Learning competitive dynamic airline pricing under different consumer models
Learning competitive dynamic airline pricing under different consumer models
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.
game theory, customer demand, artificial intelligence, reinforcement learning, air transport
416-430
Collins, A
6f372e58-be5c-4d56-8e1f-ca4be1c30f8f
Thomas, Lyn C.
a3ce3068-328b-4bce-889f-965b0b9d2362
Collins, A
6f372e58-be5c-4d56-8e1f-ca4be1c30f8f
Thomas, Lyn C.
a3ce3068-328b-4bce-889f-965b0b9d2362
Collins, A and Thomas, Lyn C.
(2013)
Learning competitive dynamic airline pricing under different consumer models.
Journal of Revenue and Pricing Management, 12, .
(doi:10.1057/rpm.2013.10).
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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e-pub ahead of print date: 3 May 2013
Keywords:
game theory, customer demand, artificial intelligence, reinforcement learning, air transport
Organisations:
Centre of Excellence in Decision, Analytics & Risk Research
Identifiers
Local EPrints ID: 375183
URI: http://eprints.soton.ac.uk/id/eprint/375183
ISSN: 1476-6930
PURE UUID: 2a6233ce-577f-4baf-9418-caa3729dc456
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Date deposited: 16 Mar 2015 13:17
Last modified: 14 Mar 2024 19:21
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Author:
A Collins
Author:
Lyn C. Thomas
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