The Simple Empirics of Optimal Online Auctions
The Simple Empirics of Optimal Online Auctions
复制标题
最佳在线拍卖的简单经验
DOI：
10.3386/w24698
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Caio Waisman
中科院分区：
文献类型：
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作者：
Dominic Coey;B. Larsen;Kane Sweeney;Caio Waisman
我们研究基于对不完整出价数据的历史观察计算出的保留价，以最大化卖家的预期利润，这些出价数据通常可供拍卖设计者在广告或电子商务的在线拍卖中使用。这种直接计算保留价的方法避免了完全恢复投标人估值分布的需要。我们得到了渐近结果，并基于经验Rademacher复杂性，给出了使估计保留价格下的收益任意逼近最优保留价格下的收益所需的历史拍卖观察次数的一个新的界。这种估算储量的简单方法可能对大数据环境下的拍卖设计特别有用，在这种情况下，传统的经验式拍卖方法的实施成本可能会很高。我们以eBay的电子商务拍卖数据为例说明了该方法。我们还演示了如何将这个想法扩展到估计实现Myerson(1981)最优拍卖所需的所有物品。
We study reserve prices computed to maximize the expected profit of the seller based on historical observations of incomplete bid data typically available to the auction designer in online auctions for advertising or e-commerce. This direct approach to computing reserve prices circumvents the need to fully recover distributions of bidder valuations. We derive asymptotic results and also provide a new bound, based on the empirical Rademacher complexity, for the number of historical auction observations needed in order for revenue under the estimated reserve price to approximate revenue under the optimal reserve arbitrarily closely. This simple approach to estimating reserves may be particularly useful for auction design in Big Data settings, where traditional empirical auctions methods may be costly to implement. We illustrate the approach with e-commerce auction data from eBay. We also demonstrate how this idea can be extended to estimate all objects necessary to implement the Myerson (1981) optimal auction.