知道谁，而非多少：面向消费者效用最大化的学习增强机制
Knowing Who, Not How Much: Learning-Augmented Mechanisms for Consumer Utility Maximization
AI总结：
研究在线随机顺序模型中战略代理的消费者效用最大化问题，通过识别最高价值代理的身份预测，设计了一个确定性的真实机制，在预测正确时实现常数近似最优解，在预测错误时仍保证常数近似最优可实施解。
AI中文摘要：
我们研究在线随机顺序模型中的消费者效用最大化问题，其中战略代理顺序到达。为了规避效用最大化的强不可能性结果，我们转向学习增强机制设计的框架。关键的是，我们表明学习增强机制设计中常用的预测类型（如代理价值或最优值的预测）对效用最大化无用，因为支付与目标直接冲突。相反，我们识别出一种定性不同的预测就足够了：最高价值代理的身份。首先，我们通过调整离线随机技术为我们的在线设置提供了一个确定性的真实机制。然后，我们用预测增强我们的机制。当预测正确时，我们在完全信息下实现最优解的常数近似（一致性），即使预测任意差，我们也保证最佳可实施解的常数近似（鲁棒性）。
英文摘要：
We study consumer utility maximization in an online random-order model where strategic agents arrive sequentially. To circumvent strong impossibility results for utility maximization, we turn to the framework of learning-augmented mechanism design. Crucially, we show that the types of predictions commonly used in learning-augmented mechanism design (such as predictions of agent values or the optimal value) are not useful for utility maximization, where payments are directly at odds with the objective. Instead, we identify that a qualitatively different kind of prediction suffices: the identity of the highest-valued agent. First, we provide a deterministic truthful mechanism for our online setting by adapting offline randomized techniques. Then, we augment our mechanism with predictions. When the predictions are correct, we achieve a constant approximation to the optimal solution under full information (consistency), and even when predictions are arbitrarily bad, we guarantee a constant approximation to the best implementable solution (robustness).
