Budget Pacing in Repeated Auctions: Regret and Efficiency without Convergence

Gaitonde, Jason; Li, Yingkai; Light, Bar; Lucier, Brendan; Slivkins, Aleksandrs

doi:10.48550/arxiv.2205.08674

Cited by 4 publications

(5 citation statements)

References 20 publications

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“…4 Uniform bidding is only an optimal strategy when auctions are truthful (Aggarwal et al, 2019). Even though for FPA these strategies are suboptimal, they have gained recent attention in the literature due to their tractability Conitzer et al (2022a,b); Chen et al (2021); Gaitonde et al (2022). We show that in such a scenario, FPA with uniform bidding turns out to be AIC (Theorem 4.2).…”

Section: Resultsmentioning

confidence: 81%

“…Balseiro and Gur (2019) investigate strategies to minimize regret in simultaneous first-price auctions with learning. Gaitonde et al (2022) take this concept further by extending the approach to a wider range of auction settings. Furthermore, Golrezaei et al (2021a) examines how to effectively price and bid for advertising campaigns when advertisers have both ROI and budget constraints.…”

Section: Related Workmentioning

confidence: 99%

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Incentive Compatibility in the Auto-bidding World

Alimohammadi¹,

Mehta²,

Perlroth³

2023

Preprint

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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. These auto-bidding intermediaries interact in a decentralized manner in the underlying auctions, leading to new interesting practical and theoretical questions on auction design, for example, in understanding the bidding equilibrium properties between auto-bidder intermediaries for different auctions. 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.

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Section: Resultsmentioning

confidence: 81%

Section: Related Workmentioning

confidence: 99%

Incentive Compatibility in the Auto-bidding World

Alimohammadi¹,

Mehta²,

Perlroth³

2023

Preprint

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“…6 We note here that parallel to our work, Gaitonde et al [2022] extend the pacing techniques to a class of auction forms (called core auctions). They obtain O(T .…”

Section: Discussionmentioning

confidence: 93%

No-regret Learning in Repeated First-Price Auctions with Budget Constraints

Ai¹,

Wang²,

Li³

et al. 2022

Preprint

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Recently the online advertising market has exhibited a gradual shift from second-price auctions to first-price auctions. Although there has been a line of works concerning online bidding strategies in first-price auctions, it still remains open how to handle budget constraints in the problem. In the present paper, we initiate the study for a buyer with budgets to learn online bidding strategies in repeated first-price auctions. We propose an RL-based bidding algorithm against the optimal non-anticipating strategy under stationary competition. Our algorithm obtains O( √ T )-regret if the bids are all revealed at the end of each round. With the restriction that the buyer only sees the winning bid after each round, our modified algorithm obtains O(T 7 12 )-regret by techniques developed from survival analysis. Our analysis extends to the more general scenario where the buyer has any bounded instantaneous utility function with regrets of the same order.

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“…Kumar et al (2022) study an episodic setting and provide a density-estimation-based algorithm for learning the target expenditures for each episode. Gaitonde et al (2022) study the performance of the algorithm of Balseiro & Gur (2019) for the different objective of value maximization, and against the different benchmark comprised of pacing multipliers which spend the same amount B/T at each time period. Under the no-overbidding assumption, they show that the algorithm of Balseiro & Gur (2019) achieves O(T 3/4 ) regret.…”

Section: Putting It All Togethermentioning

confidence: 99%

“…Under the no-overbidding assumption, they show that the algorithm of Balseiro & Gur (2019) achieves O(T 3/4 ) regret. Recent years have also seen significant attention being given to pacing in multi-buyer settings, but we focus on the single-agent setting here and refer the reader to the recent works of Chen et al (2021) and Gaitonde et al (2022) for an overview.…”

Section: Putting It All Togethermentioning

confidence: 99%

Robust Budget Pacing with a Single Sample

Balseiro¹,

Kumar²,

Mirrokni³

et al. 2023

Preprint

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Major Internet advertising platforms offer budget pacing tools as a standard service for advertisers to manage their ad campaigns. Given the inherent non-stationarity in an advertiser's value and also competing advertisers' values over time, a commonly used approach is to learn a target expenditure plan that specifies a target spend as a function of time, and then run a controller that tracks this plan. This raises the question: how many historical samples are required to learn a good expenditure plan? We study this question by considering an advertiser repeatedly participating in T second-price auctions, where the tuple of her value and the highest competing bid is drawn from an unknown time-varying distribution. The advertiser seeks to maximize her total utility subject to her budget constraint. Prior work has shown the sufficiency of T log T samples per distribution to achieve the optimal O( √ T )-regret. We dramatically improve this state-of-the-art and show that just one sample per distribution is enough to achieve the near-optimal Õ( √ T )-regret, while still being robust to noise in the sampling distributions.

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Budget Pacing in Repeated Auctions: Regret and Efficiency without Convergence

Cited by 4 publications

References 20 publications

Incentive Compatibility in the Auto-bidding World

Incentive Compatibility in the Auto-bidding World

No-regret Learning in Repeated First-Price Auctions with Budget Constraints

Robust Budget Pacing with a Single Sample

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