VCG Under False-name Attacks: a Bayesian Analysis
Abstract
VCG is a classical combinatorial auction that maximizes social welfare. However, while the standard single-item Vickrey auction is false-name-proof, a major failure of multi-item VCG is its vulnerability to false-name attacks. This occurs already in the natural bare minimum model in which there are two identical items and bidders are single-minded. Previous solutions to this challenge focused on developing alternative mechanisms that compromise social welfare. We re-visit the VCG auction vulnerability and consider the bidder behavior in Bayesian settings. In service of that we introduce a novel notion, termed the granularity threshold, that characterizes VCG Bayesian resilience to false-name attacks as a function of the bidder type distribution. Using this notion we show a large class of cases in which VCG indeed obtains Bayesian resilience for the two-item single-minded setting.
Cite
@article{arxiv.1911.07210,
title = {VCG Under False-name Attacks: a Bayesian Analysis},
author = {Yotam Gafni and Ron Lavi and Moshe Tennenholtz},
journal= {arXiv preprint arXiv:1911.07210},
year = {2021}
}
Comments
A partial and preliminary version of this paper has appeared in The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20). Supporting code for generating the article's figures can be found at https://github.com/yotam-gafni/vcg_bayesian_fnp