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Dynamic Pricing and Learning with Competition: Insights from the Dynamic Pricing Challenge at the 2017 INFORMS RM & Pricing Conference

Author

Listed:
  • Ruben van de Geer
  • Arnoud V. den Boer
  • Christopher Bayliss
  • Christine Currie
  • Andria Ellina
  • Malte Esders
  • Alwin Haensel
  • Xiao Lei
  • Kyle D. S. Maclean
  • Antonio Martinez-Sykora
  • Asbj{o}rn Nilsen Riseth
  • Fredrik {O}degaard
  • Simos Zachariades

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.

Suggested Citation

  • Ruben van de Geer & Arnoud V. den Boer & Christopher Bayliss & Christine Currie & Andria Ellina & Malte Esders & Alwin Haensel & Xiao Lei & Kyle D. S. Maclean & Antonio Martinez-Sykora & Asbj{o}rn Nil, 2018. "Dynamic Pricing and Learning with Competition: Insights from the Dynamic Pricing Challenge at the 2017 INFORMS RM & Pricing Conference," Papers 1804.03219, arXiv.org.
  • Handle: RePEc:arx:papers:1804.03219
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    References listed on IDEAS

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    Cited by:

    1. Ian Yeoman, 2019. "Algorithms," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 18(3), pages 183-184, June.

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