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Modeling the Transient Nature of Dynamic Pricing with Demand Learning in a Competitive Environment

In: Network Science, Nonlinear Science and Infrastructure Systems

Author

Listed:
  • Soulaymane Kachani

    (Columbia University)

  • Georgia Perakis

    (MIT Sloan School of Management)

  • Carine Simon

    (MIT Operations Research Center)

Abstract

This paper focuses on joint dynamic pricing and demand learning in an oligopolistic market. Each firm seeks to learn the price-demand relationship for itself and its competitors, and to set optimal prices, taking into account its competitors’ likely moves. We follow a closed-loop approach to capture the transient aspect of the problem, that is, pricing decisions are updated dynamically over time, using the data acquired thus far. We formulate the problem faced at each time period by each firm as a Mathematical Program with Equilibrium Constraints (MPEC). We utilize variational inequalities to capture the game-theoretic aspect of the problem. We present computational results that provide insights on the model and illustrate the pricing policies this model gives rise to.

Suggested Citation

  • Soulaymane Kachani & Georgia Perakis & Carine Simon, 2007. "Modeling the Transient Nature of Dynamic Pricing with Demand Learning in a Competitive Environment," International Series in Operations Research & Management Science, in: Terry L. Friesz (ed.), Network Science, Nonlinear Science and Infrastructure Systems, chapter 0, pages 223-267, Springer.
    • Handle: RePEc:spr:isochp:978-0-387-71134-8_11
    DOI: 10.1007/0-387-71134-1_11
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    Cited by:

    1. Elodie Adida & Georgia Perakis, 2010. "Dynamic pricing and inventory control: robust vs. stochastic uncertainty models—a computational study," Annals of Operations Research, Springer, vol. 181(1), pages 125-157, December.

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