Approximation algorithms for the metric labeling problem via a new linear programming formulation
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Competitive auctions and digital goods
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Competitive generalized auctions
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
A Polylogarithmic Approximation of the Minimum Bisection
SIAM Journal on Computing
The Price of Truth: Frugality in Truthful Mechanisms
STACS '03 Proceedings of the 20th Annual Symposium on Theoretical Aspects of Computer Science
Operations Research
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Truthful Mechanisms for One-Parameter Agents
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
Towards a Characterization of Truthful Combinatorial Auctions
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Proceedings of the 6th ACM conference on Electronic commerce
On profit-maximizing envy-free pricing
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Beyond VCG: Frugality of Truthful Mechanisms
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Single-minded unlimited supply pricing on sparse instances
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
ESA'05 Proceedings of the 13th annual European conference on Algorithms
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In this paper, we examine a duopolistic market where the two firms compete to sell a system of components. Components are digital (firms haveunlimited supply at no marginal cost), and customers are homogeneous in their component preferences. Each customer will assemble a utility maximizing system by purchasing each necessary component from one of the two firms. While components from the same firm are always compatible, pairwise compatibility of components from rival firms may vary; in addition to utility due to the quality of the system purchased, customers have negative utility for purchasing incompatible parts. We investigate algorithms and hardness results for profit-maximizing decisions of the firms with regards to their price-setting, component value-enhancing and compatibility-enabling strategies. The users' behavior can be modeled as a minimum cut computation, and the company's strategies require addressing novel and interesting questions about graph cuts and flows. We develop a polynomial-time algorithm for finding profit-maximizing prices if the qualities and compatibilities are fixed. On the other hand, we show that finding profit-maximizing quality improvements is equivalent to the Maximum Size Bounded Capacity Cut problem, and thus NP-complete. Finally, for the problem of improving compatibilities to maximize the price, we give polynomial approximation hardness results even in very restricted cases, but show that if all components have uniform prices, and quality differences are small, then an approximation can be found in polynomial time.