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Optimal Allocation Mechanisms When Bidders Ranking for the objects is common

  • Juan Feng
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    Search engines commonly use “sponsored linksâ€, where certain advertisers’ links are promoted to be placed above others in return for monetary payment. It is natural to assume that all providers value a higher ranked placement more than lower ranked ones. Then how should the seller optimally sell these ranked slots is critical for the search engines. In this paper we study the seller’s (search engine) optimal selling mechanism in the following setting: buyers (advertisers), each of whom has unit demand, compete for positions o ered by the seller. While each buyer’s valuation for each position is private and independent, the ranking for these positions is common among all the buyers. However the rate at which these valuations change might be di erent. We begin with 4 simplified scenarios specifying how buyers valuations change for di erent positions, namely,“parallelâ€, “convergentâ€, “divergentâ€, and “convergent then divergentâ€. We find that the optimal incentive compatible allocation mechanism is quite di erent in determining the “pivot†types and the order to fill in the positions. Under some conditions, these mechanisms are even ecient in terms of maximizing the total welfare of the auctioneer and bidders. When the buyers’ valuations for lower positions decrease at di erent rates, the seller earns more than the case of simple second-price sequential auction

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    Paper provided by Econometric Society in its series Econometric Society 2004 North American Summer Meetings with number 545.

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    Date of creation: 11 Aug 2004
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    Handle: RePEc:ecm:nasm04:545
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    1. Armstrong, Mark, 2000. "Optimal Multi-object Auctions," Review of Economic Studies, Wiley Blackwell, vol. 67(3), pages 455-81, July.
    2. Paul Resnick & Christopher Avery & Richard Zeckhauser, 1999. "The Market for Evaluations," American Economic Review, American Economic Association, vol. 89(3), pages 564-584, June.
    3. Gibbard, Allan, 1973. "Manipulation of Voting Schemes: A General Result," Econometrica, Econometric Society, vol. 41(4), pages 587-601, July.
    4. Alvin E. Roth & Axel Ockenfels, . "Last-Minute Bidding and the Rules for Ending Second-Price Auctions: Evidence from eBay and Amazon Auctions on the Internet," Papers on Strategic Interaction 2002-32, Max Planck Institute of Economics, Strategic Interaction Group.
    5. Green, Jerry & Laffont, Jean-Jacques, 1977. "Characterization of Satisfactory Mechanisms for the Revelation of Preferences for Public Goods," Econometrica, Econometric Society, vol. 45(2), pages 427-38, March.
    6. Avery, Christopher & Hendershott, Terrence, 2000. "Bundling and Optimal Auctions of Multiple Products," Review of Economic Studies, Wiley Blackwell, vol. 67(3), pages 483-97, July.
    7. P. Dasgupta & Eric Maskin, 1998. "Efficient Auctions," Harvard Institute of Economic Research Working Papers 1857, Harvard - Institute of Economic Research.
    8. Flavio Menezes & Paulo Klinger Monteiro, 1996. "Simultaneous Pooled Auctions," Microeconomics 9611001, EconWPA.
    9. Lawrence M. Ausubel & Peter Cramton, 1995. "Demand Reduction and Inefficiency in Multi-Unit Auctions," Papers of Peter Cramton 98wpdr, University of Maryland, Department of Economics - Peter Cramton, revised 22 Jul 2002.
    10. Jehiel, Philippe & Moldovanu, Benny, 2001. "Efficient Design with Interdependent Valuations," Econometrica, Econometric Society, vol. 69(5), pages 1237-59, September.
    11. Palfrey, Thomas R, 1983. "Bundling Decisions by a Multiproduct Monopolist with Incomplete Information," Econometrica, Econometric Society, vol. 51(2), pages 463-83, March.
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