Robust combinatorial auction protocol against false-name bids.
Artificial Intelligence
Truth revelation in approximately efficient combinatorial auctions
Journal of the ACM (JACM)
Taming the Computational Complexity of Combinatorial Auctions: Optimal and Approximate Approaches
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
An Algorithm for Optimal Winner Determination in Combinatorial Auctions
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Combinatorial Auctions: A Survey
INFORMS Journal on Computing
Beyond VCG: Frugality of Truthful Mechanisms
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
False-name-proof combinatorial auction protocol: Groves Mechanism with SubModular Approximation
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
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The Vickrey-Clarke-Groves (VCG) protocol is a theoretically well-founded protocol that can be used for combinatorial auctions. However, the VCG has several limitations such as (a) vulnerability to false-name bids, (b) vulnerability to loser collusion, and (c) the outcome is not in the core. Yokoo, Matsutani, & Iwasaki (2006) presented a new combinatorial auction protocol called the Groves Mechanism with SubModular Approximation (GM-SMA). This protocol satisfies the following characteristics: (1) it is false-name-proof, (2) each winner is included in a Pareto efficient allocation, and (3) as long as a Pareto efficient allocation is achieved, the protocol is robust against the collusion of losers and the outcome is in the core. The GM-SMA is the first protocol that satisfies all three of these characteristics. The basic ideas of the GM-SMA are as follows: (i) it is based on the VCG protocol, i.e., the payment of a winner in this protocol is identical to the payment in one instance of the Groves mechanism, which is a class of protocols that includes the VCG, (ii) when calculating the payment of a bidder, we approximate the valuations of other bidders by using a submodular valuation function (submodular approximation). This paper shows a high-level presentation of the GM-SMA protocol. and discusses open problems and the relationship to other works in AI.