IDEAS home Printed from https://ideas.repec.org/a/bla/ecinqu/v63y2025i4p1036-1065.html

Tacit collusion by pricing algorithms

Author

Listed:
  • Bharat Bhole
  • Sunita Surana

Abstract

This article contributes to the debate about the potential of pricing algorithms to collude and earn supra‐competitive profits without explicit communication. By simulating competition among seven algorithms, we demonstrate that: (1) algorithms can reach supra‐competitive prices in a reasonably short time, taking less than 1/1,000th $1/1,00{0}^{\text{th}}$ the time taken by algorithms in recent studies; and (2) tacit collusion among the algorithms is robust to the choice of different algorithms by competing firms. These results address the main criticisms concerning the practical relevance of recent studies that demonstrate algorithmic collusion. The top‐performing algorithms possess properties of niceness, forgiveness, provocability, and flexibility.

Suggested Citation

  • Bharat Bhole & Sunita Surana, 2025. "Tacit collusion by pricing algorithms," Economic Inquiry, Western Economic Association International, vol. 63(4), pages 1036-1065, October.
  • Handle: RePEc:bla:ecinqu:v:63:y:2025:i:4:p:1036-1065
    DOI: 10.1111/ecin.70004
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/ecin.70004
    Download Restriction: no

    File URL: https://libkey.io/10.1111/ecin.70004?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Timo Klein, 2021. "Autonomous algorithmic collusion: Q‐learning under sequential pricing," RAND Journal of Economics, RAND Corporation, vol. 52(3), pages 538-558, September.
    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. 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.
    4. Jacob W. Crandall & Mayada Oudah & Tennom & Fatimah Ishowo-Oloko & Sherief Abdallah & Jean-François Bonnefon & Manuel Cebrian & Azim Shariff & Michael A. Goodrich & Iyad Rahwan, 2018. "Cooperating with machines," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
      • Abdallah, Sherief & Bonnefon, Jean-François & Cebrian, Manuel & Crandall, Jacob W. & Ishowo-Oloko, Fatimah & Oudah, Mayada & Rahwan, Iyad & Shariff, Azim & Tennom,, 2017. "Cooperating with Machines," TSE Working Papers 17-806, Toulouse School of Economics (TSE).
      • Abdallah, Sherief & Bonnefon, Jean-François & Cebrian, Manuel & Crandall, Jacob W. & Ishowo-Oloko, Fatimah & Oudah, Mayada & Rahwan, Iyad & Shariff, Azim & Tennom,, 2017. "Cooperating with Machines," IAST Working Papers 17-68, Institute for Advanced Study in Toulouse (IAST).
    5. Waltman, Ludo & Kaymak, Uzay, 2008. "Q-learning agents in a Cournot oligopoly model," Journal of Economic Dynamics and Control, Elsevier, vol. 32(10), pages 3275-3293, October.
    6. Arnoud V. den Boer & Janusz M. Meylahn & Maarten Pieter Schinkel, 2022. "Artificial Collusion: Examining Supracompetitive Pricing by Q-learning Algorithms," Tinbergen Institute Discussion Papers 22-067/VII, Tinbergen Institute.
    7. Ulrich Schwalbe, 2018. "Algorithms, Machine Learning, And Collusion," Journal of Competition Law and Economics, Oxford University Press, vol. 14(4), pages 568-607.
    8. Niklas Horstmann & Jan Krämer & Daniel Schnurr, 2018. "Number Effects and Tacit Collusion in Experimental Oligopolies," Journal of Industrial Economics, Wiley Blackwell, vol. 66(3), pages 650-700, September.
    9. Sigrid Suetens & Jan Potters, 2007. "Bertrand colludes more than Cournot," Experimental Economics, Springer;Economic Science Association, vol. 10(1), pages 71-77, March.
    10. Zach Y. Brown & Alexander MacKay, 2023. "Competition in Pricing Algorithms," American Economic Journal: Microeconomics, American Economic Association, vol. 15(2), pages 109-156, May.
    11. Davis, Douglas, 2009. "Pure numbers effects, market power, and tacit collusion in posted offer markets," Journal of Economic Behavior & Organization, Elsevier, vol. 72(1), pages 475-488, October.
    12. Matthias Hettich, 2021. "Algorithmic Collusion: Insights from Deep Learning," CQE Working Papers 9421, Center for Quantitative Economics (CQE), University of Muenster.
    13. Marc Harper & Vincent Knight & Martin Jones & Georgios Koutsovoulos & Nikoleta E Glynatsi & Owen Campbell, 2017. "Reinforcement learning produces dominant strategies for the Iterated Prisoner’s Dilemma," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-33, December.
    14. Werner, Tobias, 2023. "Algorithmic and Human Collusion," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277573, Verein für Socialpolitik / German Economic Association.
    15. 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.
    16. Christoph Engel, 2007. "How Much Collusion? A Meta-Analysis Of Oligopoly Experiments," Journal of Competition Law and Economics, Oxford University Press, vol. 3(4), pages 491-549.
