提出更可靠的后悔值估计方法，纠正深度学习拍卖模型的严重低估问题
Bridging the Gap Between Estimated and True Regret Towards Reliable Regret Estimation in Deep Learning based Mechanism Design
- 基于物品级后悔近似与下界推导，提升估计精度
- 实测显示真实后悔值是报告值的数百倍，现有模型严重高估性能
- 新方法在降低计算成本的同时显著提高评估可靠性
近期研究如RegretNet、ALGnet、RegretFormer和CITransNet采用深度学习近似多物品拍卖机制，通过放松激励相容（IC）并以事后后悔度量其违反程度。然而，这些方法的后悔估计准确性尚未明确。精确计算后悔值在计算上不可行，现有模型依赖梯度优化器，结果受超参数影响极大。通过大量实验，我们发现现有方法系统性低估真实后悔值（某些模型中真实后悔值为报告值的数百倍），导致对激励相容性和收益的过度乐观评价。为此，我们推导出后悔值下界，并提出一种高效的物品级后悔近似方法。在此基础上，设计了引导式精炼流程，显著提升估计准确率并降低计算开销。该方法为深度学习驱动的拍卖机制中的激励相容性评估提供了更可靠基础，并提示需重新审视该领域先前性能声称。
原文摘要 · Abstract (English)
Recent advances, such as RegretNet, ALGnet, RegretFormer and CITransNet, use deep learning to approximate optimal multi item auctions by relaxing incentive compatibility (IC) and measuring its violation via ex post regret. However, the true accuracy of these regret estimates remains unclear. Computing exact regret is computationally intractable, and current models rely on gradient based optimizers whose outcomes depend heavily on hyperparameter choices. Through extensive experiments, we reveal that existing methods systematically underestimate actual regret (In some models, the true regret is several hundred times larger than the reported regret), leading to overstated claims of IC and revenue. To address this issue, we derive a lower bound on regret and introduce an efficient item wise regret approximation. Building on this, we propose a guided refinement procedure that substantially improves regret estimation accuracy while reducing computational cost. Our method provides a more reliable foundation for evaluating incentive compatibility in deep learning based auction mechanisms and highlights the need to reassess prior performance claims in this area.
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