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Tacit Collusion on Steroids: The Potential Risks for Competition Resulting from the Use of Algorithm Technology by Companies

Author

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  • Christophe Samuel Hutchinson

    (Department of Legal Regulation of Economic Activity, Financial University under the Government of the Russian Federation, 125167 Moscow, Russia)

  • Gulnara Fliurovna Ruchkina

    (Law Faculty, Financial University under the Government of the Russian Federation, 125167 Moscow, Russia)

  • Sergei Guerasimovich Pavlikov

    (Department of Legal Regulation of Economic Activity, Financial University under the Government of the Russian Federation, 125167 Moscow, Russia)

Abstract

Digitalization has a growing impact on everyone’s life. It influences the way consumers purchase products, read online news, access multimedia content, and even meet or interact socially. At the core of digital products lies algorithm technology, decision-making software capable of fulfilling multiple tasks: data mining, result ranking, user matching, dynamic pricing, product recommendations, and ads targeting, among others. Notwithstanding the perceived benefits of algorithms for the economy, the question has been raised of whether the use of algorithms by businesses might have countervailing effects on competition. Although any anti-competitive behavior typically observed in traditional markets can be implemented by this technology, a particular issue highlighted in discussions between researchers and practitioners is the concern that algorithms might foster collusion. Because of their capacity to increase market transparency and the frequency of interactions between competing firms, they can be used to facilitate parallel collusive behavior while dispensing competing firms with the need for explicit communication. Consequently, it is not excluded that algorithms will be used in the years to come to obtain the effects of a cartel without the need to enter into restrictive agreements or to engage in concerted practices. We evaluate the collusion risks associated with the use of algorithms and discuss whether the “agreement for antitrust purposes” concept needs revisiting. The more firms made use of types of algorithms that enable direct and indirect communication between the competitors, the more likely those companies may be considered liable.

Suggested Citation

  • Christophe Samuel Hutchinson & Gulnara Fliurovna Ruchkina & Sergei Guerasimovich Pavlikov, 2021. "Tacit Collusion on Steroids: The Potential Risks for Competition Resulting from the Use of Algorithm Technology by Companies," Sustainability, MDPI, vol. 13(2), pages 1-14, January.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:951-:d:482665
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    References listed on IDEAS

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    1. Ivaldi, Marc & Jullien, Bruno & Rey, Patrick & Seabright, Paul & Tirole, Jean, 2003. "The Economics of Tacit Collusion," IDEI Working Papers 186, Institut d'Économie Industrielle (IDEI), Toulouse.
    2. Hellwig, Michael & Hüschelrath, Kai, 2016. "Cartel cases and the cartel enforcement process in the European Union 2001-2015: A quantitative assessment," ZEW Discussion Papers 16-063, ZEW - Leibniz Centre for European Economic Research.
    3. Joseph E Harrington, 2018. "Developing Competition Law For Collusion By Autonomous Artificial Agents," Journal of Competition Law and Economics, Oxford University Press, vol. 14(3), pages 331-363.
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