Learning Simple Auctions
This provides a theoretical foundation for applying auction design in real-world settings where prior information is limited, though it is incremental as it extends known computational results to unknown priors.
The paper tackles the problem of learning optimal auctions from samples when the prior distribution is unknown, showing that for various 'simple' auction classes (including item/bundle pricing with single/multiple buyers), a polynomial number of samples suffices to compute near-optimal auctions.
We present a general framework for proving polynomial sample complexity bounds for the problem of learning from samples the best auction in a class of "simple" auctions. Our framework captures all of the most prominent examples of "simple" auctions, including anonymous and non-anonymous item and bundle pricings, with either a single or multiple buyers. The technique we propose is to break the analysis of auctions into two natural pieces. First, one shows that the set of allocation rules have large amounts of structure; second, fixing an allocation on a sample, one shows that the set of auctions agreeing with this allocation on that sample have revenue functions with low dimensionality. Our results effectively imply that whenever it's possible to compute a near-optimal simple auction with a known prior, it is also possible to compute such an auction with an unknown prior (given a polynomial number of samples).