Mature internet advertising platforms offer high-level campaign management tools to help advertisers run their campaigns, often abstracting away the intricacies of how each ad is placed and focusing on aggregate metrics of interest to advertisers. On such platforms, advertisers often participate in auctions through a proxy bidder, so the standard incentive analyses that are common in the literature do not apply directly. In this paper, we take the perspective of a budget management system that surfaces aggregated incentives—instead of individual auctions—and compare first and second price auctions. We show that theory offers surprising endorsement for using a first price auction to sell individual impressions. In particular, first price auctions guarantee uniqueness of the steady-state equilibrium of the budget management system, monotonicity, and other desirable properties, as well as efficient computation through the solution to the well-studied Eisenberg–Gale convex program. Contrary to what one can expect from first price auctions, we show that incentives issues are not a barrier that undermines the system. Using realistic instances generated from data collected at real-world auction platforms, we show that bidders have small regret with respect to their optimal ex post strategy, and they do not have a big incentive to misreport when they can influence equilibria directly by giving inputs strategically. Finally, budget-constrained bidders, who have significant prevalence in real-world platforms, tend to have smaller regrets. Our computations indicate that bidder budgets, pacing multipliers, and regrets all have a positive association in statistical terms. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Funding: D. Panigrahi was supported in part by the National Science Foundation [Awards CCF 1535972, CCF 1750140, and CCF 1955703]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.4310 .
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Contextual Standard Auctions with Budgets: Revenue Equivalence and Efficiency Guarantees
The internet advertising market is a multibillion dollar industry in which advertisers buy thousands of ad placements every day by repeatedly participating in auctions. An important and ubiquitous feature of these auctions is the presence of campaign budgets, which specify the maximum amount the advertisers are willing to pay over a specified time period. In this paper, we present a new model to study the equilibrium bidding strategies in standard auctions, a large class of auctions that includes first and second price auctions, for advertisers who satisfy budget constraints on average. Our model dispenses with the common yet unrealistic assumption that advertisers’ values are independent and instead assumes a contextual model in which advertisers determine their values using a common feature vector. We show the existence of a natural value pacing–based Bayes–Nash equilibrium under very mild assumptions. Furthermore, we prove a revenue equivalence showing that all standard auctions yield the same revenue even in the presence of budget constraints. Leveraging this equivalence, we prove price of anarchy bounds for liquid welfare and structural properties of pacing-based equilibria that hold for all standard auctions. In recent years, the internet advertising market has adopted first price auctions as the preferred paradigm for selling advertising slots. Our work, thus, takes an important step toward understanding the implications of the shift to first price auctions in internet advertising markets by studying how the choice of the selling mechanism impacts revenues, welfare, and advertisers’ bidding strategies. This paper was accepted by Itai Ashlagi, revenue management and market analytics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.4719 .
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- Award ID(s):
- 2147361
- NSF-PAR ID:
- 10442705
- Date Published:
- Journal Name:
- Management Science
- ISSN:
- 0025-1909
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1287/mnsc.2023.4719
