AI Algorithmic Pricing for Online Platforms: A Literature Review
Lixun Song, C.L. Philip Chen, Federick Lin · Advances in Economics Management and Political Sciences · 2025
Rapid advances in Artificial Intelligence (AI) are reshaping the economic landscape, creating opportunities and challenges for businesses, consumers, and policymakers. These technologies are changing market dynamics by optimizing collusive pricing and altering the competitive landscape. Therefore, we summarized the relevant literature and explored the following questions in depth: Will algorithmic decision-making promote competition or lead to new market concentration and collusion forms? How will AI-driven automation affect dynamic pricing on online platforms? How does price discrimination compare to consumer behavior? What are the current regulatory challenges and antitrust laws? This research paper presents a well-structured review of existing literature on AI algorithmic pricing in online platforms and explores the economic implications. We first examine the evolution of pricing mechanisms, contrasting traditional models with AI-driven approaches, including dynamic and personalized pricing. Next, we consider the use of AI-driven algorithms in competition and explore how these algorithms bring about capacitation, leakage of price information, and market dominance. Then, we consider consumers' responses to algorithms through AI-based pricing, and we take into account the ethical issues like fairness, transparency, and perceptual biases. Finally, we analyse consists of identifying existing research gaps, the focus on the need to the regulatory adaptations and ethical considerations to comply with AI-driven pricing that creates the competitive and consumer-friendly online platform.