Optimal pricing in the presence of local network effects
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Author(s)
Candogan, Utku Ozan
Bimpikis, Konstantinos
Ozdaglar, Asuman E
Date Issued
December 2010
Journal
Proceedings of the 6th Workshop on Internet & Network Economics, WINE 2010
Citation
Candogan, Ozan, Kostas Bimpikis and Asuman Ozdaglar. "Optimal pricing in the presence of local network effects." Proceedings of the 6th Workshop on Internet & Network Economics, WINE 2010, December 13-16, 2010, Stanford University, Stanford, California, USA.
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Author's final manuscript
Abstract
We study the optimal pricing strategies of a monopolist selling a divisible good (service) to
consumers that are embedded in a social network. A key feature of our model is that consumers
experience a (positive) local network e ffect. In particular, each consumer's usage level depends
directly on the usage of her neighbors in the social network structure. Thus, the monopolist's
optimal pricing strategy may involve o ffering discounts to certain agents3, who have a central
position in the underlying network. Our results can be summarized as follows. First, we consider a
setting where the monopolist can o er individualized prices and derive an explicit characterization
of the optimal price for each consumer as a function of her network position. In particular, we
show that it is optimal for the monopolist to charge each agent a price that is proportional to her
Bonacich centrality in the social network. In the second part of the paper, we discuss the optimal
strategy of a monopolist that can only choose a single uniform price for the good and derive an
algorithm polynomial in the number of agents to compute such a price. Thirdly, we assume that
the monopolist can o er the good in two prices, full and discounted, and study the problem of
determining which set of consumers should be given the discount. We show that the problem is
NP-hard, however we provide an explicit characterization of the set of agents that should be o ffered
the discounted price. Finally, we describe an approximation algorithm for finding the optimal set of
agents. We show that if the pro t is nonnegative under any feasible price allocation, the algorithm
guarantees at least 88 % of the optimal pro fit.
Description
URL to paper listed on conference site
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Massachusetts Institute of Technology. Operations Research Center
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http://www.stanford.edu/group/wine/accepted.html
