自动投标世界中强有力的拍卖设计 (Robust Auction Design in the Auto-bidding World)

In classic auction theory, reserve prices are known to be effective for improving revenue for the auctioneer against quasi-linear utility maximizing bidders. The introduction of reserve prices, however, usually do not help improve total welfare of the auctioneer and the bidders. In this paper, we focus on value maximizing bidders with return on spend constraints -- a paradigm that has drawn considerable attention recently as more advertisers adopt auto-bidding algorithms in advertising platforms -- and show that the introduction of reserve prices has a novel impact on the market. Namely, by choosing reserve prices appropriately the auctioneer can improve not only the total revenue but also the total welfare. Our results also demonstrate that reserve prices are robust to bidder types, i.e., reserve prices work well for different bidder types, such as value maximizers and utility maximizers, without using bidder type information. We generalize these results for a variety of auction mechanisms such as VCG, GSP, and first-price auctions. Moreover, we show how to combine these results with additive boosts to improve the welfare of the outcomes of the auction further. Finally, we complement our theoretical observations with an empirical study confirming the effectiveness of these ideas using data from online advertising auctions.

翻译：在传统的拍卖理论中,据知储备价格对于提高拍卖商的收入对准线性公用事业最大化投标人有效。然而,引入储备价格通常无助于改善拍卖商和投标人的总体福利。在本文中,我们侧重于以支出限制回报的方式使投标人获得最大价值 -- -- 最近随着更多的广告商在广告平台中采用自动招标算法,这种范式引起了人们的极大注意 -- -- 并表明引入储备价格对市场产生了新影响。也就是说,通过适当选择储备价格,拍卖商不仅能够改善总收入,而且能够改善总体福利。我们的结果还表明,储备价格对于投标人类型来说是强劲的,即保留价格对于不同的投标人类型(如价值最大化者和效用最大化者)效果良好,而没有使用投标人类型的信息。我们将这些结果归纳为各种拍卖机制,如VCG、普惠制和首价拍卖。此外,我们展示了如何将这些结果与添加剂的刺激因素结合起来,进一步提高拍卖结果的效益。最后,我们用一项经验研究来补充我们的理论观察,用在线拍卖数据确认这些想法的有效性。