Approximation Schemes for Sequential Posted Pricing in Multi-unit Auctions

Tanmoy Chakraborty, Eyal Even-Dar, Sudipto Guha, Yishay Mansour, S. Muthukrishnan

Abstract: We design algorithms for computing approximately revenue-maximizing sequential posted-pricing mechanisms (SPM) in K-unit auctions, in a standard Bayesian model. A seller has K copies of an item to sell, and there are n buyers, each interested in only one copy, and has some value for the item. The seller posts a price for each buyer, using Bayesian information about buyers’ valuations, who arrive in a sequence. An SPM specifies the ordering of buyers and the posted prices, and may be adaptive or non-adaptive in its behavior. The goal is to design SPM in polynomial time to maximize expected revenue. We compare against the expected revenue of optimal SPM, and provide a polynomial time approximation scheme (PTAS) for both non-adaptive and adaptive SPMs. This is achieved by two algorithms: an efficient algorithm that gives a $(1-\frac{1}{\sqrt{2\pi K}})$ -approximation (and hence a PTAS for sufficiently large K), and another that is a PTAS for constant K. The first algorithm yields a non-adaptive SPM that yields its approximation guarantees against an optimal adaptive SPM – this implies that the adaptivity gap in SPMs vanishes as K becomes larger.