Autobidding with Constraints

Aggarwal, Gagan; Badanidiyuru, Ashwinkumar; Mehta, Aranyak

doi:10.1007/978-3-030-35389-6_2

Cited by 43 publications

(87 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast to works assuming utility-maximizing agents, a growing body of work has recently focused on auto-bidders such as target CPA bidders and target ROAS bidders. Aggarwal et al [2019] nd the optimal bidding strategies for a general class of auto-bidding objectives and prove the existence of pure-strategy equilibrium. Deng et al [2021b] show how boosts can be used to improve the e ciency guarantees when target CPA and target ROAS auto-bidders coexist.…”

Section: Related Workmentioning

confidence: 99%

“…As auto-bidding-the practice of using optimization algorithms to procure advertising slots-is becoming the prevalent option in online advertising, a growing body of work is revisiting auction theory from the lens of the auto-bidding world [Aggarwal et al, 2019, Babaio et al, 2021, Balseiro et al, 2021a, Deng et al, 2021b, Golrezaei et al, 2021c. A bene t of auto-bidding is that it simpli es advertisers' bidding process by asking advertisers for their high-level goals and then bidding on behalf of the advertisers.…”

Section: Introductionmentioning

confidence: 99%

“…One surprising observation when agents are value maximizers is that the Vickrey-Clarke-Groves (VCG) auction, which is truthful and e cient for utility maximizers, is no longer truthful nor e cient. Value maximizers have incentives to strategize their bids and, moreover, there exist auction instances where the social welfare at equilibria under the VCG auction is only 1/2 of the optimal social welfare [Aggarwal et al, 2019, Deng et al, 2021b. This is because the ratio between bids and values can be very di erent for each value-maximizing agent, and an agent with a low value may end up outbidding other bidders with high values in some auctions, creating allocation ine ciency.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust Auction Design in the Auto-bidding World

Balseiro¹,

Deng²,

Mao³

et al. 2021

Preprint

View full text Add to dashboard Cite

In classic auction theory, reserve prices are known to be e ective for improving revenue for the auctioneer against quasi-linear utility maximizing bidders. The introduction of reserve prices, however, usually do not help improve total welfare of the auctioneer and the bidders. In this paper, we focus on value maximizing bidders with return on spend constraints-a paradigm that has drawn considerable attention recently as more advertisers adopt auto-bidding algorithms in advertising platforms-and show that the introduction of reserve prices has a novel impact on the market. Namely, by choosing reserve prices appropriately the auctioneer can improve not only the total revenue but also the total welfare. Our results also demonstrate that reserve prices are robust to bidder types, i.e., reserve prices work well for di erent bidder types, such as value maximizers and utility maximizers, without using bidder type information. We generalize these results for a variety of auction mechanisms such as VCG, GSP, and rst-price auctions. Moreover, we show how to combine these results with additive boosts to improve the welfare of the outcomes of the auction further. Finally, we complement our theoretical observations with an empirical study con rming the e ectiveness of these ideas using data from online advertising auctions.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Auction Design in the Auto-bidding World

Balseiro¹,

Deng²,

Mao³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…To learn the bidding strategies for auto-bidding agents, a natural option, also widely adopted in real-time-bidding (RTB) literature [1,31], is to solve an isolated optimization problem for each auto-bidding agent, and the effects of other agents are implicitly encoded within the auction environment. However, this formulation ignores the fact that the ad auction mechanism is inherently a distributed multi-agent system in nature -the outcome of an ad auction depends heavily on the bidding behaviors of all the involved auto-bidding agents [14].…”

Section: Introductionmentioning

confidence: 99%

“…Extensive offline and online experiments demonstrate the effectiveness of our MAAB in terms of both social welfare and platform's revenue. 1 The contributions in this paper can be summarized as follows:…”

Section: Introductionmentioning

confidence: 99%

A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising

Chao¹,

Xu²,

Zhang³

et al. 2022

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

View full text Add to dashboard Cite

In online advertising, auto-bidding has become an essential tool for advertisers to optimize their preferred ad performance metrics by simply expressing high-level campaign objectives and constraints. Previous works designed auto-bidding tools from the view of singleagent, without modeling the mutual influence between agents. In this paper, we instead consider this problem from a distributed multi-agent perspective, and propose a general Multi-Agent reinforcement learning framework for Auto-Bidding, namely MAAB, to learn the auto-bidding strategies. First, we investigate the competition and cooperation relation among auto-bidding agents, and propose a temperature-regularized credit assignment to establish a mixed cooperative-competitive paradigm. By carefully making a competition and cooperation trade-off among agents, we can reach an equilibrium state that guarantees not only individual advertiser's utility but also the system performance (i.e., social welfare). Second, to avoid the potential collusion behaviors of bidding low prices underlying the cooperation, we further propose bar agents to set a personalized bidding bar for each agent, and then alleviate the revenue degradation due to the cooperation. Third, to deploy MAAB in the large-scale advertising system with millions of advertisers, we propose a mean-field approach. By grouping advertisers with the same objective as a mean auto-bidding agent, the interactions among the large-scale advertisers are greatly simplified, making it practical to train MAAB efficiently. Extensive experiments on the

show abstract