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Pricing with Limited Knowledge of Demand

Author

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  • Maxime C. Cohen
  • Georgia Perakis
  • Robert S. Pindyck

Abstract

How should a firm price a new product for which little is known about demand? We propose a simple pricing rule: the firm only estimates the maximum price it can charge and still expect to sell at least some units, and then sets price as though the actual demand curve were linear. We show that if the true demand curve is one of many commonly used demand functions, or even if it is a more complex function, and if marginal cost is known and constant, the firm can expect its profit to be close to what it would earn if it knew the true demand curve. We derive analytical performance bounds for a variety of demand functions, and calculate expected profit performance for randomly generated demand curves.

Suggested Citation

  • Maxime C. Cohen & Georgia Perakis & Robert S. Pindyck, 2015. "Pricing with Limited Knowledge of Demand," NBER Working Papers 21679, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:21679
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    References listed on IDEAS

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    1. Vivek F. Farias & Benjamin Van Roy, 2010. "Dynamic Pricing with a Prior on Market Response," Operations Research, INFORMS, vol. 58(1), pages 16-29, February.
    2. Andrew E. B. Lim & J. George Shanthikumar, 2007. "Relative Entropy, Exponential Utility, and Robust Dynamic Pricing," Operations Research, INFORMS, vol. 55(2), pages 198-214, April.
    3. Rothschild, Michael, 1974. "A two-armed bandit theory of market pricing," Journal of Economic Theory, Elsevier, vol. 9(2), pages 185-202, October.
    4. Dirk Bergemann & Karl Schlag, 2012. "Robust Monopoly Pricing," World Scientific Book Chapters, in: Robust Mechanism Design The Role of Private Information and Higher Order Beliefs, chapter 13, pages 417-441, World Scientific Publishing Co. Pte. Ltd..
    5. Yossi Aviv & Amit Pazgal, 2005. "A Partially Observed Markov Decision Process for Dynamic Pricing," Management Science, INFORMS, vol. 51(9), pages 1400-1416, September.
    6. Omar Besbes & Assaf Zeevi, 2009. "Dynamic Pricing Without Knowing the Demand Function: Risk Bounds and Near-Optimal Algorithms," Operations Research, INFORMS, vol. 57(6), pages 1407-1420, December.
    7. Michael O. Ball & Maurice Queyranne, 2009. "Toward Robust Revenue Management: Competitive Analysis of Online Booking," Operations Research, INFORMS, vol. 57(4), pages 950-963, August.
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    Cited by:

    1. Miller, Nathan H. & Remer, Marc & Ryan, Conor & Sheu, Gloria, 2017. "Upward pricing pressure as a predictor of merger price effects," International Journal of Industrial Organization, Elsevier, vol. 52(C), pages 216-247.
    2. Jian Hu & Junxuan Li & Sanjay Mehrotra, 2019. "A Data-Driven Functionally Robust Approach for Simultaneous Pricing and Order Quantity Decisions with Unknown Demand Function," Operations Research, INFORMS, vol. 67(6), pages 1564-1585, November.
    3. Omar Besbes & Adam N. Elmachtoub & Yunjie Sun, 2020. "Pricing Analytics for Rotable Spare Parts," Interfaces, INFORMS, vol. 50(5), pages 313-324, September.

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    More about this item

    JEL classification:

    • D40 - Microeconomics - - Market Structure, Pricing, and Design - - - General
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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