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An Adaptive Sponsored Search Mechanism δ-Gain Truthful in Valuation, Time, and Budget

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Internet and Network Economics (WINE 2007)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4858))

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Abstract

This paper presents an online sponsored search auction that motivates advertisers to report their true budget, arrival time, departure time, and value per click. The auction is based on a modified Multi-Armed Bandit (MAB) mechanism that allows for advertisers who arrive and depart in an online fashion, have a value per click, and are budget constrained.

In tackling the problem of truthful budget, arrival and departure times, it turns out that it is not possible to achieve truthfulness in the classical sense (which we show in a companion paper). As such, we define a new concept called δ-gain. δ-gain bounds the utility a player can gain by lying as opposed to his utility when telling the truth. Building on the δ-gain concept we define another new concept called relative ε -gain, which bounds the relative ratio of the gain a player can achieve by lying with respect to his true utility. We argue that for many practical applications if the δ-gain and or the relative ε-gain are small, then players will not invest time and effort in making strategic choices but will truthtell as a default strategy. These concepts capture the essence of dominant strategy mechanisms as they lead the advertiser to choose truthtelling over other strategies.

In order to achieve δ-gain truthful mechanism this paper also presents a new payment scheme, Time series Truthful Payment Scheme (TTPS), for an online budget-constrained auction mechanism. The payment scheme is a generalization of the VCG principles for an online scheduling environment with budgeted players.

Using the concepts of δ-gain truthful we present the only known budget-constrained sponsored search auction with truthful guarantees on budget, arrivals, departures, and valuations. Previous works that deal with advertiser budgets only deal with the non-strategic case.

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References

  1. Aggarwal, G., Goel, A., Motwani, R.: Truthful Auctions for Pricing Search Keywords. In: Proceding of EC 2006 (2006)

    Google Scholar 

  2. Archer, A., Papadimitriou, C., Talwar, K., Tardos, E.: An approximate truthful mechanism for combinatorial auctions with single parameter agents. In: Proc. of the 14th SODA (2003)

    Google Scholar 

  3. Berry, D.A., Fristedt, B.: Bandit problems. Sequential allocation of experiments. Chapman and Hall (1985)

    Google Scholar 

  4. Bergemann, D., Valimaki, J.: Learning and Strategic Pricing. Econometrica, Econometric Society 64(5), 1125–1149 (1996)

    MATH  Google Scholar 

  5. Bergemann and Valimaki Efficient Auctions, Available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=936633

  6. Borgs, C., Chayes, J., Immorlica, N., Mahdian, M., Saberi, A.: Multi-unit auctions with budget-constrained bidders. In: ACMConference on Electronic Commerce (EC 2005), 2005

    Google Scholar 

  7. Edelman, B., Ostrovsky, M., Schwarz, M.: Internet Advertising and the Generalized Second Price Auction: Selling Billions of Dollars Worth of Keywords. Working paper (2005)

    Google Scholar 

  8. Even-Dar, E., Manor, S., Mansour, Y.: PAC Bounds for Multi-Armed Bandit and Markov Decision Processes. In: The Fifthteenth Annual Conference on Computational Learning Theory 2002 (2002)

    Google Scholar 

  9. Gittins, J.C.: Multi-armed Bandit Allocation Indices. In: Mathematical Reviews: MR90e:62113, Wiley, New York (1989)

    Google Scholar 

  10. Gonen, R.: On the Hardness of Truthful Online Auctions with Multidimensional Constraints Submitted (2007)

    Google Scholar 

  11. Gonen, R., Pavlov, E.: An Incentive-Compatible Multi Armed Bandit Mechanism. In: Third Workshop on Sponsored Search Auctions WWW2007, PODC 2007 (2007)

    Google Scholar 

  12. Gonen, R., Pavlov, E.: An Adaptive Sponsored Search Mechanism δ-Gain Truthful in Valuation, Time, and Budget, http://www.cs.huji.ac.il/~rgonen or http://www.ricagonen.com

  13. Gonen, R., Pavlov, E.: An Incentive Compatible Budgete constraind sponsered search auction with monotonicaly decreasing slots. Working paper (2007)

    Google Scholar 

  14. Kleinberg, R.: Anytime Algorithms for Multi-Armed Bandit Problems. In: Proceedings of the 17th ACM-SIAM Symposium on Discrete Algorithms (SODA 2006) (2006)

    Google Scholar 

  15. Mehta, A., Saberi, A., Vazirani, U., Vazirani, V.: Adwords and Generalized Online Matching. In: Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS), pp. 264–273. IEEE Computer Society, Los Alamitos (2005)

    Chapter  Google Scholar 

  16. Lavi, R., Nisan, N.: Online Ascending Auctions for Gradually Expiring Items. In: Proceedings of SODA 2005 (2005)

    Google Scholar 

  17. Pandey, S., Olston, C.: Handling Advertisements of Unknown Quality in Search Advertising. In: the proceedings of NIPS 2006

    Google Scholar 

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Xiaotie Deng Fan Chung Graham

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Gonen, R., Pavlov, E. (2007). An Adaptive Sponsored Search Mechanism δ-Gain Truthful in Valuation, Time, and Budget. In: Deng, X., Graham, F.C. (eds) Internet and Network Economics. WINE 2007. Lecture Notes in Computer Science, vol 4858. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77105-0_36

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  • DOI: https://doi.org/10.1007/978-3-540-77105-0_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-77104-3

  • Online ISBN: 978-3-540-77105-0

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