Tight Approximation Ratio of Anonymous Pricing

11/02/2018
by   Yaonan Jin, et al.
0

We consider two canonical Bayesian mechanism design settings. In the single-item setting, we prove tight approximation ratio for anonymous pricing: compared with Myerson Auction, it extracts at least 1/2.62-fraction of revenue; there is a matching lower-bound example. In the unit-demand single-buyer setting, we prove tight approximation ratio between the simplest and optimal deterministic mechanisms: in terms of revenue, uniform pricing admits a 2.62-approximation of item pricing; we further validate the tightness of this ratio. These results settle two open problems asked in H13,CD15,AHNPY15,L17,JLTX18. As an implication, in the single-item setting: we improve the approximation ratio of the second-price auction with anonymous reserve to 2.62, which breaks the state-of-the-art upper bound of e ≈ 2.72.

READ FULL TEXT

page 8

page 10

page 11

page 20

page 27

research
02/15/2021

Tight Revenue Gaps among Multi-Unit Mechanisms

This paper considers Bayesian revenue maximization in the k-unit setting...
research
10/01/2018

Optimal Pricing For MHR Distributions

We study the performance of anonymous posted-price selling mechanisms fo...
research
07/09/2019

Robust Revenue Maximization Under Minimal Statistical Information

We study the problem of multi-dimensional revenue maximization when sell...
research
07/12/2021

Worst-Case Welfare of Item Pricing in the Tollbooth Problem

We study the worst-case welfare of item pricing in the tollbooth problem...
research
04/20/2018

Bayesian Auctions with Efficient Queries

Generating good revenue is one of the most important problems in Bayesia...
research
04/17/2023

Optimal Pricing Schemes for Identical Items with Time-Sensitive Buyers

Time or money? That is a question! In this paper, we consider this dilem...
research
12/10/2020

Online Posted Pricing with Unknown Time-Discounted Valuations

We study the problem of designing posted-price mechanisms in order to se...

Please sign up or login with your details

Forgot password? Click here to reset