Title
Revenue Maximization In Stackelberg Pricing Games: Beyond The Combinatorial Setting
Abstract
In a Stackelberg Pricing Game a distinguished player, the leader, chooses prices for a set of items, and the other players, the followers, each seek to buy a minimum cost feasible subset of the items. The goal of the leader is to maximize her revenue, which is determined by the sold items and their prices. Most previously studied cases of such games can be captured by a combinatorial model where we have a base set of items, some with fixed prices, some priceable, and constraints on the subsets that are feasible for each follower. In this combinatorial setting, Briest et al. and Balcan et al. independently showed that the maximum revenue can be approximated to a factor of H-k similar to log k, where k is the number of priceable items. Our results are twofold. First, we strongly generalize the model by letting the follower minimize any continuous function plus a linear term over any compact subset of R->= 0(n); the coefficients (or prices) in the linear term are chosen by the leader and determine her revenue. In particular, this includes the fundamental case of linear programs. We give a tight lower bound on the revenue of the leader, generalizing the results of Briest et al. and Balcan et al. Besides, we prove that it is strongly NP-hard to decide whether the optimum revenue exceeds the lower bound by an arbitrarily small factor. Second, we study the parameterized complexity of computing the optimal revenue with respect to the number k of priceable items. In the combinatorial setting, given an efficient algorithm for optimal follower solutions, the maximum revenue can be found by enumerating the 2(k) subsets of priceable items and computing optimal prices via a result of Briest et al., giving time O(2(k) vertical bar I vertical bar(c)) where vertical bar I vertical bar is the input size. Our main result here is a W[1]-hardness proof for the case where the followers minimize a linear program, ruling out running time f (k)vertical bar I vertical bar(c) unless FPT = W[1] and ruling out time vertical bar I vertical bar(o(k)) under the Exponential-Time Hypothesis.
Year
DOI
Venue
2017
10.1007/s10107-020-01495-0
MATHEMATICAL PROGRAMMING
Keywords
Field
DocType
Algorithmic pricing, Stackelberg games, Approximation algorithms, Revenue maximization, Parameterized complexity
Revenue,Continuous function,Revenue maximization,Mathematical economics,Parameterized complexity,Combinatorics,Upper and lower bounds,Generalization,Computer science,Linear programming,Stackelberg competition
Conference
Volume
Issue
ISSN
187
1-2
0025-5610
Citations 
PageRank 
References 
0
0.34
8
Authors
3
Name
Order
Citations
PageRank
Toni Böhnlein100.34
Stefan Kratsch256142.59
Oliver Schaudt39521.74