Predictive Benchmark for Revenue in Combinatorial Auctions

Area 09 – Ingegneria industriale e dell'informazione

A goal often desired in auctions is the maximization of the auctioneer’s revenue. Auctions designers need to define some kind of benchmark to measure the effectiveness, in terms of maximizing revenue, of the experimented auction mechanisms. In the case of single-attribute first-price iterative (ascending) combinatorial auctions, with independent and private valuations, we argue that the lower the competition level underlying the players’ valuations, the harder to obtain the benchmark equal to the maximum revenue when all players bid truthfully, notwithstanding the additional auction rules adopted.We therefore estimate the maximum revenue which the level of intrinsic competition in the auction can induce, excluding collusion phenomena and budget restrictions for the players, and we consider this quantity as a significant benchmark; we also provide an integer linear problem to compute it. Then we present an index based on these two benchmarks to fairly assess the auction performance in terms ofrevenue obtained by the auctioneer. Finally we show how the proposed benchmark suggests a new rule to decide the payments of selected groups of bidders. In addition, every criterion to share out the joint payments among the single players of such groups completes the definition of a payment rule for bidders

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