The Competition Complexity of Dynamic Pricing

Abstract

We study the competition complexity of dynamic pricing relative to the optimal auction in the fundamental single-item setting. In prophet inequality terminology, we compare the expected reward [Formula: see text] achievable by the optimal online policy on m independent and identically distributed (i.i.d.) random variables distributed according to F to the expected maximum [Formula: see text] of n i.i.d. draws from F. We ask how big m has to be to ensure that [Formula: see text] for all F. We resolve this question and characterize the competition complexity as a function of ε. When [Formula: see text], the competition complexity is unbounded. That is, for any n and m there is a distribution F such that [Formula: see text]. In contrast, for any [Formula: see text], it is sufficient and necessary to have [Formula: see text], where [Formula: see text]. Therefore, the competition complexity not only drops from unbounded to linear, it is actually linear with a very small constant. The technical core of our analysis is a lossless reduction to an infinite dimensional and nonlinear optimization problem that we solve optimally. A corollary of this reduction is a novel proof of the factor [Formula: see text] i.i.d. prophet inequality, which simultaneously establishes matching upper and lower bounds. Funding: This work was supported by ANID (Anillo ICMD) [Grant ACT210005] and the Center for Mathematical Modeling [Grant FB210005].