Combinatorial Auctions
Average-case analysis of VCG with approximate resource allocation algorithms
Decision Support Systems
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We present a series of results providing evidence that the incentive problem with approximate VCG-based mechanisms is often not very severe. Our first result uses average-case analysis to show that if an algorithm can solve the allocation problem well for a large proportion of instances, incentives to lie essentially disappear. We next show that even if such incentives exist, a simple enhancement of the mechanism makes it unlikely that any player will find an improving deviation. Additionally, we offer a simulation-based technique to verify empirically the incentive properties of an arbitrary approximation algorithm and demonstrate it in a specific instance using combinatorial auction data.