On revenue maximization with sharp multi-unit demands

Abstract

We consider markets consisting of a set of indivisible items, and buyers that have sharp multi-unit demand. This means that each buyer $$i$$ wants a specific number $$d_i$$ of items; a bundle of size less than $$d_i$$ has no value. We consider the objective of setting prices and allocations in order to maximize the total revenue of the market maker. The pricing problem with sharp multi-unit demand buyers has a number of properties that the unit-demand model does not possess, and is an important question in algorithmic pricing. We consider the problem of computing a revenue maximizing solution for two solution concepts: competitive equilibrium and envy-free pricing. For unrestricted valuations, these problems are NP-complete; we focus on a realistic special case of “correlated values” where each buyer $$i$$ has a valuation $$v_iq_j$$ for item $$j$$ , where $$v_i$$ and $$q_j$$ are positive quantities associated with buyer $$i$$ and item $$j$$ respectively. We present a polynomial time algorithm to solve the revenue-maximizing competitive equilibrium problem. For envy-free pricing, if the demand of each buyer is bounded by a constant, a revenue maximizing solution can be found efficiently; the general demand case is shown to be NP-hard.