论文标题
内源抽样的经验收入最大化算法的游戏理论分析
A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling
论文作者
论文摘要
经验收入最大化（ERM）是拍卖设计中最重要的价格学习算法之一：由于文献表明，它可以在重复拍卖和统一的拍卖中学习大约最佳的储备价格，以使收入最大化的拍卖商在拍卖中最大化。但是，在这些应用程序中，向ERM提供投入的代理商有动力操纵输入以降低输出价格。我们概括了Lavi等人（2019年）提出的激励意识度量的定义，以量化由于$ N $输入样本中的$ M \ ge 1 $的变化，量化ERM的输出价格的降低，并提供此量度的特定收敛速率，以$ n $为$ n $，以$ n $为$ n $提供不同类型的输入分配。通过采用这项措施，我们在反复拍卖中对非侧比竞标者进行了ERM构建有效的，近似激励的兼容和最佳的学习算法，并在统一的估计拍卖中显示了近似的群体激励兼容性。
The Empirical Revenue Maximization (ERM) is one of the most important price learning algorithms in auction design: as the literature shows it can learn approximately optimal reserve prices for revenue-maximizing auctioneers in both repeated auctions and uniform-price auctions. However, in these applications the agents who provide inputs to ERM have incentives to manipulate the inputs to lower the outputted price. We generalize the definition of an incentive-awareness measure proposed by Lavi et al (2019), to quantify the reduction of ERM's outputted price due to a change of $m\ge 1$ out of $N$ input samples, and provide specific convergence rates of this measure to zero as $N$ goes to infinity for different types of input distributions. By adopting this measure, we construct an efficient, approximately incentive-compatible, and revenue-optimal learning algorithm using ERM in repeated auctions against non-myopic bidders, and show approximate group incentive-compatibility in uniform-price auctions.
