Universitat Oberta de Catalunya
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.
Martínez Sykora, Antonio
Riseth, Asbjorn Nilsen
Odegaard, Fredrik
Zachariades, Simos
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
2019-04-15T11:37:12Z
2019-04-15T11:37:12Z
2018-10-16
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.
Anglès
marketplaces; algorithms; competitive environment; learning; mercados; algoritmos; entorno competitivo; aprendizaje; mercats; algorismes; entorn competitiu; aprenentatge; Computer algorithms; Algorismes computacionals; Algoritmos computacionales
Journal of Revenue and Pricing Management
Journal of Revenue and Pricing Management, 2018 ()
http://arxiv.org/pdf/1804.03219
info:eu-repo/grantAgreement/EP/N006461/1
info:eu-repo/grantAgreement/EP/L015803/1
(c) Author/s & (c) Journal
Articles [361]