    17. F. T. Dolbear & L. B. Lave & G. Bowman & A. Lieberman & E. Prescott & F. Rueter & R. Sherman, 1968. "Collusion in Oligopoly: An Experiment on the Effect of Numbers and Information," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 82(2), pages 240-259.
    18. Dufwenberg, Martin & Gneezy, Uri, 2000. "Price competition and market concentration: an experimental study," International Journal of Industrial Organization, Elsevier, vol. 18(1), pages 7-22, January.
    19. repec:hal:journl:hal-01897802 is not listed on IDEAS
    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. Ballestero, Gonzalo, 2026. "Algorithmic collusion under sequential pricing and stochastic costs," International Journal of Industrial Organization, Elsevier, vol. 106(C).
    2. Werner, Tobias, 2021. "Algorithmic and human collusion," DICE Discussion Papers 372, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    3. 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.
    4. Kevin Michael Frick, 2026. "Convergence to collusion in algorithmic pricing," Papers 2604.15825, arXiv.org, revised Oct 2026.
    5. Janusz M. Meylahn & Maximilian Schafer, 2026. "Equilibrium stability as a driver of cooperation among Q-learners," Papers 2607.13607, arXiv.org.
    6. Shidi Deng & Maximilian Schiffer & Martin Bichler, 2025. "Exploring Competitive and Collusive Behaviors in Algorithmic Pricing with Deep Reinforcement Learning," Papers 2503.11270, arXiv.org.
    7. Nobuyuki Hanaki & Aidas Masiliunas, 2021. "Market Concentration and Incentives to Collude in Cournot Oligopoly Experiments," ISER Discussion Paper 1131, Institute of Social and Economic Research, The University of Osaka.
    8. Gonzalo Ballestero, 2021. "Collusion and Artificial Intelligence: A computational experiment with sequential pricing algorithms under stochastic costs," Young Researchers Working Papers 1, Universidad de San Andres, Departamento de Economia, revised Oct 2022.
    9. Epivent, Andréa & Lambin, Xavier, 2024. "On algorithmic collusion and reward–punishment schemes," Economics Letters, Elsevier, vol. 237(C).
    10. John Asker & Chaim Fershtman & Ariel Pakes, 2024. "The impact of artificial intelligence design on pricing," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 33(2), pages 276-304, March.
    11. Fourberg, Niklas & Marques-Magalhaes, Katrin & Wiewiorra, Lukas, 2022. "They are among us: Pricing behavior of algorithms in the field," WIK Working Papers 6, WIK Wissenschaftliches Institut für Infrastruktur und Kommunikationsdienste GmbH, Bad Honnef.
    12. Normann, Hans-Theo & Sternberg, Martin, 2023. "Human-algorithm interaction: Algorithmic pricing in hybrid laboratory markets," European Economic Review, Elsevier, vol. 152(C).
    13. Dolgopolov, Arthur, 2024. "Reinforcement learning in a prisoner's dilemma," Games and Economic Behavior, Elsevier, vol. 144(C), pages 84-103.
    14. Fourberg, Niklas & Marques Magalhaes, Katrin & Wiewiorra, Lukas, 2023. "They Are Among Us: Pricing Behavior of Algorithms in the Field," 32nd European Regional ITS Conference, Madrid 2023: Realising the digital decade in the European Union – Easier said than done? 277958, International Telecommunications Society (ITS).
    15. Hans-Theo Normann & Martin Sternberg, 2021. "Human-Algorithm Interaction: Algorithmic Pricing in Hybrid Laboratory Markets," Discussion Paper Series of the Max Planck Institute for Behavioral Economics 2021_11, Max Planck Institute for Behavioral Economics, revised 13 Apr 2022.
    16. Gonzalo Ballestero, 2022. "Collusion and Artificial Intelligence: A Computational Experiment with Sequential Pricing Algorithms under Stochastic Costs," Working Papers 118, Red Nacional de Investigadores en Economía (RedNIE).
    17. Jackie Baek & Vivek F. Farias & Farrell Wu, 2026. "Misspecified Estimate-then-Optimize Leads to Supra-Competitive Prices," Papers 2605.16064, arXiv.org, revised Jun 2026.
    18. Thomas Loots & Arnoud V. den Boer, 2023. "Data‐driven collusion and competition in a pricing duopoly with multinomial logit demand," Production and Operations Management, Production and Operations Management Society, vol. 32(4), pages 1169-1186, April.
    19. Jan Potters & Sigrid Suetens, 2013. "Oligopoly Experiments In The Current Millennium," Journal of Economic Surveys, Wiley Blackwell, vol. 27(3), pages 439-460, July.
    20. Hangcheng Zhao & Ron Berman, 2025. "Algorithmic Collusion of Pricing and Advertising on E-commerce Platforms," Papers 2508.08325, arXiv.org, revised Oct 2025.

    More about this item

    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:bla:ecinqu:v:63:y:2025:i:4:p:1036-1065. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/weaaaea.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.