Sample Complexity for Non-Truthful Mechanisms

Jason Hartline; Samuel Taggart

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arXiv · 1608.01875

Sample Complexity for Non-Truthful Mechanisms

Jason Hartline·Samuel Taggart

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Abstract

This paper considers the design of non-truthful mechanisms from samples. We identify a parameterized family of mechanisms with strategically simple winner-pays-bid, all-pay, and truthful payment formats. In general (not necessarily downward-closed) single-parameter feasibility environments we prove that the family has low representation and generalization error. Specifically, polynomially many bid samples suffice to identify and run a mechanism that is $\epsilon$-close in Bayes-Nash equilibrium revenue or welfare to that of the optimal truthful mechanism with high probability.

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BibTeX RIS

Jason Hartline, Samuel Taggart. 2016-08-05. Sample Complexity for Non-Truthful Mechanisms. https://arxiv.org/abs/1608.01875

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