利用预测竞标价优化第一价格拍卖，实现近乎零遗憾。
Optimizing Bidding Strategies in First-Price Auctions in Binary Feedback Setting with Predictions
- 基于竞标预测改进在线学习算法，提升决策精度。
- 预测准确时可达到零遗憾，一般情况下遗憾上界为O(T^(3/4) * Vt^(1/4))。
- 适合研究拍卖机制与机器学习结合的学者或平台策略设计者。
本文研究在二元反馈设置下的维克里第一价格拍卖。借助机器学习算法的增强性能，新算法利用历史信息改进BROAD-OMD算法的遗憾上界。受第一价格拍卖日益重要性及机器学习预测能力的启发，本文在Hu等（2025）的BROAD-OMD框架内提出一种新算法，该算法利用对最高竞标价的预测。主要贡献在于：当预测准确时，算法可实现零遗憾；在特定正态条件下，可建立遗憾上界为O(T^(3/4) * Vt^(1/4))。该结果表明，引入预测可显著提升拍卖策略的性能。
原文摘要 · Abstract (English)
This paper studies Vickrey first-price auctions under binary feedback. Leveraging the enhanced performance of machine learning algorithms, the new algorithm uses past information to improve the regret bounds of the BROAD-OMD algorithm. Motivated by the growing relevance of first-price auctions and the predictive capabilities of machine learning models, this paper proposes a new algorithm within the BROAD-OMD framework (Hu et al., 2025) that leverages predictions of the highest competing bid. This paper's main contribution is an algorithm that achieves zero regret under accurate predictions. Additionally, a bounded regret bound of O(T^(3/4) * Vt^(1/4)) is established under certain normality conditions.
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