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Robust Revenue Maximization Under Minimal Statistical Information

  • Yiannis Giannakopoulos
  • Diogo PoçasEmail author
  • Alexandros Tsigonias-Dimitriadis
Conference paper
  • 64 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12495)

Abstract

We study the problem of multi-dimensional revenue maximization when selling m items to a buyer that has additive valuations for them, drawn from a (possibly correlated) prior distribution. Unlike traditional Bayesian auction design, we assume that the seller has a very restricted knowledge of this prior: they only know the mean \(\mu _j\) and an upper bound \(\sigma _j\) on the standard deviation of each item’s marginal distribution. Our goal is to design mechanisms that achieve good revenue against an ideal optimal auction that has full knowledge of the distribution in advance. Informally, our main contribution is a tight quantification of the interplay between the dispersity of the priors and the aforementioned robust approximation ratio. Furthermore, this can be achieved by very simple selling mechanisms.

More precisely, we show that selling the items via separate price lotteries achieves an \(O(\log r)\) approximation ratio where \(r=\max _j(\sigma _j/\mu _j)\) is the maximum coefficient of variation across the items. If forced to restrict ourselves to deterministic mechanisms, this guarantee degrades to \(O(r^2)\). Assuming independence of the item valuations, these ratios can be further improved by pricing the full bundle. For the case of identical means and variances, in particular, we get a guarantee of \(O(\log (r/m))\) which converges to optimality as the number of items grows large. We demonstrate the optimality of the above mechanisms by providing matching lower bounds. Our tight analysis for the deterministic case resolves an open gap from the work of Azar and Micali [ITCS’13].

Keywords

Optimal auctions Revenue maximization Parametric auctions Robust optimization 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.TU MunichMunichGermany
  2. 2.LASIGE, Faculdade de CiênciasUniversidade de LisboaLisbonPortugal

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