Revenue in First- and Second-Price Display Advertising Auctions: Understanding Markets with Learning Agents

Published Online:https://doi.org/10.1287/isre.2025.2160

References

  • Abada I, Lambin X (2023) Artificial intelligence: Can seemingly collusive outcomes be avoided? Management Sci. 69(9):5042–5065.LinkGoogle Scholar
  • Aggarwal G, Badanidiyuru A, Mehta A (2019) Autobidding with constraints. Caragiannis I, Mirrokni VS, Nikolova E, eds. Web Internet Econom. 15th Internat. Conf. WINE 2019 (Springer, Cham, Switzerland), 17–30.Google Scholar
  • Aggarwal G, Badanidiyuru A, Balseiro SR, Bhawalkar K, Deng Y, Feng Z, Goel G, et al. (2024) Auto-bidding and auctions in online advertising: A survey. ACM SIGecom Exchanges 22(1):159–183.Google Scholar
  • Ai R, Wang C, Li C, Zhang J, Huang W, Deng X (2022) No-regret learning in repeated first-price auctions with budget constraints. Preprint, submitted May 29, https://arxiv.org/abs/2205.14572.Google Scholar
  • Alcobendas M, Zeithammer R (2021) Adjustment of bidding strategies after a switch to first-price rules. Pennock DM, Segal I, Seuken S, eds. Proc. 23rd ACM Conf. Econom. Comput. (Association for Computing Machinery, New York), 296.Google Scholar
  • Andrade GP, Frongillo R, Piliouras G (2021) Learning in matrix games can be arbitrarily complex. Proc. Machine Learn. Res. 134:159–185.Google Scholar
  • Asker J, Fershtman C, Pakes A (2022) Artificial intelligence, algorithm design, and pricing. AEA Papers Proc. 112:452–456.CrossrefGoogle Scholar
  • Balocco F, Lu Y, Li T, Gupta A (2025) Lemon ads: Adverse selection in multichannel display advertising markets. Management Sci. 71(12):10244–10260.LinkGoogle Scholar
  • Banchio M, Skrzypacz A (2022) Artificial intelligence and auction design. Pennock DM, Segal I, Seuken S, eds. Proc. 23rd ACM Conf. Econom. Comput. (Association for Computing Machinery, New York), 30–31.Google Scholar
  • Bichler M, Paulsen P (2018) A principal-agent model of bidding firms in multi-unit auctions. Games Econom. Behav. 111:20–40.CrossrefGoogle Scholar
  • Bichler M, Fichtl M, Oberlechner M (2025) Computing Bayes–Nash equilibrium strategies in auction games via simultaneous online dual averaging. Oper. Res. 73(2):1102–1127.LinkGoogle Scholar
  • Bichler M, Gupta A, Ketter W (2010) Research commentary—Designing smart markets. Inform. Systems Res. 21(4):688–699.LinkGoogle Scholar
  • Blume A, Heidhues P (2008) Modeling tacit collusion in auctions. J. Institutional Theoret. Econom. 164(1):163–184.CrossrefGoogle Scholar
  • Borgs C, Chayes J, Immorlica N, Jain K, Etesami O, Mahdian M (2007) Dynamics of bid optimization in online advertisement auctions. Williamson CL, Zurko ME, Patel-Schneider P, Shenoy P, eds. Proc. 16th Internat. Conf. World Wide Web (Association for Computing Machinery, New York), 531–540.Google Scholar
  • Calvano E, Calzolari G, Denicolò V, Pastorello S (2020) Artificial intelligence, algorithmic pricing, and collusion. Amer. Econom. Rev. 110(10):3267–3297.CrossrefGoogle Scholar
  • Cason TN, Kannan KN, Siebert R (2011) An experimental study of information revelation policies in sequential auctions. Management Sci. 57(4):667–688.LinkGoogle Scholar
  • Chawla S, Hartline JD (2013) Auctions with unique equilibria. Kearns M, McAfee P, Tardos É, eds. Proc. Fourteenth ACM Conf. Electronic Commerce (Association for Computing Machinery, New York), 181–196.Google Scholar
  • Chen X, Kroer C, Kumar R (2024) The complexity of pacing for second-price auctions. Math. Oper. Res. 49(4):2109–2135.LinkGoogle Scholar
  • Choi H, Mela CF (2018) Display advertising pricing in exchange markets. Working paper, Fuqua School of Business, Duke University, Durham, NC.Google Scholar
  • Choi H, Mela CF, Balseiro SR, Leary A (2020) Online display advertising markets: A literature review and future directions. Inform. Systems Res. 31(2):556–575.LinkGoogle Scholar
  • Christopher RM, Park S, Han SP, Kim MK (2022) Bypassing performance optimizers of real time bidding systems in display ad valuation. Inform. Systems Res. 33(2):399–412.LinkGoogle Scholar
  • Ciocan DF, Iyer K (2021) Tractable equilibria in sponsored search with endogenous budgets. Oper. Res. 69(1):227–244.LinkGoogle Scholar
