False-Name Bidding and Economic Efficiency in Combinatorial Auctions

Author:

Alkalay-Houlihan Colleen,Vetta Adrian

Abstract

Combinatorial auctions are multiple-item auctions in which bidders may place bids on any package (subset) of goods. This additional expressibility produces benefits that have led to combinatorial auctions becoming extremely important both in practice and in theory. In the computer science community, auction design has focused primarily on computational practicality and incentive compatibility. The latter concerns mechanisms that are resistant to bidders misrepresenting themselves via a single false identity; however, with modern forms of bid submission, such as electronic bidding, other types of cheating have become feasible. Prominent amongst them is false-name bidding; that is, bidding under pseudonyms. For example, the ubiquitous Vickrey-Clarke-Groves (VCG) mechanism is incentive compatible and produces optimal allocations, but it is not false-name-proof–bidders can increase their utility by submitting bids under multiple identifiers. Thus, there has recently been much interest in the design and analysis of false-name-proof auction mechanisms. These false-name-proof mechanisms, however, have polynomially small efficiency guarantees: they can produce allocations with very low economic efficiency/social welfare. In contrast, we show that, provided the degree to which different goods are complementary is bounded (as is the case in many important, practical auctions), the VCG mechanism gives a constant efficiency guarantee. Constant efficiency guarantees hold even at equilibria where the agents bid in a manner that is not individually rational. Thus, while an individual bidder may personally benefit greatly from making false-name bids, this will have only a small detrimental effect on the objective of the auctioneer: maximizing economic efficiency. So, from the auctioneer's viewpoint the VCG mechanism remains preferable to false-name-proof mechanisms.

Publisher

Association for the Advancement of Artificial Intelligence (AAAI)

Subject

General Medicine

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