AI

Auctions with intermediaries: extended abstract

Abstract

Inspired by online advertisement exchange systems, we study a setting where potential buyers of a unique, indivisible good attempt to purchase from a central seller via a set of intermediaries. Each intermediary has captive buyers, and runs an auction for a 'contingent' good. Based on the outcome, the intermediary bids in a subsequent upstream auction run by the seller. In this paper, we study the equilibria and incentives of intermediaries and the central seller.

We find that combining the notion of optimal auction design with the double-marginalization arising from the presence of intermediaries yields new strategic elements not present in either setting individually: we show that in equilibrium, revenue-maximizing intermediaries will use an auction with a randomized reserve price chosen from an interval. We characterize the interval and the probability distribution from which this reserve price is chosen as a function of the distribution of buyers' types. Furthermore, we characterize the revenue maximizing auction for the central seller by taking into account the effect of his choice of mechanism on the mechanisms offered by the intermediaries. We find that the optimal reserve price offered by the seller decreases with the number of buyers (but remains strictly positive); in contrast to the classical optimal auction without intermediaries, where the reserve price is independent of the number of buyers.