Reducing mechanism design to algorithm design via machine learning

We use techniques from sample-complexity in machine learning to reduce problems of incentive-compatible mechanism design to standard algorithmic questions, for a broad class of revenue-maximizing pricing problems. Our reductions imply that for these problems, given an optimal (or -approximation) algorithm for an algorithmic pricing problem, we can convert it into a (1 + )-approximation (or (1 + )approximation) for the incentive-compatible mechanism design problem, so long as the number of bidders is sufficiently large as a function of an appropriate measure of complexity of the class of allowable pricings. We apply these results to the problem of auctioning a digital good, to the attribute auction problem which includes a wide variety of discriminatory pricing problems, and to the problem of item-pricing in unlimited-supply combinatorial auctions. From a machine learning perspective, these settings present several challenges: in particular, the "loss function" is discontinuo...

• Type: Journal
• Year: 2008
• Where: JCSS
• Authors: Maria-Florina Balcan, Avrim Blum, Jason D. Hartline, Yishay Mansour
• Keywords: Incentive-compatible Mechanism Design, JCSS 2008, Mechanism Design, Pricing Problems