Abstract

We study how recommendation algorithms affect trade and welfare in markets characterized by algorithmic consumption, such as e-commerce platforms and AI assistants. Our analysis begins with a model of bilateral trade in which a single product is exchanged between a buyer and a seller under uncertainty about product value and seller cost. An algorithm recommends the product based on its price and estimated buyer value, thereby steering purchasing decisions. We characterize the buyer-optimal algorithm and show that it deliberately biases recommendations to amplify buyer price sensitivity, inducing lower seller prices. This optimal algorithm strategically deviates from the ex post optimal rule to exploit price pressure and enhance buyer surplus.

Reference

Shota Ichihashi, and Alex Smolin, Buyer-Optimal Algorithmic Recommendations, The 26rd ACM Conference on Economics and Computation, July 2025, p. 667.