We provide the first analysis of (deferred acceptance) clock auctions in the learning-augmented framework. These auctions satisfy a unique list of appealing properties, including obvious strategyproofness, transparency, and unconditional winner privacy, making them particularly well-suited for real-world applications. However, early work that evaluated their performance from a worst-case analysis perspective concluded that no deterministic clock auction with $n$ bidders can achieve a $O(\log^{1-\epsilon} n)$ approximation of the optimal social welfare for any $\epsilon>0$, even in very simple settings. This overly pessimistic impossibility result heavily depends on the assumption that the designer has no information regarding the bidders' values. Leveraging the learning-augmented framework, we instead consider a designer equipped with some (machine-learned) advice regarding the optimal solution; this advice can provide useful guidance if accurate, but it may be unreliable. Our main results are learning-augmented clock auctions that use this advice to achieve much stronger guarantees whenever the advice is accurate (consistency), while maintaining worst-case guarantees even if this advice is arbitrarily inaccurate (robustness). Our first clock auction achieves the best of both worlds: $(1+\epsilon)$-consistency for any $\epsilon>0$ and $O(\log{n})$ robustness; we also extend this auction to achieve error tolerance. We then consider a much stronger notion of consistency, which we refer to as consistency$^\infty$, and provide auctions that achieves a near-optimal trade-off between consistency$^\infty$ and robustness. Finally, using our impossibility results regarding this trade-off, we prove lower bounds on the ``cost of smoothness,'' i.e., on the achievable robustness if we also require that the performance of the auction degrades smoothly as a function of the prediction error.

翻译：本文首次在增强学习框架下对（延迟接受）时钟拍卖机制进行分析。这类拍卖机制具备一系列独特的优良特性，包括显性策略证明性、透明性以及无条件获胜者隐私保护，使其特别适用于现实应用场景。然而，早期从最坏情况分析视角评估其性能的研究指出：即使在极简场景中，对于任意$\epsilon>0$，任何包含$n$个竞拍者的确定性时钟拍卖都无法实现$O(\log^{1-\epsilon} n)$的最优社会福利近似比。这一过度悲观的不可行性结论严重依赖于设计者完全不具备竞拍者估值信息的假设。通过引入增强学习框架，我们转而考虑设计者配备关于最优解的（机器学习）建议的情形：该建议在准确时可提供有效指导，但亦可能不可靠。我们的核心成果是构建了能够利用此类建议的增强学习时钟拍卖机制：当建议准确时获得显著更强的保证（一致性），即使建议任意不准确时仍保持最坏情况保证（鲁棒性）。我们提出的首个时钟拍卖机制实现了两者的最优平衡：对任意$\epsilon>0$具备$(1+\epsilon)$一致性和$O(\log{n})$鲁棒性；我们还扩展该机制以实现误差容忍。随后我们提出更强的一致性概念（称为一致性$^\infty$），并构建了能在一致性$^\infty$与鲁棒性间达成近似最优权衡的拍卖机制。最后，基于关于该权衡的不可行性结果，我们证明了“平滑性代价”的下界，即当要求拍卖性能随预测误差平滑下降时，可达到的鲁棒性界限。