Worst-case efficiency ratio in false-name-proof combinatorial auction mechanisms

Atsushi Iwasaki, Vincent Conitzer, Yoshifusa Omori, Yuko Sakurai, Taiki Todo, Mingyu Guo, Makoto Yokoo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Citations (Scopus)

Abstract

This paper analyzes the worst-case efficiency ratio of false-name-proof combinatorial auction mechanisms. False-name-proofness generalizes strategy-proofness by assuming that a bidder can submit multiple bids under fictitious identifiers. Even the well-known Vickrey-Clarke-Groves mechanism is not false-name-proof. It has previously been shown that there is no false-name-proof mechanism that always achieves a Pareto efficient allocation. Consequently, if false-name bids are possible, we need to sacrifice efficiency to some extent. This leaves the natural question of how much surplus must be sacrificed. To answer this question, this paper focuses on worst-case analysis. Specifically, we consider the fraction of the Pareto efficient surplus that, we obtain and try to maximize this fraction in the worst-case, under the constraint of false-name-proofness. As far as we are aware, this is the first attempt to examine the worst-case efficiency of false-name-proof mechanisms. We show that the worst-case efficiency ratio of any false-name-proof mechanism that satisfies some apparently minor assumptions is at most 2/(m +1) for auctions with m different goods. We also observe that the worst-case efficiency ratio of existing false-name-proof mechanisms is generally 1/m or 0. Finally, we propose a novel mechanism, called the adaptive reserve price mechanism that is falso-nanic-proof when all bidders are single-minded. The worst-case efficiency ratio is 2/(m + 1), i.e., optimal.

Original languageEnglish
Title of host publication9th International Joint Conference on Autonomous Agents and Multiagent Systems 2010, AAMAS 2010
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages633-640
Number of pages8
ISBN (Print)9781617387715
Publication statusPublished - Jan 1 2010
Event9th International Joint Conference on Autonomous Agents and Multiagent Systems 2010, AAMAS 2010 - Toronto, ON, Canada
Duration: May 10 2010 → …

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume1
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Other

Other9th International Joint Conference on Autonomous Agents and Multiagent Systems 2010, AAMAS 2010
Country/TerritoryCanada
CityToronto, ON
Period5/10/10 → …

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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