact the outcome, and 2) shatterable outcome dimension, which is based on the concept of shattering from computational learning theory. We derive an upper bound on the expected efficiency of any mechanism under its most efficient Nash equilibrium. Remarkably, it depends only on the mechanism's expressiveness. We prove that the bound increases strictly as we allow more expressiveness. We also show that in some cases a small increase in expressiveness yields an arbitrarily large increase in the bound. Finally, we study channel-based mechanisms, which subsume most combinatorial auctions, multi-attribute mechanisms, and the Vickrey-Clarke-Groves scheme. We show that our domain-independent measures of expressiveness appropriately relate to the natural measure of expressiveness of channel-based mechanisms: the number of channels allowed. Using this bridge, our general results yield interesting implications. For example, any (channel-based) multi-item auction that does not allow rich combinatorial bids can be arbitrarily inefficient--unless agents have no private information.