Abstract
Autobidding is becoming increasingly important in the domain of online advertising, and has become a critical tool used by many advertisers for optimizing their ad campaigns. We formulate fundamental questions around the problem of bidding for performance under very general affine cost constraints. We design optimal single-agent bidding strategies for the general bidding problem, in multi-slot truthful auctions. The novel contribution is to show a strong connection between bidding and auction design, in that the bidding formula is optimal if and only if the underlying auction is truthful.
Next, we move from the single-agent view to a full-system view: What happens when all advertisers adopt optimal autobidding? We prove that in general settings, there exists an equilibrium between the bidding agents for all the advertisers. As our main result, we prove a Price of Anarchy bound: For any number of general affine constraints, the total value (conversions) obtained by the advertisers in the bidding-agent equilibrium is no less than 1/2 of what we could generate via a centralized ad allocation scheme, one which does not consider any auction incentives or provide any per-advertiser guarantee.
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References
Agrawal, S., Devanur, N.R.: Fast algorithms for online stochastic convex programming. In: Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2015, San Diego, CA, USA, 4–6 January 2015, pp. 1405–1424 (2015). https://doi.org/10.1137/1.9781611973730.93
Arora, S., Hazan, E., Kale, S.: The multiplicative weights update method: a meta-algorithm and applications. Theory Comput. 8(1), 121–164 (2012)
Azar, Y., Feldman, M., Gravin, N., Roytman, A.: Liquid price of anarchy. In: Bilò, V., Flammini, M. (eds.) SAGT 2017. LNCS, vol. 10504, pp. 3–15. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66700-3_1
Buchbinder, N., Jain, K., Naor, J.S.: Online primal-dual algorithms for maximizing ad-auctions revenue. In: Arge, L., Hoffmann, M., Welzl, E. (eds.) ESA 2007. LNCS, vol. 4698, pp. 253–264. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75520-3_24
Google Ads Help Center: About automated bidding. https://support.google.com/google-ads/answer/2979071. Accessed 12 Feb 2019
Google Ads Help Center: About universal app campaigns. https://support.google.com/google-ads/answer/6247380. Accessed Feb 13 2019
Charles, D.X., Chakrabarty, D., Chickering, M., Devanur, N.R., Wang, L.: Budget smoothing for internet ad auctions: a game theoretic approach. In: Proceedings of the Fourteenth ACM Conference on Electronic Commerce, EC 2013, Philadelphia, PA, USA, 16–20 June 2013, pp. 163–180 (2013)
Chen, P., et al.: Ad serving using a compact allocation plan. In: Proceedings of the 13th ACM Conference on Electronic Commerce, EC 2012, Valencia, Spain, 4–8 June 2012, pp. 319–336 (2012)
Chen, Y., Berkhin, P., Anderson, B., Devanur, N.R.: Real-time bidding algorithms for performance-based display ad allocation. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, 21–24 August 2011, pp. 1307–1315 (2011)
Devanur, N.R., Jain, K., Sivan, B., Wilkens, C.A.: Near optimal online algorithms and fast approximation algorithms for resource allocation problems. J. ACM 66(1), 7:1–7:41 (2019). https://doi.org/10.1145/3284177
Dobzinski, S., Leme, R.P.: Efficiency guarantees in auctions with budgets. In: Esparza, J., Fraigniaud, P., Husfeldt, T., Koutsoupias, E. (eds.) ICALP 2014, Part I. LNCS, vol. 8572, pp. 392–404. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-43948-7_33
Feldman, J., Korula, N., Mirrokni, V., Muthukrishnan, S., Pál, M.: Online ad assignment with free disposal. In: Leonardi, S. (ed.) WINE 2009. LNCS, vol. 5929, pp. 374–385. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-10841-9_34
Feldman, J., Muthukrishnan, S., Pál, M., Stein, C.: Budget optimization in search-based advertising auctions. In: Proceedings 8th ACM Conference on Electronic Commerce (EC-2007), San Diego, California, USA, 11–15 June 2007, pp. 40–49 (2007)
Ghosh, A., Rubinstein, B.I.P., Vassilvitskii, S., Zinkevich, M.: Adaptive bidding for display advertising. In: Proceedings of the 18th International Conference on World Wide Web, WWW 2009, Madrid, Spain, 20–24 April 2009, pp. 251–260 (2009)
Karande, C., Mehta, A., Srikant, R.: Optimizing budget constrained spend in search advertising. In: Sixth ACM International Conference on Web Search and Data Mining, WSDM 2013, Rome, Italy, 4–8 February 2013, pp. 697–706 (2013)
Mehta, A.: Online matching and ad allocation. Found. Trends® Theor. Comput. Sci. 8(4), 265–368 (2013)
Mehta, A., Saberi, A., Vazirani, U., Vazirani, V.: Adwords and generalized online matching. J. ACM 54(5), 22 (2007)
Plotkin, S.A., Shmoys, D.B., Tardos, É.: Fast approximation algorithms for fractional packing and covering problems. In: 32nd Annual Symposium on Foundations of Computer Science, San Juan, Puerto Rico, 1–4 October 1991, pp. 495–504 (1991)
Ren, K., Zhang, W., Chang, K., Rong, Y., Yu, Y., Wang, J.: Bidding machine: learning to bid for directly optimizing profits in display advertising. IEEE Trans. Knowl. Data Eng. 30(4), 645–659 (2018)
Zhang, W., Yuan, S., Wang, J.: Optimal real-time bidding for display advertising. In: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, New York, NY, USA, 24–27 August 2014, pp. 1077–1086 (2014)
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Aggarwal, G., Badanidiyuru, A., Mehta, A. (2019). Autobidding with Constraints. In: Caragiannis, I., Mirrokni, V., Nikolova, E. (eds) Web and Internet Economics. WINE 2019. Lecture Notes in Computer Science(), vol 11920. Springer, Cham. https://doi.org/10.1007/978-3-030-35389-6_2
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DOI: https://doi.org/10.1007/978-3-030-35389-6_2
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