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Dynamic Nonlinear Pricing in Networks with Interdependent Demand

Author

Listed:
  • Anirudh Dhebar

    (Harvard University, Boston, Massachusetts)

  • Shmuel S. Oren

    (University of California, Berkeley, California)

Abstract

We consider the pricing decision for a new product whose consumption value increases as the network of adopters expands. This demand interdependence is a characteristic feature of telecommunications networks. The paper analyzes the case of a single supplier offering the product to a heterogeneous population. Consumers decide on network access and consumption quantity. The pricing decision consists of choosing a pricing schedule (over the dimensions of quantity and time) that maximizes the present value of a weighted sum of total surplus and producer profits, subject to the dynamics of market growth. We find that the decision can be decoupled into two nested optimization problems: (a) given some market size, what should the optimal nonlinear price schedule be? and (b) how should the price schedule be changed optimally over time? This separation enables us to solve the pricing problem. Explicit consideration of the dynamics, the discounting of future surpluses, and the extent of market growth anticipation affect not only the price and network size trajectories, but also their steady-state equilibrium values.

Suggested Citation

  • Anirudh Dhebar & Shmuel S. Oren, 1986. "Dynamic Nonlinear Pricing in Networks with Interdependent Demand," Operations Research, INFORMS, vol. 34(3), pages 384-394, June.
    • Handle: RePEc:inm:oropre:v:34:y:1986:i:3:p:384-394
    DOI: 10.1287/opre.34.3.384
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    Citations

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    Cited by:

    1. Fatemeh Nosrat & William L. Cooper & Zizhuo Wang, 2021. "Pricing for a product with network effects and mixed logit demand," Naval Research Logistics (NRL), John Wiley & Sons, vol. 68(2), pages 159-182, March.
    2. Marius F. Niculescu & Hyoduk Shin & Seungjin Whang, 2012. "Underlying Consumer Heterogeneity in Markets for Subscription-Based IT Services with Network Effects," Information Systems Research, INFORMS, vol. 23(4), pages 1322-1341, December.
    3. Nan Yang & Renyu Zhang, 2022. "Dynamic pricing and inventory management in the presence of online reviews," Production and Operations Management, Production and Operations Management Society, vol. 31(8), pages 3180-3197, August.
    4. Yifan Dou & Marius F. Niculescu & D. J. Wu, 2013. "Engineering Optimal Network Effects via Social Media Features and Seeding in Markets for Digital Goods and Services," Information Systems Research, INFORMS, vol. 24(1), pages 164-185, March.
    5. Mesak, Hani I. & Bari, Abdullahel & Babin, Barry J. & Birou, Laura M. & Jurkus, Anthony, 2011. "Optimum advertising policy over time for subscriber service innovations in the presence of service cost learning and customers' disadoption," European Journal of Operational Research, Elsevier, vol. 211(3), pages 642-649, June.
    6. Ming Hu & Zizhuo Wang & Yinbo Feng, 2020. "Information Disclosure and Pricing Policies for Sales of Network Goods," Operations Research, INFORMS, vol. 68(4), pages 1162-1177, July.
    7. Zhen Lian & Garrett van Ryzin, 2021. "Optimal Growth in Two-Sided Markets," Management Science, INFORMS, vol. 67(11), pages 6862-6879, November.
    8. Roy Radner & Ami Radunskaya & Arun Sundararajan, 2010. "Dynamic Pricing of Network Goods with Boundedly Rational Consumers," Working Papers 10-13, New York University, Leonard N. Stern School of Business, Department of Economics.
    9. Yuri Levin & Mikhail Nediak & Andrei Bazhanov, 2014. "Quantity Premiums and Discounts in Dynamic Pricing," Operations Research, INFORMS, vol. 62(4), pages 846-863, August.

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