Revelation Gap for Pricing from Samples
Yiding Feng, Jason D. Hartline, Yingkai Li

TL;DR
This paper investigates the limitations of truthful mechanisms in prior-independent auction design, revealing a significant gap between truthful and non-truthful mechanisms in revenue approximation for single-item sales.
Contribution
It identifies and quantifies the revelation gap for revenue maximization, demonstrating that non-truthful mechanisms can outperform truthful ones in prior-independent settings.
Findings
Sample-bid mechanism achieves approximation ratios of 1.835 (regular) and 1.296 (MHR)
No truthful mechanism can surpass approximation ratios of 1.957 (regular) and 1.543 (MHR)
Non-truthful mechanisms can outperform truthful mechanisms, establishing a non-trivial revelation gap.
Abstract
This paper considers prior-independent mechanism design, in which a single mechanism is designed to achieve approximately optimal performance on every prior distribution from a given class. Most results in this literature focus on mechanisms with truthtelling equilibria, a.k.a., truthful mechanisms. Feng and Hartline (2018) introduce the revelation gap to quantify the loss of the restriction to truthful mechanisms. We solve a main open question left in Feng and Hartline (2018); namely, we identify a non-trivial revelation gap for revenue maximization. Our analysis focuses on the canonical problem of selling a single item to a single agent with only access to a single sample from the agent's valuation distribution. We identify the sample-bid mechanism (a simple non-truthful mechanism) and upper-bound its prior-independent approximation ratio by 1.835 (resp. 1.296) for regular (resp.…
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Taxonomy
TopicsAuction Theory and Applications · Consumer Market Behavior and Pricing · Experimental Behavioral Economics Studies
