We identify the first static credible mechanism for multi-item additive auctions that achieves a constant factor of the optimal revenue. This is one instance of a more general framework for designing two-part tariff auctions, adapting the duality framework of Cai et al [CDW16]. Given a (not necessarily incentive compatible) auction format $A$ satisfying certain technical conditions, our framework augments the auction with a personalized entry fee for each bidder, which must be paid before the auction can be accessed. These entry fees depend only on the prior distribution of bidder types, and in particular are independent of realized bids. Our framework can be used with many common auction formats, such as simultaneous first-price, simultaneous second-price, and simultaneous all-pay auctions. If all-pay auctions are used, we prove that the resulting mechanism is credible in the sense that the auctioneer cannot benefit by deviating from the stated mechanism after observing agent bids. If second-price auctions are used, we obtain a truthful $O(1)$-approximate mechanism with fixed entry fees that are amenable to tuning via online learning techniques. Our results for first price and all-pay are the first revenue guarantees of non-truthful mechanisms in multi-dimensional environments; an open question in the literature [RST17].

中文翻译：

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简单、可信且近似最优的拍卖

我们确定了多项目附加拍卖的第一个静态可信机制，该机制实现了最佳收入的恒定因素。这是用于设计两部分关税拍卖的更通用框架的一个实例，采用 Cai 等人 [CDW16] 的二元框架。给定（不一定是激励兼容的）拍卖格式 $A$ 满足某些技术条件，我们的框架通过为每个投标人提供个性化的入场费来增强拍卖，必须在可以访问拍卖之前支付这笔费用。这些入场费仅取决于投标人类型的事先分配，特别是与已实现的投标无关。我们的框架可用于许多常见的拍卖格式，例如同步第一价格、同步第二价格和同步全额拍卖。如果使用全付费拍卖，我们证明了由此产生的机制是可信的，因为在观察代理出价后，拍卖师不能通过偏离规定的机制而受益。如果使用第二价格拍卖，我们将获得一个真实的 $O(1)$ 近似机制，具有固定的入场费，可以通过在线学习技术进行调整。我们对首价全付的结果是多维环境下非真实机制的第一收益保证；文献中的一个悬而未决的问题 [RST17]。我们对首价全付的结果是多维环境下非真实机制的第一收益保证；文献中的一个悬而未决的问题 [RST17]。我们对首价全付的结果是多维环境下非真实机制的第一收益保证；文献中的一个悬而未决的问题 [RST17]。