Pricing under information asymmetry for a large population of users
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In the communication network pricing literature, it is the linear pricing schemes that have been largely adopted as the means of controlling network usage or generating profits for network service providers. This paper extends the framework to nonlinear pricing and investigates optimal nonlinear pricing policy design for a monopolistic service provider. The problem is formulated as an incentive-design problem, and incentive (pricing) policies are obtained for a many-users regime, which enable the service provider to approach arbitrarily close to Pareto- optimal solutions. Under the assumption that the service provider knows the true user types, analytical and numerical results indicate a profit improvement exceeding 38% over linear pricing by the introduction of nonlinear pricing. We also consider the scenario where the service provider has incomplete information on user types. A comparative study of the results for complete information and incomplete information is carried out as well, with numerical results pointing to 25%-40% loss of profit by the service provider due to incompleteness of information on the user types.