We present a partial identification approach for ascending auctions with bidder asymmetries, where bidders' asymmetric types may be unobservable to the econometrician. Our approach yields sharp bounds and builds on and generalizes other recent bounds approaches for correlated private values ascending auctions. When bidder identities are observable, our approach yields tighter bounds than previous approaches that ignore asymmetry, demonstrating that bidder asymmetries can function as an aid rather than a hindrance to identification. We present a nonparametric estimation and inference approach relying on our identification argument and apply it to data from U.S. timber auctions, finding that bounds on optimal reserve prices and other objects of interest are noticeably tighter when exploiting bidder asymmetries.
Advertisers that engage in real-time bidding (RTB) to display their ads commonly have two goals: learning their optimal bidding policy and estimating the expected effect of exposing users to their ads. Typical strategies to accomplish one of these goals tend to ignore the other, creating an apparent tension between the two. This paper exploits the economic structure of the bid optimization problem faced by advertisers to show that these two objectives can actually be perfectly aligned. By framing the advertiser's problem as a multi-armed bandit (MAB) problem, we propose a modified Thompson Sampling (TS) algorithm that concurrently learns the optimal bidding policy and estimates the expected effect of displaying the ad while minimizing economic losses from potential sub-optimal bidding. Simulations show that not only the proposed method successfully accomplishes the advertiser's goals, but also does so at a much lower cost than more conventional experimentation policies aimed at performing causal inference.
Storms, and Anthony Zhang for helpful comments. Coey and Sweeney were employees of eBay Research Labs while working on this project, and Larsen was a part-time consultant for eBay Research Labs when the project was started. eBay had the right to review the paper before it was circulated, but did not attempt to influence the research in any way. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.
This paper provides a set of tools to compute and implement optimal reserve prices for online auctions.
When multiple firms are simultaneously running experiments on a platform, the treatment effects for one firm may depend on the experimentation policies of others. This paper presents a set of causal estimands that are relevant to such an environment. We also present an experimental design that is suitable for facilitating experimentation across multiple competitors in such an environment. Together, these can be used by a platform to run experiments "as a service," on behalf of its participating firms. We show that the causal estimands we develop are identified nonparametrically by the variation induced by the design, and present two scalable estimators that help measure them in typical, high-dimensional situations. We implement the design on the advertising platform of JD.com, an eCommerce company, which is also a publisher of digital ads in China. We discuss how the design is engineered within the platform's auction-driven ad-allocation system, which is typical of modern, digital advertising marketplaces. Finally, we present results from a parallel experiment involving 16 advertisers and millions of JD.com users. These results showcase the importance of accommodating a role for interactions across experimenters and demonstrates the viability of the framework.
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