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Algorithmic Pricing and Competition: Balancing Efficiency and Consumer Welfare

Author

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  • Frédéric Marty

    (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (1965 - 2019) - CNRS - Centre National de la Recherche Scientifique - UniCA - Université Côte d'Azur, CIRANO - Centre interuniversitaire de recherche en analyse des organisations [Montréal, Canada] = Center for Interuniversity Research and Analysis on Organizations [Montréal, Canada])

  • Thierry Warin

    (HEC Montréal - HEC Montréal, CIRANO - Centre interuniversitaire de recherche en analyse des organisations [Montréal, Canada] = Center for Interuniversity Research and Analysis on Organizations [Montréal, Canada])

Abstract

This article examines the competitive implications of algorithmic pricing in digital markets. While algorithmic pricing can enhance market efficiency through real-time adjustments, personalized offers, and inventory optimization, it also raises substantial risks, including tacit collusion, discriminatory pricing, market segmentation, and exploitative consumer manipulation. Drawing on theoretical models, simulations, and emerging empirical evidence, the brief explores how algorithmic strategies may lead to supra- competitive prices without explicit coordination, particularly in oligopolistic or data-rich environments. It also highlights how common algorithm providers, shared data sources, and learning dynamics can undermine competition. Special attention is given to the challenges posed by loyalty penalties, ecosystem lock-in, and granular predatory pricing. The paper concludes with a set of policy recommendations emphasizing updated enforcement tools, transparency mechanisms, ex ante regulation for dominant platforms, and a coordinated approach to digital market oversight that balances innovation with consumer protection.

Suggested Citation

  • Frédéric Marty & Thierry Warin, 2025. "Algorithmic Pricing and Competition: Balancing Efficiency and Consumer Welfare," Working Papers halshs-05199988, HAL.
    • Handle: RePEc:hal:wpaper:halshs-05199988
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    References listed on IDEAS

    as
    1. Stephanie Assad & Robert Clark & Daniel Ershov & Lei Xu, 2024. "Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market," Journal of Political Economy, University of Chicago Press, vol. 132(3), pages 723-771.
    2. Emilio Calvano & Giacomo Calzolari & Vincenzo Denicolò & Sergio Pastorello, 2020. "Artificial Intelligence, Algorithmic Pricing, and Collusion," American Economic Review, American Economic Association, vol. 110(10), pages 3267-3297, October.
    3. Nathalie de Marcellis-Warin & Frédéric Marty & Eva Thelisson & Thierry Warin, 2022. "Artificial intelligence and consumer manipulations: from consumer's counter algorithms to firm's self-regulation tools," Post-Print halshs-03921216, HAL.
    4. Abada, Ibrahim & Lambin, Xavier & Tchakarov, Nikolay, 2024. "Collusion by mistake: Does algorithmic sophistication drive supra-competitive profits?," European Journal of Operational Research, Elsevier, vol. 318(3), pages 927-953.
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    Keywords

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    JEL classification:

    • L41 - Industrial Organization - - Antitrust Issues and Policies - - - Monopolization; Horizontal Anticompetitive Practices
    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • K21 - Law and Economics - - Regulation and Business Law - - - Antitrust Law
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation

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