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Autonomous algorithmic collusion: economic research and policy implications

Author

Listed:
  • Stephanie Assad
  • Emilio Calvano
  • Giacomo Calzolari
  • Robert Clark
  • Vincenzo Denicolò
  • Daniel Ershov
  • Justin Johnson
  • Sergio Pastorello
  • Andrew Rhodes
  • Lei Xu
  • Matthijs Wildenbeest

Abstract

Markets are being populated with new generations of pricing algorithms, powered with artificial intelligence (AI), that have the ability to autonomously learn to operate. This ability can be both a source of efficiency and cause of concern for the risk that algorithms autonomously and tacitly learn to collude. In this paper we explore recent developments in the economic literature and discuss implications for policy.

Suggested Citation

  • Stephanie Assad & Emilio Calvano & Giacomo Calzolari & Robert Clark & Vincenzo Denicolò & Daniel Ershov & Justin Johnson & Sergio Pastorello & Andrew Rhodes & Lei Xu & Matthijs Wildenbeest, 2021. "Autonomous algorithmic collusion: economic research and policy implications," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 37(3), pages 459-478.
    • Handle: RePEc:oup:oxford:v:37:y:2021:i:3:p:459-478.
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    File URL: http://hdl.handle.net/10.1093/oxrep/grab011
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    Citations

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    Cited by:

    1. Martin, Simon & Rasch, Alexander, 2024. "Demand forecasting, signal precision, and collusion with hidden actions," International Journal of Industrial Organization, Elsevier, vol. 92(C).
    2. 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.
    3. Simon Martin & Alexander Rasch, 2022. "Collusion by Algorithm: The Role of Unobserved Actions," CESifo Working Paper Series 9629, CESifo.
    4. N. M. Rozanova, 2025. "Digital Economy Cartels," Studies on Russian Economic Development, Springer, vol. 36(1), pages 135-140, February.
    5. Rhodes, Andrew, 2023. "A Survey on Drip Pricing and Other False Advertising," TSE Working Papers 23-1434, Toulouse School of Economics (TSE).
    6. Aleksandar B. Todorov, 2022. "Algorithmic pricing and concerted behaviour – competitive challenges?," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 1, pages 90-107.
    7. Werner, Tobias, 2023. "Algorithmic and Human Collusion," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277573, Verein für Socialpolitik / German Economic Association.
    8. Bougette, Patrice & Budzinski, Oliver & Marty, Frédéric, 2025. "Ex-ante versus Ex-post in competition law enforcement: Blurred boundaries and economic rationale," International Review of Law and Economics, Elsevier, vol. 82(C).
    9. Zexin Ye, 2025. "Algorithmic Collusion under Observed Demand Shocks," Papers 2502.15084, arXiv.org, revised Dec 2025.
    10. Robin Ng, 2025. "Competition Through Recommendations," CRC TR 224 Discussion Paper Series crctr224_2025_718, University of Bonn and University of Mannheim, Germany.
    11. Frédéric Marty, 2023. "Deciphering Algorithmic Collusion: Insights from Bandit Algorithms and Implications for Antitrust Enforcement," Working Papers halshs-04363106, HAL.

    More about this item

    Keywords

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    JEL classification:

    • D42 - Microeconomics - - Market Structure, Pricing, and Design - - - Monopoly
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • L42 - Industrial Organization - - Antitrust Issues and Policies - - - Vertical Restraints; Resale Price Maintenance; Quantity Discounts

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