Revenue failures and collusion in combinatorial auctions and exchanges with vcg payments

In a combinatorial auction, there are multiple items for sale, and bidders are allowed to place a bid on a bundle of these items rather than just on the individual items. A key problem in this and similar settings is that of strategic bidding, where bidders misreport their true preferences in order to effect a better outcome for themselves. The VCG payment scheme is the canonical method for motivating the bidders to bid truthfully. We study two related problems concerning the VCG payment scheme: the problem of revenue guarantees, and that of collusion. The existence of such problems is known by many; in this paper, we lay out their full extent. We study four settings: combinatorial forward auctions with free disposal, combinatorial reverse auctions with free disposal, combinatorial forward (or reverse) auctions without free disposal, and combinatorial exchanges. In each setting, we give an example of how additional bidders (colluders) can make the outcome much worse (less revenue or higher cost) under the VCG payment scheme (but not under a first price scheme); derive necessary and sufficient conditions for such an effective collusion to be possible under the VCG payment scheme; and (when nontrivial) study the computational complexity of deciding whether these conditions hold.

[1]  Vincent Conitzer,et al.  Automated mechanism design: complexity results stemming from the single-agent setting , 2003, ICEC '03.

[2]  Noam Nisan,et al.  Computationally feasible VCG mechanisms , 2000, EC '00.

[3]  Noam Nisan,et al.  Towards a characterization of truthful combinatorial auctions , 2003, 44th Annual IEEE Symposium on Foundations of Computer Science, 2003. Proceedings..

[4]  Michael P. Wellman,et al.  AkBA: a progressive, anonymous-price combinatorial auction , 2000, EC '00.

[5]  Noam Nisan,et al.  Bidding and allocation in combinatorial auctions , 2000, EC '00.

[6]  David Levine,et al.  CABOB: A Fast Optimal Algorithm for Combinatorial Auctions , 2001, IJCAI.

[7]  Y. Shoham,et al.  Truth revelation in rapid, approximately efficient combinatorial auctions , 2001 .

[8]  E. H. Clarke Multipart pricing of public goods , 1971 .

[9]  Ronald M. Harstad,et al.  Computationally Manageable Combinational Auctions , 1998 .

[10]  Phz eMKMLafgZ,et al.  iBundle: An Efficient Ascending Price Bundle Auction , 1999 .

[11]  Theodore Groves,et al.  Incentives in Teams , 1973 .

[12]  A. Mas-Colell,et al.  Microeconomic Theory , 1995 .

[13]  Ilya Segal,et al.  Solutions manual for Microeconomic theory : Mas-Colell, Whinston and Green , 1997 .

[14]  William Vickrey,et al.  Counterspeculation, Auctions, And Competitive Sealed Tenders , 1961 .

[15]  Tuomas Sandholm,et al.  Algorithm for optimal winner determination in combinatorial auctions , 2002, Artif. Intell..

[16]  Jerry R. Green,et al.  Characterization of Satisfactory Mechanisms for the Revelation of Preferences for Public Goods , 1977 .

[17]  Noam Nisan,et al.  Incentive compatible multi unit combinatorial auctions , 2003, TARK '03.

[18]  Daniel Lehmann,et al.  Optimal solutions for multi-unit combinatorial auctions: branch and bound heuristics , 2000, EC '00.

[19]  Vincent Conitzer,et al.  Self-interested automated mechanism design and implications for optimal combinatorial auctions , 2004, EC '04.

[20]  Vincent Conitzer,et al.  Complexity of Mechanism Design , 2002, UAI.

[21]  Lawrence M. Ausubel,et al.  Ascending Auctions with Package Bidding , 2002 .