Optimal Pricing for MHR and λ-regular Distributions

Giannakopoulos Y, Pocas D, Zhu K (2021)


Publication Type: Journal article

Publication year: 2021

Journal

Book Volume: 9

Pages Range: 2:1--2:28

Article Number: 3434423

Journal Issue: 1

DOI: 10.1145/3434423

Open Access Link: https://arxiv.org/abs/1810.00800

Abstract

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)≈ 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. Furthermore, we extend our techniques to handle the more general class of λ-regular distributions that interpolate between MHR (λ =0) and regular (λ =1). Our anonymous pricing rule now results in an asymptotic approximation ratio that ranges smoothly, with respect to λ, from 1 (MHR distributions) to e/(e-1) (regular distributions). Finally, we explicitly give a class of continuous distributions that provide matching lower bounds, for every λ.

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APA:

Giannakopoulos, Y., Pocas, D., & Zhu, K. (2021). Optimal Pricing for MHR and λ-regular Distributions. ACM Transactions on Economics and Computation, 9(1), 2:1--2:28. https://dx.doi.org/10.1145/3434423

MLA:

Giannakopoulos, Yiannis, Diogo Pocas, and Keyu Zhu. "Optimal Pricing for MHR and λ-regular Distributions." ACM Transactions on Economics and Computation 9.1 (2021): 2:1--2:28.

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