IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2301.13414.html

Incentive Compatibility in the Auto-bidding World

Author

Listed:
  • Yeganeh Alimohammadi
  • Aranyak Mehta
  • Andres Perlroth

Abstract

Auto-bidding has recently become a popular feature in ad auctions. This feature enables advertisers to simply provide high-level constraints and goals to an automated agent, which optimizes their auction bids on their behalf. In this paper, we examine the effect of different auctions on the incentives of advertisers to report their constraints to the auto-bidder intermediaries. More precisely, we study whether canonical auctions such as first price auction (FPA) and second price auction (SPA) are auto-bidding incentive compatible (AIC): whether an advertiser can gain by misreporting their constraints to the autobidder. We consider value-maximizing advertisers in two important settings: when they have a budget constraint and when they have a target cost-per-acquisition constraint. The main result of our work is that for both settings, FPA and SPA are not AIC. This contrasts with FPA being AIC when auto-bidders are constrained to bid using a (sub-optimal) uniform bidding policy. We further extend our main result and show that any (possibly randomized) auction that is truthful (in the classic profit-maximizing sense), scalar invariant and symmetric is not AIC. Finally, to complement our findings, we provide sufficient market conditions for FPA and SPA to become AIC for two advertisers. These conditions require advertisers' valuations to be well-aligned. This suggests that when the competition is intense for all queries, advertisers have less incentive to misreport their constraints. From a methodological standpoint, we develop a novel continuous model of queries. This model provides tractability to study equilibrium with auto-bidders, which contrasts with the standard discrete query model, which is known to be hard. Through the analysis of this model, we uncover a surprising result: in auto-bidding with two advertisers, FPA and SPA are auction equivalent.

Suggested Citation

  • Yeganeh Alimohammadi & Aranyak Mehta & Andres Perlroth, 2023. "Incentive Compatibility in the Auto-bidding World," Papers 2301.13414, arXiv.org, revised May 2024.
    • Handle: RePEc:arx:papers:2301.13414
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2301.13414
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Amine Allouah & Omar Besbes, 2020. "Prior-Independent Optimal Auctions," Management Science, INFORMS, vol. 66(10), pages 4417-4432, October.
    2. Santiago R. Balseiro & Yonatan Gur, 2019. "Learning in Repeated Auctions with Budgets: Regret Minimization and Equilibrium," Management Science, INFORMS, vol. 65(9), pages 3952-3968, September.
    3. Aranyak Mehta & Andres Perlroth, 2023. "Auctions without commitment in the auto-bidding world," Papers 2301.07312, arXiv.org, revised Mar 2023.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yiding Feng & Brendan Lucier & Aleksandrs Slivkins, 2023. "Strategic Budget Selection in a Competitive Autobidding World," Papers 2307.07374, arXiv.org, revised Nov 2023.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yiding Feng & Brendan Lucier & Aleksandrs Slivkins, 2023. "Strategic Budget Selection in a Competitive Autobidding World," Papers 2307.07374, arXiv.org, revised Nov 2023.
    2. Luofeng Liao & Christian Kroer, 2024. "Statistical Inference and A/B Testing in Fisher Markets and Paced Auctions," Papers 2406.15522, arXiv.org, revised Mar 2025.
    3. Çağıl Koçyiğit & Daniel Kuhn & Napat Rujeerapaiboon, 2024. "Regret Minimization and Separation in Multi-Bidder, Multi-Item Auctions," INFORMS Journal on Computing, INFORMS, vol. 36(6), pages 1543-1561, December.
    4. Dirk Bergemann & Alessandro Bonatti & Nicholas Wu, 2025. "How Do Digital Advertising Auctions Impact Product Prices?," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 92(4), pages 2330-2358.
    5. Bergemann, Dirk & Bonatti, Alessandro & Wu, Nicholas, 2025. "Bidding with budgets: Data-driven bid algorithms in digital advertising," International Journal of Industrial Organization, Elsevier, vol. 102(C).
    6. Yoav Kolumbus & Joe Halpern & 'Eva Tardos, 2024. "Games with Payments between Learning Agents," Papers 2405.20880, arXiv.org, revised Feb 2026.
    7. Carl F. Mela & Jason M. T. Roos & Tulio Sousa, 2023. "Advertiser Learning in Direct Advertising Markets," Papers 2307.07015, arXiv.org, revised Jul 2025.
    8. Erdmann, Anett & Arilla, Ramón & Ponzoa, José M., 2022. "Search engine optimization: The long-term strategy of keyword choice," Journal of Business Research, Elsevier, vol. 144(C), pages 650-662.
    9. Vincent Conitzer & Christian Kroer & Debmalya Panigrahi & Okke Schrijvers & Nicolas E. Stier-Moses & Eric Sodomka & Christopher A. Wilkens, 2022. "Pacing Equilibrium in First Price Auction Markets," Management Science, INFORMS, vol. 68(12), pages 8515-8535, December.
    10. Han, Jun & Weber, Thomas A., 2023. "Price discrimination with robust beliefs," European Journal of Operational Research, Elsevier, vol. 306(2), pages 795-809.
    11. Mengzhou Zhuang & Eric (Er) Fang & Jongkuk Lee & Xiaoling Li, 2021. "The Effects of Price Rank on Clicks and Conversions in Product List Advertising on Online Retail Platforms," Information Systems Research, INFORMS, vol. 32(4), pages 1412-1430, December.
    12. Yurong Chen & Qian Wang & Zhijian Duan & Haoran Sun & Zhaohua Chen & Xiang Yan & Xiaotie Deng, 2023. "Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets," Papers 2306.07709, arXiv.org.
    13. Hana Choi & Carl F. Mela & Santiago R. Balseiro & Adam Leary, 2020. "Online Display Advertising Markets: A Literature Review and Future Directions," Information Systems Research, INFORMS, vol. 31(2), pages 556-575, June.
    14. Zhu Mingxi & Song Michelle, 2024. "Design Information Disclosure under Bidder Heterogeneity in Online Advertising Auctions: Implications of Bid-Adherence Behavior," Papers 2410.05535, arXiv.org.
    15. Zikun Ye & Dennis J. Zhang & Heng Zhang & Renyu Zhang & Xin Chen & Zhiwei Xu, 2023. "Cold Start to Improve Market Thickness on Online Advertising Platforms: Data-Driven Algorithms and Field Experiments," Management Science, INFORMS, vol. 69(7), pages 3838-3860, July.
    16. Shixin Wang, 2024. "Multi-Item Screening with a Maximin-Ratio Objective," Papers 2408.13580, arXiv.org, revised Oct 2025.
    17. Briand, Martin & Franklin, Rod & Lafkihi, Mariam, 2022. "A dynamic routing protocol with payments for the Physical Internet: A simulation with learning agents," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    18. Yifan Huang & Dong Hao & Zhiyi Fan & Yuhang Guo & Bin Li, 2025. "Approximate Revenue Maximization for Diffusion Auctions," Papers 2507.14470, arXiv.org.
    19. Zhaohua Chen & Mingwei Yang & Chang Wang & Jicheng Li & Zheng Cai & Yukun Ren & Zhihua Zhu & Xiaotie Deng, 2022. "Budget-Constrained Auctions with Unassured Priors: Strategic Equivalence and Structural Properties," Papers 2203.16816, arXiv.org, revised Feb 2024.
    20. Luofeng Liao & Christian Kroer, 2023. "Statistical Inference and A/B Testing for First-Price Pacing Equilibria," Papers 2301.02276, arXiv.org, revised Jun 2023.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2301.13414. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.