IDEAS home Printed from https://ideas.repec.org/p/gre/wpaper/2022-32.html
   My bibliography  Save this paper

From Economic Evidence to Algorithmic Evidence: Artificial Intelligence and Blockchain: An Application to Anti-competitive Agreements

Author

Listed:
  • Frédéric Marty

    (Université Côte d'Azur, France
    GREDEG CNRS)

Abstract

This contribution considers how anti-competitive agreements can be impacted by algorithms, especially those that use artificial intelligence, and by the use of blockchains. In both cases, the aim is to analyze how these technical devices can contribute to the consolidation of agreements, how they can hinder the supervision exercised by -competition authorities and the effectiveness of their tools to unravel cartels, and finally how they can be used to augment this oversight.

Suggested Citation

  • Frédéric Marty, 2022. "From Economic Evidence to Algorithmic Evidence: Artificial Intelligence and Blockchain: An Application to Anti-competitive Agreements," GREDEG Working Papers 2022-32, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    • Handle: RePEc:gre:wpaper:2022-32
    as

    Download full text from publisher

    File URL: http://195.220.190.85/GREDEG-WP-2022-32.pdf
    File Function: First version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gallego, Jorge & Rivero, Gonzalo & Martínez, Juan, 2021. "Preventing rather than punishing: An early warning model of malfeasance in public procurement," International Journal of Forecasting, Elsevier, vol. 37(1), pages 360-377.
    2. Huber, Martin & Imhof, David, 2019. "Machine learning with screens for detecting bid-rigging cartels," International Journal of Industrial Organization, Elsevier, vol. 65(C), pages 277-301.
    3. Stephanie Assad & Robert Clark & Daniel Ershov & Lei Xu, 2020. "Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market," CESifo Working Paper Series 8521, CESifo.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bantle, Melissa, 2024. "Screen for collusive behavior: A machine learning approach," Hohenheim Discussion Papers in Business, Economics and Social Sciences 01-2024, University of Hohenheim, Faculty of Business, Economics and Social Sciences.
    2. Hannes Wallimann & Silvio Sticher, 2023. "On suspicious tracks: machine-learning based approaches to detect cartels in railway-infrastructure procurement," Papers 2304.11888, arXiv.org.
    3. Martin Huber & David Imhof & Rieko Ishii, 2022. "Transnational machine learning with screens for flagging bid‐rigging cartels," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1074-1114, July.
    4. Timo Klein, 2021. "Autonomous algorithmic collusion: Q‐learning under sequential pricing," RAND Journal of Economics, RAND Corporation, vol. 52(3), pages 538-558, September.
    5. Bedri Kamil Onur Tas, 2024. "A machine learning approach to detect collusion in public procurement with limited information," Journal of Computational Social Science, Springer, vol. 7(2), pages 1913-1935, October.
    6. Max Berre, 2022. "Which Factors Matter Most? Can Startup Valuation be Micro-Targeted?," Post-Print hal-03829877, HAL.
    7. Bingyan Han, 2021. "Understanding algorithmic collusion with experience replay," Papers 2102.09139, arXiv.org, revised Mar 2021.
    8. Marcel Wieting & Geza Sapi, 2021. "Algorithms in the Marketplace: An Empirical Analysis of Automated Pricing in E-Commerce," Working Papers 21-06, NET Institute.
    9. Yiquan Gu & Leonardo Madio & Carlo Reggiani, 2019. "Exclusive Data, Price Manipulation and Market Leadership," CESifo Working Paper Series 7853, CESifo.
    10. Werner, Tobias, 2021. "Algorithmic and human collusion," DICE Discussion Papers 372, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    11. Drolsbach, Chiara Patricia & Gail, Maximilian Maurice & Klotz, Phil-Adrian, 2023. "Pass-through of Temporary Fuel Tax Reductions: Evidence from Europe," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277655, Verein für Socialpolitik / German Economic Association.
    12. Martinez-Carrasco, José & ConceiçaÞo, Otavio & Dezolt, Ana Lúcia, 2023. "More Information, Lower Price? Access Market-based Reference Prices and Gains in Public Procurement Efficiency," IDB Publications (Working Papers) 12754, Inter-American Development Bank.
    13. Martin, Simon & Rasch, Alexander, 2022. "Collusion by algorithm: The role of unobserved actions," DICE Discussion Papers 382, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    14. Gallego, J & Prem, M & Vargas, J. F., 2022. "Predicting Politicians Misconduct: Evidence From Colombia," Documentos de Trabajo 20504, Universidad del Rosario.
    15. Belev, S. & Veterinarov, V. & Matveev, E., 2023. "Vertical collusion in public procurement: Estimation based on data for R&D composite auctions," Journal of the New Economic Association, New Economic Association, vol. 59(2), pages 36-63.
    16. Marcus Buckmann & Andy Haldane & Anne-Caroline Hüser, 2021. "Comparing minds and machines: implications for financial stability," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 37(3), pages 479-508.
    17. Martin, Simon & Schmal, W. Benedikt, 2021. "Collusive compensation schemes aided by algorithms," DICE Discussion Papers 375, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    18. Gallego, J & Prem, M & Vargas, J. F, 2020. "Corruption in the times of pandemia," Documentos de Trabajo 18178, Universidad del Rosario.
    19. Bovin, Andreas & Bos, Iwan, 2023. "Market Shares as Collusive Marker: Evidence from the European Truck Industry," Research Memorandum 011, Maastricht University, Graduate School of Business and Economics (GSBE).
    20. Martin, Simon & Rasch, Alexander, 2024. "Demand forecasting, signal precision, and collusion with hidden actions," International Journal of Industrial Organization, Elsevier, vol. 92(C).

    More about this item

    Keywords

    algorithms; blockchains; cartels; collusive agreements; leniency programs; facilitating practices;
    All these keywords.

    JEL classification:

    • K21 - Law and Economics - - Regulation and Business Law - - - Antitrust Law
    • L41 - Industrial Organization - - Antitrust Issues and Policies - - - Monopolization; Horizontal Anticompetitive Practices
    • L42 - Industrial Organization - - Antitrust Issues and Policies - - - Vertical Restraints; Resale Price Maintenance; Quantity Discounts

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gre:wpaper:2022-32. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Patrice Bougette (email available below). General contact details of provider: https://edirc.repec.org/data/credcfr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.