  • Conitzer V, Kroer C, Sodomka E, Stier-Moses NE (2022) Multiplicative pacing equilibria in auction markets. Oper. Res. 70(2):963–989.LinkGoogle Scholar
  • den Boer AV, Meylahn J, Schinkel MP (2026) Artificial collusion: Examining supracompetitive pricing by Q-learning algorithms. Management Sci., ePub ahead of print June 9, https://doi.org/10.1287/mnsc.2024.08557.Google Scholar
  • Despotakis S, Ravi R, Sayedi A (2021) First-price auctions in online display advertising. J. Marketing Res. 58(5):888–907.CrossrefGoogle Scholar
  • Edelman B, Ostrovsky M, Schwarz M (2007) Internet advertising and the generalized second-price auction: Selling billions of dollars worth of keywords. Amer. Econom. Rev. 97(1):242–259.CrossrefGoogle Scholar
  • EMarketer, Feger A (2022) Programmatic advertising in 2022: Digital display ads industry. Accessed June 10, 2026, https://www.insiderintelligence.com/insights/programmatic-digital-display-ad-spending/.Google Scholar
  • Fabra N (2003) Tacit collusion in repeated auctions: Uniform versus discriminatory. J. Indust. Econom. 51(3):271–293.CrossrefGoogle Scholar
  • Fibich G, Gavish N (2011) Numerical simulations of asymmetric first-price auctions. Games Econom. Behav. 73(2):479–495.CrossrefGoogle Scholar
  • Geradin D, Katsifis D (2019) An EU competition law analysis of online display advertising in the programmatic age. Eur. Competition J. 15(1):55–96.CrossrefGoogle Scholar
  • Gilboa I, Postlewaite A, Samuelson L, Schmeidler D (2014) Economic models as analogies. Econom. J. 124(578):F513–F533.Google Scholar
  • Goke S, Weintraub GY, Mastromonaco R, Seljan S (2021) Bidders’ responses to auction format change in internet display advertising auctions. Pennock DM, Segal I, Seuken S, eds. Proc. 23rd ACM Conf. Econom. Comput. (Association for Computing Machinery, New York), 295.Google Scholar
  • Greenwald AR, Kephart JO (2000) Shopbots and pricebots. Moukas A, Ygge F, Sierra C, eds. Agent Mediated Electronic Commerce II. AMEC 1999, Lecture Notes in Computer Science, vol. 1788 (Springer, Berlin), 1–23.CrossrefGoogle Scholar
  • Greenwald A, Kannan K, Krishnan R (2010) On evaluating information revelation policies in procurement auctions: A Markov decision process approach. Inform. Systems Res. 21(1):15–36.LinkGoogle Scholar
  • Han Y, Zhou Z, Weissman T (2020) Optimal no-regret learning in repeated first-price auctions. Oper. Res. 73(1):209–238.Google Scholar
  • Hansen KT, Misra K, Pai MM (2021) Frontiers: Algorithmic collusion: Supra-competitive prices via independent algorithms. Marketing Sci. 40(1):1–12.LinkGoogle Scholar
  • Hartline J, Syrgkanis V, Tardos E (2015) No-regret learning in Bayesian games. Cortes C, Lawrence ND, Lee DD, Sugiyama M, Garnett R, eds. Adv. Neural Inform. Processing Systems, vol. 28 (Curran Associates Inc., Red Hook, NY), 3061–3069.Google Scholar
  • Hettich M (2021) Algorithmic collusion: Insights from deep learning. Preprint, submitted November 24, http://dx.doi.org/10.2139/ssrn.3785966.Google Scholar
  • Jin J, Song C, Li H, Gai K, Wang J, Zhang W (2018) Real-time bidding with multi-agent reinforcement learning in display advertising. Cuzzocrea A, Allan J, Paton N, Srivastava D, Agrawal R, Broder A, Zaki M, et al., eds. Proc. 27th ACM Internat. Conf. Inform. Knowledge Management (Association for Computing Machinery, New York), 2193–2201.Google Scholar
  • Klein T (2021) Autonomous algorithmic collusion: Q-learning under sequential pricing. RAND J. Econom. 52(3):538–558.CrossrefGoogle Scholar
  • Krishna V (2009) Auction Theory (Academic Press, Burlington, MA).Google Scholar
  • Kumar R, Schneider J, Sivan B (2024) Strategically-robust learning algorithms for bidding in first-price auctions. Bergemann D, Kleinberg R, Saban D, eds. Proc. 25th ACM Conf. Econom. Comput. (Association for Computing Machinery, New York), 893.Google Scholar
  • Lattimore T, Szepesvári C (2020) Bandit Algorithms (Cambridge University Press, Cambridge, UK).CrossrefGoogle Scholar
  • Liang JCN, Lu H, Zhou B (2023) Online ad procurement in non-stationary autobidding worlds. Oh A, Naumann T, Globerson A, Saenko K, Hardt M, Levine S, eds. Adv. Neural Inform. Processing Systems, vol. 36 (Curran Associates, Red Hook, NY), 42552–42575.Google Scholar
