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

TL;DR

This paper analyzes the results of a competitive dynamic pricing challenge, highlighting the variability in algorithm performance across different market environments and emphasizing the complexity of pricing with competition.

Contribution

It provides empirical insights into the effectiveness of various pricing algorithms in simulated competitive markets, based on a large-scale challenge at a major conference.

Findings

Algorithm performance varies significantly across market types

Market dynamics greatly influence pricing algorithm success

Competition adds complexity to demand learning and pricing strategies

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.