Bo Waggoner

Low-Cost Learning via Active Data Procurement

We design mechanisms for online procurement of data held by strategic agents for machine learning tasks. The challenge is to use past data to actively price future data and give learning guarantees even when an agent’s cost for revealing her data may depend arbitrarily on the data itself. We achieve this goal by showing how to convert a large class of no-regret algorithms into online posted-price and learning mechanisms. Our results in a sense parallel classic sample complexity guarantees, but with the key resource being money rather than quantity of data: With a budget constraint B, we give robust risk (predictive error) bounds on the order of 1/ √ B. Because we use an active approach, we can often guarantee to do significantly better by leveraging correlations between costs and data. Our algorithms and analysis go through a model of no-regret learning with T arriving pairs (cost, data) and a budget constraint of B. Our regret bounds for this model are on the order of T/ √ B and we give lower bounds on the same order.

Designing Markets for Daily Deals

Daily deals platforms such as Amazon Local, Google Offers, GroupOn, and LivingSocial have provided a new channel for merchants to directly market to consumers. In order to maximize consumer acquisition and retention, these platforms would like to offer deals that give good value to users. Currently, selecting such deals is done manually; however, the large number of submarkets and localities necessitates an automatic approach to selecting good deals and determining merchant payments. We approach this challenge as a market design problem. We postulate that merchants already have a good idea of the attractiveness of their deal to consumers as well as the amount they are willing to pay to offer their deal. The goal is to design an auction that maximizes a combination of the revenue of the auctioneer platform, welfare of the bidders merchants, and the positive externality on a third party the consumer, despite the asymmetry of information about this consumer benefit. We design auctions that truthfully elicit this information from the merchants and maximize the social welfare objective, and we characterize the consumer welfare functions for which this objective is truthfully implementable. We generalize this characterization to a very broad mechanism-design setting and give examples of other applications.

Descending Price Optimally Coordinates Search

Investigating potential purchases, such as a start-up company to acquire, is often a substantial investment under uncertainty. Standard market designs, such as simultaneous or ascending price auctions, compound this with additional uncertainty about the eventual price a bidder will have to pay in order to win. As a result they tend to confuse the process of search by leading to both wasteful information acquisition on goods that have already found a good purchaser and discouraging needed investigations of objects, potentially eliminating all gains from trade. Fully efficient procedures that avoid these problems, such as dynamic Vickrey-Clarke-Groves processes, are extremely complex and fragile. By contrast, we show that the Dutch auction preserves all of its properties from a standard setting without information costs because it guarantees, at the time of information acquisition, a price at which the good can be purchased.

Descending Price Coordinates Approximately Efficient Search

Contrary to common practice in selling homes and start-ups, mechanism design theory typically recommends English (increasing price) over Dutch (decreasing price) auctions. Yet this theory neglects the uncertain investment required to investigate purchases. We show that English and other standard auctions burden such investments with further uncertainty about the price necessary to win, potentially eliminating all gains from trade. In contrast, Dutch auctions preserve their properties because they guarantee, at the moment when investigation is optimal, a price at which the good can be purchased. Numerical explorations based partly on prior empirical results qualitatively confirm these conclusions.

An Online Appendix to 'Descending Price Optimally Coordinates Search'

This online appendix accompanies the paper “Descending Price Optimally Coordinates Search” by the same authors. The main paper is available at http://ssrn.com/abstract=2753858.