  • Mehta S, Dawande M, Janakiraman G, Mookerjee V (2020) Sustaining a good impression: Mechanisms for selling partitioned impressions at ad exchanges. Inform. Systems Res. 31(1):126–147.LinkGoogle Scholar
  • Milgrom PR, Weber RJ (1985) Distributional strategies for games with incomplete information. Math. Oper. Res. 10(4):619–632.LinkGoogle Scholar
  • Myerson RB (1981) Optimal auction design. Math. Oper. Res. 6(1):58–73.LinkGoogle Scholar
  • Nekipelov D, Syrgkanis V, Tardos E (2015) Econometrics for learning agents. Roughgarden T, Feldman M, Schwarz M, eds. Proc. Sixteenth ACM Conf. Econom. Comput. (Association for Computing Machinery, New York), 1–18.Google Scholar
  • Noti G, Syrgkanis V (2021) Bid prediction in repeated auctions with learning. Leskovec J, Grobelnik M, Najork M, Tang J, Zia L, eds. Proc. Web Conf. 2021 (Association for Computing Machinery, New York), 3953–3964.Google Scholar
  • OECD (2017) Algorithms and collusion: Competition policy in the digital age. OECD Roundtables on Competition Policy Papers, No. 206, OECD Publishing, Paris.Google Scholar
  • Pinker EJ, Seidmann A, Vakrat Y (2003) Managing online auctions: Current business and research issues. Management Sci. 49(11):1457–1484.LinkGoogle Scholar
  • Pošćić A, Martinović A (2020) EU competition law in the digital era: Algorithmic collusion as a regulatory challenge. EU Comparative Law Issues Challenges Series 4, 1016–1039.Google Scholar
  • Sanders JBT, Farmer JD, Galla T (2018) The prevalence of chaotic dynamics in games with many players. Sci. Rep. 8(1):4902.CrossrefGoogle Scholar
  • Sayedi A (2018) Real-time bidding in online display advertising. Marketing Sci. 37(4):553–568.LinkGoogle Scholar
  • Shalev-Shwartz S (2011) Online learning and online convex optimization. Foundations Trends Machine Learn. 4(2):107–194. CrossrefGoogle Scholar
  • Skrzypacz A, Hopenhayn H (2004) Tacit collusion in repeated auctions. J. Econom. Theory 114(1):153–169.CrossrefGoogle Scholar
  • Szymanski BK, Lee JS (2006) Impact of ROI on bidding and revenue in sponsored search advertisement auctions. Second Workshop Sponsored Search Auctions.Google Scholar
  • Tilli T, Espinosa-Leal L (2021) Multi-armed bandits for bid shading in first-price real-time bidding auctions. J. Intelligent Fuzzy Systems 41(6):6111–6125.Google Scholar
  • Tunuguntla S, Hoban PR (2021) A near-optimal bidding strategy for real-time display advertising auctions. J. Marketing Res. 58(1):1–21.CrossrefGoogle Scholar
  • Vickrey W (1961) Counterspeculation, auctions, and competitive sealed tenders. J. Finance 16(1):8–37.CrossrefGoogle Scholar
  • Wang Q, Yang Z, Deng X, Kong Y (2023) Learning to bid in repeated first-price auctions with budgets. Krause A, Brunskill E, Cho K, Engelhardt B, Sabato S, Scarlett J, eds. Internat. Conf. Machine Learn. (PMLR, New York), 36494–36513.Google Scholar
  • Wilkens CA, Cavallo R, Niazadeh R (2017) GSP: The Cinderella of mechanism design. Barrett R, Cummings R, Agichtein E, Gabrilovich E, eds. Proc. 26th Internat. Conf. World Wide Web (International World Wide Web Conferences Steering Committee, Geneva), 25–32.Google Scholar
  • Wilson R (1985) Game-Theoretic Analyses of Trading Processes (Institute for Mathematical Studies in the Social Sciences, Stanford University, Stanford, CA).Google Scholar
  • Yuan Y, Wang F, Li J, Qin R (2014) A survey on real time bidding advertising. Proc. 2014 IEEE Internat. Conf. Service Oper. Logist. Informatics (IEEE, New York), 418–423.Google Scholar
  • Zhang M, Luo H (2023) Online learning in contextual second-price pay-per-click auctions. Dasgupta S, Mandt S, Li Y, eds. Proc. 27th Internat. Conf. Artificial Intelligence Statist. (PMLR, New York), 2395–2403.Google Scholar
  • Zhang W, Han Y, Zhou Z, Flores A, Weissman T (2022) Leveraging the hints: Adaptive bidding in repeated first-price auctions. Koyejo S, Mohamed S, Agarwal A, Belgrave D, Cho K, Oh A, eds. Adv. Neural Inform. Processing Systems, vol. 35 (Curran Associates Inc., Red Hook, NY), 21329–21341.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.