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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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    References listed on IDEAS

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    1. Robert Clark & Jean-Fran?ois Houde, 2013. "Collusion with Asymmetric Retailers: Evidence from a Gasoline Price-Fixing Case," American Economic Journal: Microeconomics, American Economic Association, vol. 5(3), pages 97-123, August.
    2. David P. Byrne & Nicolas de Roos, 2019. "Learning to Coordinate: A Study in Retail Gasoline," American Economic Review, American Economic Association, vol. 109(2), pages 591-619, February.
    3. Robert Clark & Jean-François Houde, 2014. "The Effect of Explicit Communication on pricing: Evidence from the Collapse of a Gasoline Cartel," Journal of Industrial Economics, Wiley Blackwell, vol. 62(2), pages 191-228, June.
    4. Johnson, Justin Pappas & Rhodes, Andrew & Wildenbeest, Matthijs, 2020. "Platform Design when Sellers Use Pricing Algorithms," TSE Working Papers 20-1146, Toulouse School of Economics (TSE).
    5. Stephanie Assad & Robert Clark & Daniel Ershov & Lei Xu, 2020. "Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market," Working Paper 1438, Economics Department, Queen's University.
    6. Jeanine Miklós-Thal & Catherine Tucker, 2019. "Collusion by Algorithm: Does Better Demand Prediction Facilitate Coordination Between Sellers?," Management Science, INFORMS, vol. 65(4), pages 1552-1561, April.
    7. Catherine Tucker, 2008. "Identifying Formal and Informal Influence in Technology Adoption with Network Externalities," Management Science, INFORMS, vol. 54(12), pages 2024-2038, December.
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    Citations

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    Cited by:

    1. 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.
    2. Werner, Tobias, 2023. "Algorithmic and Human Collusion," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277573, Verein für Socialpolitik / German Economic Association.
    3. 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).
    4. Simon Martin & Alexander Rasch, 2022. "Collusion by Algorithm: The Role of Unobserved Actions," CESifo Working Paper Series 9629, CESifo.
    5. Rhodes, Andrew, 2023. "A Survey on Drip Pricing and Other False Advertising," TSE Working Papers 23-1434, Toulouse School of Economics (TSE).
    6. Frédéric Marty & Thierry Warin, 2023. "Deciphering Algorithmic Collusion: Insights from Bandit Algorithms and Implications for Antitrust Enforcement," CIRANO Working Papers 2023s-26, CIRANO.

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    More about this item

    Keywords

    algorithmic pricing; antitrust; competition policy; artificial intelligence; collusion; platforms;
    All these keywords.

    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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