Effects of Algorithmic Pricing on Platform Competition

Platforms play an essential role in the modern economy. At the same time, algorithms are becoming more widely used for pricing and other business functions. Previous literature examined algorithmic pricing, but not in the context of network effects and platforms. Moreover, platform competition literature has not considered how algorithms may affect competition. This paper considers algorithmic platform pricing and its effect on platform competition outcomes. We evaluate the impact of sophisticated AI-enabled pricing algorithms, specifically reinforcement learning (Q-learning) and evolutionary algorithms (PSO), as well as naïve algorithms (price-matching). When firms' algorithm choice is endogenous, we find and characterize two equilibria. Although all algorithms help platforms set an optimal price structure, we find no basis for general regulation of algorithmic pricing because different algorithms lead to different outcomes