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Pricing under information asymmetry for a large population of users

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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 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.

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References

  1. Shen, H.-X., & Başar, T. (2007). Optimal nonlinear pricing for a monopolistic network service provider with complete and incomplete information. IEEE Journal on Selected Areas in Communications, 25(6), 1216–1223.

    Article  Google Scholar 

  2. Kelly, F., Maulloo, A., & Tan, D. (1998). Rate control for communication networks: Shadow prices, proportional fairness and stability. Journal of the Operational Research Society, 49, 237–252.

    Google Scholar 

  3. Johari, R., & Tsitsiklis, J. (2004). Efficiency loss in a network resource allocation game. Mathematics of Operations Research, 29(3), 407–435.

    Article  Google Scholar 

  4. Maheswaran, R., & Başar, T. (2006). Efficient signal proportional allocation (ESPA) mechanisms: Decentralized social welfare maximization for divisible resources. IEEE Journal on Selected Areas in Communications, 24(5), 1000–1009.

    Article  Google Scholar 

  5. Liu, S., Başar, T., & Srikant, R. (2003). Controlling the Internet: A survey and some new results. In Proc. IEEE conference on decision and control, December 9–12, 2003, Maui, Hawaii (pp. 3048–3057).

  6. Honig, M., & Steiglitz, K. (1995). Usage-based pricing of packet data generated by a heterogeneous user population. In Proceedings IEEE INFOCOM 1995 (vol. 2, pp. 867–874).

  7. He, L., & Walrand, J. (2006). Pricing and revenue sharing strategies for Internet service providers. IEEE Journal on Selected Areas in Communications, 24(5), 942–951.

    Article  Google Scholar 

  8. Başar, T., & Srikant, R. (2002). Revenue-maximizing pricing and capacity expansion in a many-users regime. In Proceedings IEEE INFOCOM 2002 (pp. 1556–1563).

  9. Shen, H.-X., & Başar, T. (2007). Incentive-based pricing for network games with complete and incomplete information. In S. Jørgensen, M. Quincampoix, & T. L. Vincent (Eds.), Advances in dynamic game theory: numerical methods, algorithms, and applications to ecology and economics : Vol. 9. Annals of the International Society of Dynamic Games (pp. 431–458). Boston: Birkhäuser.

    Google Scholar 

  10. Shen, H.-X., & Başar, T. (2006). Hierarchical network games with various types of public and private information. In Proceedings of the 45th IEEE conference on decision and control (pp. 2825–2830).

  11. Shen, H.-X. (2007). Linear and nonlinear pricing for network games with complete and incomplete information. Ph.D. dissertation, University of Illinois at Urbana-Champaign, Urbana, IL, 2007.

  12. Varian, H. R. (2003). Intermediate microeconomics: a modern approach (6th edn.). New York: Norton.

    Google Scholar 

  13. Maskin, E., & Riley, J. (1984). Monopoly with incomplete information. The RAND Journal of Economics, 15(2), 171–196.

    Article  Google Scholar 

  14. Varadhan, S. R. S. (2001). Probability theory. Providence: American Mathematical Society.

    Google Scholar 

  15. Luenberger, D. G. (1969). Optimization by vector space methods. New York: Wiley.

    Google Scholar 

  16. Bertsekas, D. P. (1999). Nonlinear Programming (2nd edn.). Belmont: Athena Scientific.

    Google Scholar 

  17. Başar, T., & Srikant, R. (2002). A Stackelberg network game with a large number of followers. Journal of Optimization Theory and Applications, 115(3), 479–490.

    Article  Google Scholar 

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Correspondence to Hongxia Shen.

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The first author was with the Coordinated Science Laboratory, University of Illinois, Urbana, IL 61801, USA, when this work was done. This work was supported in part by the National Science Foundation (NSF) under Grant ANI-031976. An earlier version was presented at the Workshop on Game Theory in Communication Networks (GameComm2007), held in Nantes, France, October 27, 2007.

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Shen, H., Başar, T. Pricing under information asymmetry for a large population of users. Telecommun Syst 47, 123–136 (2011). https://doi.org/10.1007/s11235-010-9306-2

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