Algorithmic Collusion or Competition: the Role of Platforms' Recommender Systems
This addresses a gap in understanding how recommender systems influence competition dynamics for platforms and sellers, offering insights into regulatory and design implications, though it is incremental by integrating existing models into a new framework.
The study investigates how online platforms' recommender systems affect algorithmic collusion or competition among sellers, finding that revenue-maximizing recommenders intensify collusion while utility-maximizing ones encourage competitive pricing, with a 'more is less' effect where larger recommendation sets under utility-maximization do not consistently boost consumer utility, especially as horizontal differentiation increases.
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.