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Dynamic pricing and learning with competition: insights from the dynamic pricing challenge at the 2017 INFORMS RM & pricing conference

Ruben Geer (), Arnoud V. Boer, Christopher Bayliss, Christine S. M. Currie, Andria Ellina, Malte Esders, Alwin Haensel, Xiao Lei, Kyle D. S. Maclean, Antonio Martinez-Sykora, Asbjørn Nilsen Riseth, Fredrik Ødegaard and Simos Zachariades
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Ruben Geer: Vrije Universiteit
Arnoud V. Boer: University of Amsterdam
Christopher Bayliss: Universitat Oberta de Catalunya
Christine S. M. Currie: University of Southampton
Andria Ellina: University of Southampton
Malte Esders: Technische Universität Berlin
Alwin Haensel: Haensel AMS, Advanced Mathematical Solutions
Xiao Lei: Columbia University
Kyle D. S. Maclean: Western University
Antonio Martinez-Sykora: University of Southampton
Asbjørn Nilsen Riseth: University of Oxford
Fredrik Ødegaard: Western University
Simos Zachariades: University of Southampton

Journal of Revenue and Pricing Management, 2019, vol. 18, issue 3, 185-203

Abstract: 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.

Keywords: Dynamic pricing; Learning; Competition; Numerical performance (search for similar items in EconPapers)
Date: 2019
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