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

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Network Science, Nonlinear Science and Infrastructure Systems

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 102))

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.

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Author information

Authors and Affiliations

  1. IEOR Department, Columbia University, New York, NY

    Soulaymane Kachani

  2. MIT Sloan School of Management, Cambridge, MA

    Georgia Perakis

  3. MIT Operations Research Center, Cambridge, MA

    Carine Simon

Authors
  1. Soulaymane Kachani
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  2. Georgia Perakis
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  3. Carine Simon
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Corresponding author

Correspondence to Soulaymane Kachani .

Editor information

Editors and Affiliations

  1. Pennsylvania State University, University Park, PA, USA

    Terry L. Friesz

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Kachani, S., Perakis, G., Simon, C. (2007). Modeling the Transient Nature of Dynamic Pricing with Demand Learning in a Competitive Environment. In: Friesz, T.L. (eds) Network Science, Nonlinear Science and Infrastructure Systems. International Series in Operations Research & Management Science, vol 102. Springer, Boston, MA. https://doi.org/10.1007/0-387-71134-1_11

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