NDLI: Demand Learning and Dynamic Pricing under Competition in a State-Space Framework

Content Provider	IEEE Xplore Digital Library
Author	Byung Do Chung Jiahan Li Tao Yao Changhyun Kwon Friesz, T.L.
Copyright Year	1988
Abstract	In this paper, we propose a revenue optimization framework integrating demand learning and dynamic pricing for firms in monopoly or oligopoly markets. We introduce a state-space model for this revenue management problem, which incorporates game-theoretic demand dynamics and nonparametric techniques for estimating the evolution of underlying state variables. Under this framework, stringent model assumptions are removed. We develop a new demand learning algorithm using Markov chain Monte Carlo methods to estimate model parameters, unobserved state variables, and functional coefficients in the nonparametric part. Based on these estimates, future price sensitivities can be predicted, and the optimal pricing policy for the next planning period is obtained. To test the performance of demand learning strategies, we solve a monopoly firm's revenue maximizing problem in simulation studies. We then extend this paradigm to dynamic competition, where the problem is formulated as a differential variational inequality. Numerical examples show that our demand learning algorithm is efficient and robust.
Sponsorship	IEEE Technology Management Council
Starting Page	240
Ending Page	249
Page Count	10
File Size	374411
File Format	PDF
ISSN	00189391
Volume Number	59
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mathematical model Equations Sensitivity Pricing Estimation Markov processes Predictive models nonlinear time series Competition demand learning differential variational inequality dynamic pricing Markov chain Monte Carlo
Content Type	Text
Resource Type	Article
Subject	Strategy and Management Electrical and Electronic Engineering

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