Multi-Platform Autobidding with and without Predictions
Gagan Aggarwal, Anupam Gupta, Xizhi Tan, Mingfei Zhao

TL;DR
This paper develops query-efficient algorithms for multi-platform autobidding strategies, leveraging predictions to improve learning speed while maintaining robustness against prediction errors.
Contribution
It introduces algorithms that optimize bidding strategies with minimal queries, incorporating a learning-augmented approach that adapts to prediction accuracy.
Findings
Query complexity is reduced to O(m) with correct predictions.
Algorithm maintains near-optimal complexity even with inaccurate predictions.
Proposes a robust framework balancing learning speed and prediction errors.
Abstract
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 is the number of platforms and is the number of…
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Taxonomy
TopicsAuction Theory and Applications · Consumer Market Behavior and Pricing · Complexity and Algorithms in Graphs
