校准点击拍卖：一种信息设计方法
Calibrated Click-Through Auctions: An Information Design Approach
摘要：
我们分析了最优信息设计的点击拍卖与固定的估值每次点击，但随机点击率。拍卖人在给定点击拍卖即广义第二价格拍卖的拍卖规则的情况下，可以设计投标人之间关于点击率的信息流。在这种情况下，一个自然的要求是要求在学习意义上校准信息结构。在此约束下，拍卖需要通过出价和点击率的无偏估计的乘积来对广告进行排名，并且设计最优信息结构的任务因此被简化为设计最优无偏估计的任务。我们发现，在一个对称的设置与不确定性的点击率，最优的信息结构达到社会效率和盈余提取。最佳信息结构要求向投标人发出私人（而不是公开）信号。它还需要相关的（而不是独立的）信号，即使关于点击率的潜在不确定性是独立的。除了对称设置，我们表明，最佳的信息结构需要部分信息披露。
英文摘要：
We analyze the optimal information design in a click-through auction with fixed valuations per click, but stochastic click-through rates. While the auctioneer takes as given the auction rule of the click-through auction, namely the generalized second-price auction, the auctioneer can design the information flow regarding the click-through rates among the bidders. A natural requirement in this context is to ask for the information structure to be calibrated in the learning sense. With this constraint, the auction needs to rank the ads by a product of the bid and an unbiased estimator of the click-through rates, and the task of designing an optimal information structure is thus reduced to the task of designing an optimal unbiased estimator. We show that in a symmetric setting with uncertainty about the click-through rates, the optimal information structure attains both social efficiency and surplus extraction. The optimal information structure requires private (rather than public) signals to the bidders. It also requires correlated (rather than independent) signals, even when the underlying uncertainty regarding the click-through rates is independent. Beyond symmetric settings, we show that the optimal information structure requires partial information disclosure.
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