Pricing under information asymmetry for a large population of users

In this paper, we study optimal nonlinear pricing policy design for a monopolistic network service provider in the face of a large population of users of different types described by a given probability distribution. In an earlier work (Shen and Başar in IEEE J. Sel. Areas Commun. 25(6):1216-1223, 2007), we had considered games with symmetric information, in the sense that either users' true types are public information available to all parties, or each user's true type is private information known only to that user. In this paper, we study the intermediate case with information asymmetry; that is, users' true types are shared information among the users themselves, but are not disclosed to the service provider. The problem can be formulated as an incentive-design problem, for which an ε-team optimal incentive (pricing) policy has been obtained, which almost achieves Pareto optimality for the service provider. A comparative study between games with information symmetry and asymmetry are conducted as well to evaluate the service provider's game preferences.