Optimal and efficient parametric auctions

Pablo Azar, Constantinos Daskalakis, Silvio Micali, S. Matthew Weinberg

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Scopus citations

Abstract

Consider a seller who seeks to provide service to a collection of interested parties, subject to feasibility constraints on which parties may be simultaneously served. Assuming that a distribution is known on the value of each party for service - arguably a strong assumption - Myerson's seminal work provides revenue optimizing auctions [12]. We show instead that, for very general feasibility constraints, only knowledge of the median of each party's value distribution, or any other quantile of these distributions, or approximations thereof, suffice for designing simple auctions that simultaneously approximate both the optimal revenue and the optimal welfare. Our results apply to all downward-closed feasibility constraints under the assumption that the underlying, unknown value distributions are monotone hazard rate, and to all matroid feasibility constraints under the weaker assumption of regularity of the underlying distributions. Our results jointly generalize the single-item results obtained by Azar and Micali [2] on parametric auctions, and Daskalakis and Pierrakos [6] for simultaneously approximately optimal and efficient auctions.

Original languageEnglish (US)
Title of host publicationProceedings of the 24th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2013
PublisherAssociation for Computing Machinery
Pages596-604
Number of pages9
ISBN (Print)9781611972511
DOIs
StatePublished - Jan 1 2013
Externally publishedYes
Event24th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2013 - New Orleans, LA, United States
Duration: Jan 6 2013Jan 8 2013

Publication series

NameProceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms

Other

Other24th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2013
CountryUnited States
CityNew Orleans, LA
Period1/6/131/8/13

All Science Journal Classification (ASJC) codes

  • Software
  • Mathematics(all)

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