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Dynamic Pricing with an Unknown Demand Model: Asymptotically Optimal Semi-Myopic Policies

Author

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  • N. Bora Keskin

    (Booth School of Business, University of Chicago, Chicago, Illinois 60637)

  • Assaf Zeevi

    (Graduate School of Business, Columbia University, New York, New York 10027)

Abstract

We consider a monopolist who sells a set of products over a time horizon of T periods. The seller initially does not know the parameters of the products' linear demand curve, but can estimate them based on demand observations. We first assume that the seller knows nothing about the parameters of the demand curve, and then consider the case where the seller knows the expected demand under an incumbent price. It is shown that the smallest achievable revenue loss in T periods, relative to a clairvoyant who knows the underlying demand model, is of order T in the former case and of order log T in the latter case. To derive pricing policies that are practically implementable, we take as our point of departure the widely used policy called greedy iterated least squares (ILS), which combines sequential estimation and myopic price optimization. It is known that the greedy ILS policy itself suffers from incomplete learning, but we show that certain variants of greedy ILS achieve the minimum asymptotic loss rate. To highlight the essential features of well-performing pricing policies, we derive sufficient conditions for asymptotic optimality.

Suggested Citation

  • N. Bora Keskin & Assaf Zeevi, 2014. "Dynamic Pricing with an Unknown Demand Model: Asymptotically Optimal Semi-Myopic Policies," Operations Research, INFORMS, vol. 62(5), pages 1142-1167, October.
    • Handle: RePEc:inm:oropre:v:62:y:2014:i:5:p:1142-1167
    DOI: 10.1287/opre.2014.1294
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    References listed on IDEAS

    as
    1. Omar Besbes & Assaf Zeevi, 2011. "On the Minimax Complexity of Pricing in a Changing Environment," Operations Research, INFORMS, vol. 59(1), pages 66-79, February.
    2. Josef Broder & Paat Rusmevichientong, 2012. "Dynamic Pricing Under a General Parametric Choice Model," Operations Research, INFORMS, vol. 60(4), pages 965-980, August.
    3. Anderson, T W & Taylor, John B, 1976. "Some Experimental Results on the Statistical Properties of Least Squares Estimates in Control Problems," Econometrica, Econometric Society, vol. 44(6), pages 1289-1302, November.
    4. J. Michael Harrison & N. Bora Keskin & Assaf Zeevi, 2012. "Bayesian Dynamic Pricing Policies: Learning and Earning Under a Binary Prior Distribution," Management Science, INFORMS, vol. 58(3), pages 570-586, March.
    5. Taylor, John B, 1974. "Asymptotic Properties of Multiperiod Control Rules in the Linear Regression Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 15(2), pages 472-484, June.
    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.
    Full references (including those not matched with items on IDEAS)

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