Scalable Optimal Online Auctions
This paper studies reserve prices computed to maximize the expected profit of the seller based on historical observations of incomplete bid data. This direct approach to computing reserve prices circumvents the need to fully recover distributions of bidder valuations. We specify precise conditions under which this approach is valid and derive asymptotic properties of the estimators. We apply the approach to e-commerce auction data for used smartphones from eBay, where we examine empirically the benefit of the optimal reserve as well as the size of data set required in practice to achieve that benefit. 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, whereas the approach we discuss is immediately scalable