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Dynamic Pay-Per-Action Mechanisms and Applications to Online Advertising

Author

Listed:
  • Hamid Nazerzadeh

    (Marshall School of Business, University of Southern California, Los Angeles, California 94305)

  • Amin Saberi

    (Management Science and Engineering Department, Stanford University, Stanford, California 94305)

  • Rakesh Vohra

    (Kellogg School of Management, Northwestern University, Evanston, Illinois 60208)

Abstract
We examine the problem of allocating an item repeatedly over time amongst a set of agents. The value that each agent derives from consumption of the item may vary over time. Furthermore, it is private information to the agent, and prior to consumption it may be unknown to that agent. We describe a mechanism based on a sampling-based learning algorithm that under suitable assumptions is asymptotically individually rational, asymptotically Bayesian incentive compatible, and asymptotically ex ante efficient. Our mechanism can be interpreted as a pay-per-action or pay-per-acquisition (PPA) charging scheme in online advertising. In this scheme, instead of paying per click, advertisers pay only when a user takes a specific action (e.g., purchases an item or fills out a form) on their websites.

Suggested Citation

  • Hamid Nazerzadeh & Amin Saberi & Rakesh Vohra, 2013. "Dynamic Pay-Per-Action Mechanisms and Applications to Online Advertising," Operations Research, INFORMS, vol. 61(1), pages 98-111, February.
    • Handle: RePEc:inm:oropre:v:61:y:2013:i:1:p:98-111
    DOI: 10.1287/opre.1120.1124
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    References listed on IDEAS

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    3. Sham M. Kakade & Ilan Lobel & Hamid Nazerzadeh, 2013. "Optimal Dynamic Mechanism Design and the Virtual-Pivot Mechanism," Operations Research, INFORMS, vol. 61(4), pages 837-854, August.
    4. Krishnamurthy Iyer & Ramesh Johari & Mukund Sundararajan, 2014. "Mean Field Equilibria of Dynamic Auctions with Learning," Management Science, INFORMS, vol. 60(12), pages 2949-2970, December.
    5. Ensthaler, Ludwig & Giebe, Thomas, 2014. "Bayesian optimal knapsack procurement," European Journal of Operational Research, Elsevier, vol. 234(3), pages 774-779.
    6. Tao Zhang & Quanyan Zhu, 2022. "On Incentive Compatibility in Dynamic Mechanism Design With Exit Option in a Markovian Environment," Dynamic Games and Applications, Springer, vol. 12(2), pages 701-745, June.
    7. Santiago R. Balseiro & Omar Besbes & Gabriel Y. Weintraub, 2019. "Dynamic Mechanism Design with Budget-Constrained Buyers Under Limited Commitment," Operations Research, INFORMS, vol. 67(3), pages 711-730, May.
    8. Shweta Jain & Satyanath Bhat & Ganesh Ghalme & Divya Padmanabhan & Y. Narahari, 2016. "Mechanisms with learning for stochastic multi-armed bandit problems," Indian Journal of Pure and Applied Mathematics, Springer, vol. 47(2), pages 229-272, June.
    9. Shivam Gupta & Wei Chen & Milind Dawande & Ganesh Janakiraman, 2023. "Three Years, Two Papers, One Course Off: Optimal Nonmonetary Reward Policies," Management Science, INFORMS, vol. 69(5), pages 2852-2869, May.
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    11. Richet, Jean-Loup, 2022. "How cybercriminal communities grow and change: An investigation of ad-fraud communities," Technological Forecasting and Social Change, Elsevier, vol. 174(C).

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