  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.
