Optimal Pricing for MHR Distributions

  • Yiannis GiannakopoulosEmail author
  • Keyu Zhu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11316)


We study the performance of anonymous posted-price selling mechanisms for a standard Bayesian auction setting, where n bidders have i.i.d. valuations for a single item. We show that for the natural class of Monotone Hazard Rate (MHR) distributions, offering the same, take-it-or-leave-it price to all bidders can achieve an (asymptotically) optimal revenue. In particular, the approximation ratio is shown to be \(1+O(\ln {\ln {n}}/ \ln {n})\), matched by a tight lower bound for the case of exponential distributions. This improves upon the previously best-known upper bound of \(e/(e-1)\approx 1.58\) for the slightly more general class of regular distributions. In the worst case (over n), we still show a global upper bound of 1.35. We give a simple, closed-form description of our prices which, interestingly enough, relies only on minimal knowledge of the prior distribution, namely just the expectation of its second-highest order statistic.


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.TU MunichMunichGermany
  2. 2.Georgia Institute of TechnologyAtlantaUSA

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