ALGORITHMIC MECHANISMS OF UNFAIR COMPETITION IN DIGITAL MARKETS

Abstract

The article examines the transformation of unfair competition under conditions of digitalization and the growing dominance of platform-based business models. It argues that traditional anti-competitive practices increasingly acquire an algorithmic nature and are implemented through ranking systems, recommendation mechanisms, automated pricing tools, and large-scale data processing. The study identifies key algorithmic mechanisms of competitive distortion, including self-preferencing, visibility manipulation, algorithmic coordination, data opacity, interoperability restrictions, and the reinforcement of market power through network effects. It demonstrates that the interaction between data, algorithms, and platform infrastructure produces a new configuration of digital market power that challenges conventional competition law doctrines.

The paper develops an integrated economic and legal model of regulatory response that combines ex post antitrust enforcement with ex ante preventive obligations. The proposed model includes algorithmic risk detection, legal qualification of conduct, proportional remedial measures (transparency requirements, algorithmic audits, interoperability duties), and compliance monitoring. Particular attention is paid to the evidentiary challenges associated with algorithmic opacity and the need for adapted standards of digital proof. The scientific novelty of the research lies in the conceptualization of a systemic framework linking algorithmic mechanisms, digital market structures, regulatory instruments, and digital evidence in the context of contemporary antitrust control.