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JCSS
2008

Reducing mechanism design to algorithm design via machine learning

10 years 4 months ago
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...
Maria-Florina Balcan, Avrim Blum, Jason D. Hartlin
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2008
Where JCSS
Authors Maria-Florina Balcan, Avrim Blum, Jason D. Hartline, Yishay Mansour
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