Internet and Network Economics

Volume 6484 of the series Lecture Notes in Computer Science pp 118-132

Optimal Pricing in the Presence of Local Network Effects

  • Ozan CandoganAffiliated withLaboratory for Information and Decision Systems, MIT
  • , Kostas BimpikisAffiliated withOperations Research Center and Laboratory for Information and Decision Systems, MIT
  • , Asuman OzdaglarAffiliated withLaboratory for Information and Decision Systems, MIT

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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 effect. 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 offering discounts to certain agents, 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 offer 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 offer 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 offered the discounted price. Finally, we describe an approximation algorithm for finding the optimal set of agents. We show that if the profit is nonnegative under any feasible price allocation, the algorithm guarantees at least 88 % of the optimal profit.