Algorithmic Pricing and AI-Driven Competitive Strategies: A Game Theory Review

Artificial Intelligence (AI) has revolutionized digital markets and pricing by empowering companies to leverage machine learning, reinforcement learning, and predictive analysis to automate strategic pricing decisions. Algorithmic pricing enhances efficiency, personalization and responsiveness but also poses risks of tacit collusion, anti-competitive coordination and regulatory shortcomings. In this review, the literature published from 2015 to 2025, related to algorithmic pricing and its game-theoretic perspective, is critically synthesised as a narrative review. The paper explores the interactions between AI systems in repeated strategic settings, the convergence of reinforcement learning agents towards collusive equilibria, and the amplification of algorithmic coordination in platform-based markets. This paper brings together game theory, AI governance, industrial organization and digital platform economics, as opposed to prior reviews such as Ezrachi and Stucke (2017) and the OECD (2017), which are more focused on either economic efficiency or antitrust implications. The review reveals that, despite empirical work, the literature does not have a satisfactory theoretical and policy understanding of how an algorithm can autonomously reach collusive equilibria, nor how it can be regulated by laws that assume intentionality in humans, thus making them inappropriate to govern the coordination that is generated by algorithms. To fill this void, the paper suggests the notion of ‘emergent strategic coordination’—that is, market coordination that is computationally produced through self-learning optimization processes in the absence of intentional collusion, a concept that builds on, rather than supplants, existing frameworks developed by Harrington (2018) and Ezrachi and Stucke (2017). The paper suggests that current antitrust laws have failed to keep up with AI’s strategic interactions, especially since they were drafted for humans rather than AI systems. The paper concludes that algorithmic pricing is much more than a new technology in business practices, it is a transformation in the nature of competition and the requirement for new business theory and regulatory frameworks.