Efficient Advert Assignment
For online advertising platforms and advertisers, this work offers a theoretically grounded mechanism that achieves social welfare optimality without requiring global knowledge of search term distributions, addressing information asymmetry in large-scale ad auctions.
This paper develops a framework for large-scale ad auctions over a continuum of search types, providing an efficient mechanism that maximizes social welfare by decomposing the optimization into separate time-scale problems for the platform and advertisers. The mechanism incentivizes truthful bidding, has a unique socially optimal Nash equilibrium, and proves convergence under smooth bid adaptation.
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 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 search 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. 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 social welfare. Finally, we describe several extensions which illustrate the flexibility and tractability of our framework.