Multi-Platform Autobidding with and without Predictions
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912248353521664 |
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| author | Aggarwal, Gagan Gupta, Anupam Tan, Xizhi Zhao, Mingfei |
| author_facet | Aggarwal, Gagan Gupta, Anupam Tan, Xizhi Zhao, Mingfei |
| contents | We study the problem of finding the optimal bidding strategy for an advertiser in a multi-platform auction setting. The competition on a platform is captured by a value and a cost function, mapping bidding strategies to value and cost respectively. We assume a diminishing returns property, whereby the marginal cost is increasing in value. The advertiser uses an autobidder that selects a bidding strategy for each platform, aiming to maximize total value subject to budget and return-on-spend constraint. The advertiser has no prior information and learns about the value and cost functions by querying a platform with a specific bidding strategy. Our goal is to design algorithms that find the optimal bidding strategy with a small number of queries.
We first present an algorithm that requires \(O(m \log (mn) \log n)\) queries, where $m$ is the number of platforms and $n$ is the number of possible bidding strategies in each platform. Moreover, we adopt the learning-augmented framework and propose an algorithm that utilizes a (possibly erroneous) prediction of the optimal bidding strategy. We provide a $O(m \log (mη) \log η)$ query-complexity bound on our algorithm as a function of the prediction error $η$. This guarantee gracefully degrades to \(O(m \log (mn) \log n)\). This achieves a ``best-of-both-worlds'' scenario: \(O(m)\) queries when given a correct prediction, and \(O(m \log (mn) \log n)\) even for an arbitrary incorrect prediction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_19317 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Platform Autobidding with and without Predictions Aggarwal, Gagan Gupta, Anupam Tan, Xizhi Zhao, Mingfei Computer Science and Game Theory We study the problem of finding the optimal bidding strategy for an advertiser in a multi-platform auction setting. The competition on a platform is captured by a value and a cost function, mapping bidding strategies to value and cost respectively. We assume a diminishing returns property, whereby the marginal cost is increasing in value. The advertiser uses an autobidder that selects a bidding strategy for each platform, aiming to maximize total value subject to budget and return-on-spend constraint. The advertiser has no prior information and learns about the value and cost functions by querying a platform with a specific bidding strategy. Our goal is to design algorithms that find the optimal bidding strategy with a small number of queries. We first present an algorithm that requires \(O(m \log (mn) \log n)\) queries, where $m$ is the number of platforms and $n$ is the number of possible bidding strategies in each platform. Moreover, we adopt the learning-augmented framework and propose an algorithm that utilizes a (possibly erroneous) prediction of the optimal bidding strategy. We provide a $O(m \log (mη) \log η)$ query-complexity bound on our algorithm as a function of the prediction error $η$. This guarantee gracefully degrades to \(O(m \log (mn) \log n)\). This achieves a ``best-of-both-worlds'' scenario: \(O(m)\) queries when given a correct prediction, and \(O(m \log (mn) \log n)\) even for an arbitrary incorrect prediction. |
| title | Multi-Platform Autobidding with and without Predictions |
| topic | Computer Science and Game Theory |
| url | https://arxiv.org/abs/2502.19317 |