Efficient Advert Assignment

Abstract

We develop a framework for the analysis of large-scale Ad-auctions where adverts are assigned over a continuum of search types. For this pay-per-click market, we provide an efficient and highly decomposed mechanism that maximizes social welfare. In particular, we show that the social welfare optimization can be solved in separate optimizations conducted on the time-scales relevant to the advertisement platform and advertisers. Here, on each search occurrence, the platform solves an assignment problem and, on a slower time scale, each advertiser submits a bid which matches its demand for click-throughs with supply. Importantly knowledge of global parameters, such as the distribution of search terms, is not required when separating the problem in this way. This decomposition is implemented in an adversarial setting. Exploiting the information asymmetry between the platform and advertiser, we describe a simple mechanism which incentivizes truthful bidding and has a unique Nash equilibrium that is socially optimal, and thus implements our decomposition. Further, we consider models where advertisers adapt their bids smoothly over time, and prove convergence to the solution that maximizes aggregate utility. Finally, we describe several extensions which illustrate the flexibility and tractability of our framework.