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arXiv 2306.06554cs.GT
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贝叶斯校准的点击率拍卖

Bayesian Calibrated Click-Through Auction

Junjie Chen, Minming Li, Haifeng Xu, Song Zuo

AI总结:

本文研究点击率拍卖中的信息设计,提出FPTAS算法计算近似最优机制,并针对双竞标者对称情形设计基于最优信号比的简单无先验信号方案,以最大化卖家收入。

摘要:

我们研究了点击率拍卖中的信息设计问题,其中竞标者/广告主竞标以赢得展示其广告的机会,但仅为实际发生的点击付费。支付可能发生也可能不发生,其概率称为点击率(CTR)。这种拍卖形式在在线广告行业中被广泛使用。竞标者拥有私有价值,而卖家拥有关于每个竞标者点击率的私有信息。我们关注卖家通过部分揭示点击率信息以最大化收入的问题。点击率拍卖中的信息设计被证明与以往几乎所有相关研究都截然不同,因为任何关于点击率的信息揭示都不会影响竞标者的出价行为——他们总是会按每次点击的真实价值出价——而只会影响拍卖的分配和支付规则。在某种意义上,这使得信息设计实际上成为一个受约束的机制设计问题。\n我们的第一个结果是一个FPTAS,用于在竞标者数量为常数的情况下计算近似最优机制。该算法的设计利用了贝叶斯竞标者价值,这有助于“平滑”卖家的收入函数并提高可处理性。该FPTAS的设计复杂且主要涉及算法层面。我们的第二个主要结果致力于设计“简单”的机制,这些机制近似最优且更实用。我们主要关注两个竞标者的情况,正如最近的研究所示,这已经极具挑战性。当竞标者的点击率分布对称时,我们开发了一种简单的无先验信号方案,其构造依赖于一个称为最优信号比的参数。只要竞标者价值密度函数的最大值和最小值相差不大,所构造的方案就能保证获得良好的近似。

英文摘要:

We study information design in click-through auctions, in which the bidders/advertisers bid for winning an opportunity to show their ads but only pay for realized clicks. The payment may or may not happen, and its probability is called the click-through rate (CTR). This auction format is widely used in the industry of online advertising. Bidders have private values, whereas the seller has private information about each bidder's CTRs. We are interested in the seller's problem of partially revealing CTR information to maximize revenue. Information design in click-through auctions turns out to be intriguingly different from almost all previous studies in this space since any revealed information about CTRs will never affect bidders' bidding behaviors -- they will always bid their true value per click -- but only affect the auction's allocation and payment rule. In some sense, this makes information design effectively a constrained mechanism design problem. Our first result is an FPTAS to compute an approximately optimal mechanism under a constant number of bidders. The design of this algorithm leverages Bayesian bidder values which help to ``smooth'' the seller's revenue function and lead to better tractability. The design of this FPTAS is complex and primarily algorithmic. Our second main result pursues the design of ``simple'' mechanisms that are approximately optimal yet more practical. We primarily focus on the two-bidder situation, which is already notoriously challenging as demonstrated in recent works. When bidders' CTR distribution is symmetric, we develop a simple prior-free signaling scheme, whose construction relies on a parameter termed optimal signal ratio. The constructed scheme provably obtains a good approximation as long as the maximum and minimum of bidders' value density functions do not differ much.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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