Algorithmic Collusion or Competition: the Role of Platforms' Recommender Systems

TL;DR

This paper investigates how online recommender systems influence competition and collusion among sellers, revealing that revenue-focused recommendations promote collusion while utility-focused ones foster competition, with complex effects on consumer welfare.

Contribution

It introduces a novel framework integrating recommender systems into a repeated game model, analyzing their impact on pricing strategies and market outcomes.

Findings

Revenue-maximizing recommenders increase collusion.

Utility-maximizing recommenders promote competition.

Increasing recommendation set size does not always improve consumer utility.

Abstract

Recent scholarly work has extensively examined the phenomenon of algorithmic collusion driven by AI-enabled pricing algorithms. However, online platforms commonly deploy recommender systems that influence how consumers discover and purchase products, thereby shaping the reward structures faced by pricing algorithms and ultimately affecting competition dynamics and equilibrium outcomes. To address this gap in the literature and elucidate the role of recommender systems, we propose a novel repeated game framework that integrates several key components. We first develop a structural search model to characterize consumers' decision-making processes in response to varying recommendation sets. This model incorporates both observable and unobservable heterogeneity in utility and search cost functions, and is estimated using real-world data. Building on the resulting consumer model, we formulate personalized recommendation algorithms designed to maximize either platform revenue or consumer utility. We further introduce pricing algorithms for sellers and integrate all these elements to facilitate comprehensive numerical experiments. Our experimental findings reveal that a revenue-maximizing recommender system intensifies algorithmic collusion, whereas a utility-maximizing recommender system encourages more competitive pricing behavior among sellers. Intriguingly, and contrary to conventional insights from the industrial organization and choice modeling literature, increasing the size of recommendation sets under a utility-maximizing regime does not consistently enhance consumer utility. Moreover, the degree of horizontal differentiation moderates this phenomenon in unexpected ways. The "more is less" effect does not arise at low levels of differentiation, but becomes increasingly pronounced as horizontal differentiation increases.