Item Pricing for Revenue Maximization in Combinatorial Auctions

Abstract

Consider the problem of a retailer with various goods for sale, attempting to set prices to maximize revenue. If customers have separate valuations over the different goods, and these are known to the retailer, then the goods can be priced separately and the problem is not so difficult. However, when customers have valuations over sets of items, this becomes a combinatorial auction problem, and the problem becomes computationally hard even when valuations are fully known in advance. In this talk we present some simple randomized algorithms and mechanisms for a number of interesting cases of this problem, both in the limited and unlimited supply setting. This talk is based on joint work with Avrim Blum and Yishay Mansour.