Pricing under information asymmetry for a large population of users

Abstract

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. We assume that users have stochastic types. In [1], games with information symmetry have been considered; that is, users' true types may be public information available to all parties, or each user's true type may be 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 users, but are not disclosed to the service provider. The problem can be formulated as an incentive-design problem, and an e-team optimal incentive (pricing) policy is 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.