Authors:

V.V. Gerasimenko, K.V. Simonov

Lomonosov Moscow State University, Moscow, Russia

Abstract:

Transferring pricing functions to self-learning AI systems can result in algorithmic price collusion on oligopolistic markets. The article aims to identify the prerequisites for algorithmic price collusion and formulate comprehensive recommendations for its prevention, detection, and mitigation of its consequences within Russia’s current information technology, economic and legal environment. The research methodology is based on the concepts of algorithmic pricing and tacit collusion, the theory of industrial markets, and antitrust regulations. The methods used include a comparison of classical cartel agreement mechanisms and algorithmic pricing models, online survey, and statistical hypothesis testing using the Pearson test and binary logistic regression. The evidence base covers the results of a survey of 161 Russian specialists held in April 2025 – April 2026. The empirical findings show that 40% of Russian business community representatives believe that algorithmic price collusion is already possible today; among respondents using algorithmic pricing, this share is about 70%. The study identifies the direction of institutional and legal changes: enshrining the concepts of tacit collusion and digital cartel in legislation; introducing mandatory audits of pricing algorithms; allocating responsibility between their developers and users; and equipping antitrust authorities with the powers to test algorithms and access data concerning their operation. The study proposes approaches to algorithm auditing grounded in price reaction asymmetry, price synchronicity, and short-term price wars. They shift the focus from collusion-specific communications to testing automated systems and algorithms. The developed approaches can be used by antitrust authorities to investigate machine cartels and protect competition.

Keywords: algorithmic pricing; price collusion; digital cartel; self-learning algorithms; antitrust regulation; oligopolistic market; tacit collusion; algorithm auditing; artificial intelligence.

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For citation: Gerasimenko V.V., Simonov K.V. (2026). The role of artificial intelligence in pricing in Russian oligopolistic markets. Upravlenets / The Manager, vol. 17, no. 5, pp. 16–32. DOI: 10.29141/2218-5003-2026-17-5-2. EDN: WAICUD.