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Title: Dynamic pricing and learning with competition: insights from the dynamic pricing challenge at the 2017 INFORMS RM & pricing conference
Author: van de Geer, Ruben
den Boer, Arnoud V.
Bayliss, Christopher
Currie, Christine S. M.
Ellina, Andria
Esders, Malte
Haensel, Alwin
Xiao, Lei
Maclean, Kyle D. S.
Martinez Sykora, Antonio
Riseth, Asbjorn Nilsen
Odegaard, Fredrik
Zachariades, Simos
Others: Universitat Oberta de Catalunya (UOC)
Vrije Universiteit Amsterdam
University of Amsterdam
University of Southampton
Technische Universität Berlin
Advanced Mathematical Solutions
Columbia University
Ivey Business School
University of Oxford
Keywords: marketplaces
competitive environment
Issue Date: 16-Oct-2018
Publisher: Journal of Revenue and Pricing Management
Citation: Van de Geer, R., Den Boer, Arnoud V. , Bayliss, C., Currie, C., Ellina, A., Esders, M., Haensel, A., Xiao, L., Maclean, K. D. S., Martínez Sykora, A., Riseth, A. N., Odegaard, F. & Zachariades, S. (2018). Dynamic pricing and learning with competition: insights from the dynamic pricing challenge at the 2017 INFORMS RM & pricing conference. Journal of Revenue and Pricing Management, (), 1-19. doi: 10.1057/s41272-018-00164-4
Project identifier: info:eu-repo/grantAgreement/EP/N006461/1
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Abstract: This paper presents the results of the Dynamic Pricing Challenge, held on the occasion of the 17th INFORMS Revenue Management and Pricing Section Conference on June 29-30, 2017 in Amsterdam, The Netherlands. For this challenge, participants submitted algorithms for pricing and demand learning of which the numerical performance was analyzed in simulated market environments. This allows consideration of market dynamics that are not analytically tractable or can not be empirically analyzed due to practical complications. Our findings implicate that the relative performance of algorithms varies substantially across different market dynamics, which confirms the intrinsic complexity of pricing and learning in the presence of competition.
Language: English
ISSN: 1476-6930MIAR

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