Computationally feasible VCG mechanisms
Proceedings of the 2nd ACM conference on Electronic commerce
Mechanism Design via Machine Learning
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Simple versus optimal mechanisms
Proceedings of the 10th ACM conference on Electronic commerce
Proceedings of the 10th ACM conference on Electronic commerce
Revenue maximization with a single sample
Proceedings of the 11th ACM conference on Electronic commerce
The power of randomness in bayesian optimal mechanism design
Proceedings of the 11th ACM conference on Electronic commerce
Prior-independent auctions for risk-averse agents
Proceedings of the fourteenth ACM conference on Electronic commerce
Near-optimal multi-unit auctions with ordered bidders
Proceedings of the fourteenth ACM conference on Electronic commerce
Prior-independent mechanisms for scheduling
Proceedings of the forty-fifth annual ACM symposium on Theory of computing
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Most results in revenue-maximizing auction design hinge on "getting the price right" --- offering goods to bidders at a price low enough to encourage a sale, but high enough to garner non-trivial revenue. Getting the price right can be hard work, especially when the seller has little or no a priori information about bidders' valuations. A simple alternative approach is to "let the market do the work", and have prices emerge from competition for scarce goods. The simplest-imaginable implementation of this idea is the following: first, if necessary, impose an artificial limit on the number of goods that can be sold; second, run the welfare-maximizing VCG mechanism subject to this limit. We prove that such "supply-limiting mechanisms" achieve near-optimal expected revenue in a range of single- and multi-parameter Bayesian settings. Indeed, despite their simplicity, we prove that they essentially match the state-of-the-art in prior-independent mechanism design.