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arXiv · 2503.10304

Large-Scale Auto-bidding with Nash Equilibrium Constraints

Zhiyu Mou·Miao Xu·Rongquan Bai·Zhuoran Yang·Chuan Yu·Jian Xu·Bo Zheng
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Abstract

Auto-bidding has become a cornerstone of modern online advertising platforms, enabling many advertisers to automate bidding at scale and optimize campaign performance. However, prevailing industrial systems rely on single-agent auto-bidding methods that are scalable but overlook the strategic interdependence among advertisers' bids, leading to unstable or suboptimal outcomes. While recent works recognize the game-theoretic nature of auto-bidding, existing approaches remain either computationally intractable at scale or lack a principled equilibrium-selection that aligns with platform-wide objectives. In this paper, we bridge this gap by introducing Nash Equilibrium-Constrained Bidding (NCB), a principled and scalable auto-bidding framework that recasts auto-bidding as a platform-wide optimization problem subject to Nash equilibrium constraints. This approach accounts for fine-grained strategic interdependencies among advertisers, ensuring both agent-level stability and ecosystem-level optimality. Notably, we develop a theoretically sound penalty-based primal-dual gradient method with rigorous convergence guarantees, supported by an efficient algorithm suitable for industrial deployment. Extensive experiments validate the effectiveness of our approach.

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Zhiyu Mou, Miao Xu, Rongquan Bai, Zhuoran Yang, Chuan Yu, Jian Xu, Bo Zheng. 2025-03-13. Large-Scale Auto-bidding with Nash Equilibrium Constraints. https://arxiv.org/abs/2503.10304

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