论文标题
自动化世界中首价格拍卖的效率
Efficiency of the First-Price Auction in the Autobidding World
论文作者
论文摘要
我们研究自动化世界中首价拍卖的无政府状态价格，在那里投标可以是公用事业最大化器（即传统竞标者）或价值最大化器（即自动化车）。我们表明，仅使用Autobidders，首价拍卖的无政府状态的价格为$ 1/2 $，并且两种投标人，无政府状态的价格降低到$ 0.457 $（精确的数量由优化给出）。这些结果补充了Jin和Lu [2022]的最新结果表明，仅传统竞标者的第一价拍卖的无政府状态价格为$ 1-1/e^2 $。我们进一步研究卖方可以利用机器学习建议来提高拍卖效率的环境。在那里，我们表明，随着建议的准确性提高，无政府状态的价格从$ 0.457 $上升到$ 1 $。
We study the price of anarchy of the first-price auction in the autobidding world, where bidders can be either utility maximizers (i.e., traditional bidders) or value maximizers (i.e., autobidders). We show that with autobidders only, the price of anarchy of the first-price auction is $1/2$, and with both kinds of bidders, the price of anarchy degrades to about $0.457$ (the precise number is given by an optimization). These results complement the recent result by Jin and Lu [2022] showing that the price of anarchy of the first-price auction with traditional bidders only is $1 - 1/e^2$. We further investigate a setting where the seller can utilize machine-learned advice to improve the efficiency of the auctions. There, we show that as the accuracy of the advice increases, the price of anarchy improves smoothly from about $0.457$ to $1$.
