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Learning competitive dynamic airline pricing under different customer models

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Journal of Revenue and Pricing Management Aims and scope

Abstract

There is a tendency to focus on the overly simplistic dynamic airline pricing games or to even ignore competition completely, because of the difficulty in solving game theoretic models. Recent changes in the industry mean that airlines can no longer ignore competitors in their model. This article adds more complex customer model aspects – that is, customer choice using a logit model, customer demand using a linear probabilistic demand model and market size using a binary random function – into an existing solvable airline pricing game; originally, this game only used a simple customer model. The newly formed games were solved using a reinforcement learning algorithm with mixed results.

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Authors and Affiliations

  1. Old Dominion University, Suffolk, Virginia, USA

    Andrew Collins

  2. University of Southampton, Southampton, UK

    Lyn Thomas

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  1. Andrew Collins
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  2. Lyn Thomas
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Correspondence to Andrew Collins.

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2has been the Professor of Management Science at the University of Southampton since 2000. He was formerly the president of the Operational Research (OR) Society (1994–1995) and was awarded the Beale Medal of OR Society in 2008. Specialist research areas include credit scoring and credit control, and use of management science techniques in financial and banking areas.

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Collins, A., Thomas, L. Learning competitive dynamic airline pricing under different customer models. J Revenue Pricing Manag 12, 416–430 (2013). https://doi.org/10.1057/rpm.2013.10

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  • DOI: https://doi.org/10.1057/rpm.2013.10

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