Algorithmic Recommendations and Strategic Pricing

Abstract

We study algorithmic recommendations in a market with a buyer, privately informed sellers, and an algorithm that can recommend a product based on product values and posted prices but cannot use transfers or force purchases. For objectives ranging from total surplus to buyer surplus, algorithmic recommendations implement the same welfare outcomes as direct mechanisms. We study the effects of algorithmic recommendations on pricing, competition, the composition of trade, and market segmentation. Our results inform the operation of AI assistants and other technologies that mediate product discovery, attention, and pricing.