On Stackelberg Pricing with Computationally Bounded Consumers

In a Stackelberg pricing game a leader aims to set prices on a subset of a given collection of items, such as to maximize her revenue from a follower purchasing a feasible subset of the items. We focus on the case of computationally bounded followers who cannot optimize exactly over the range of all feasible subsets, but apply some publicly known algorithm to determine the set of items to purchase. This corresponds to general multi-dimensional pricing assuming that consumers cannot optimize over the full domain of their valuation functions but still aim to act rationally to the best of their ability. We consider two versions of this novel type of Stackelberg pricing games. Assuming that items are weighted objects and the follower seeks to purchase a min-cost selection of objects of some minimum weight (the MIN-KNAPSACK problem) and uses a simple greedy 2-approximate algorithm, we show how an extension of the known single-price algorithm can be used to derive a polynomial-time (2 + ε)-...

• Type: Conference
• Year: 2009
• Where: WINE
• Authors: Patrick Briest, Martin Hoefer, Luciano Gualà, Carmine Ventre
• Keywords: Economy, Feasible Subsets, Revenue Maximization, Stackelberg Pricing Games, WINE 2009