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AI algorithms, price discrimination and collusion: a technological, economic and legal perspective

Author

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  • Gautier, Axel

    (Université catholique de Louvain, LIDAM/CORE, Belgium)

  • Ittoo, Ashwin
  • Van Cleynenbreugel, Pieter

Abstract

In recent years, important concerns have been raised about the increasing capabilities of pricing algorithms to make use of artificial intelligence (AI) technologies. Two issues have gained particular attention: algorithmic price discrimination (PD) and algorithmic tacit collusion (TC). Although the risks and opportunities of both practices have been explored extensively in the literature, neither has yet been observed in the actual practice. As a result, there remains much confusion as to the ability of algorithms to engage in potentially harmful behavior with respect to price discrimination and collusion. In this article, we embed the economic and legal literature on these topics in a technological grounding to provide a more objective account of the capabilities of current AI technologies to engage in price discrimination and collusion. We argue that attention to these current technological capabilities should more directly inform on-going discussions on the urgency to reform legal rules or enforcement practices governing algorithmic PD and TC.

Suggested Citation

  • Gautier, Axel & Ittoo, Ashwin & Van Cleynenbreugel, Pieter, 2020. "AI algorithms, price discrimination and collusion: a technological, economic and legal perspective," LIDAM Reprints CORE 3138, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvrp:3138
    DOI: https://doi.org/10.1007/s10657-020-09662-6
    Note: In: European Journal of Law and Economics, 2020, vol.50, p. 405-435
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    Citations

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    Cited by:

    1. Jason D. Hartline & Sheng Long & Chenhao Zhang, 2024. "Regulation of Algorithmic Collusion," Papers 2401.15794, arXiv.org.
    2. Alain Marciano & Antonio Nicita & Giovanni Battista Ramello, 2020. "Puzzles in the big data revolution: an introduction," European Journal of Law and Economics, Springer, vol. 50(3), pages 339-344, December.
    3. Simon Martin & Alexander Rasch, 2022. "Collusion by Algorithm: The Role of Unobserved Actions," CESifo Working Paper Series 9629, CESifo.
    4. Daehyeon Park & Doojin Ryu, 2022. "Supply chain ethics and transparency: An agent‐based model approach with Q‐learning agents," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(8), pages 3331-3337, December.
    5. 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.
    6. Laura Abrardi & Carlo Cambini & Laura Rondi, 2022. "Artificial intelligence, firms and consumer behavior: A survey," Journal of Economic Surveys, Wiley Blackwell, vol. 36(4), pages 969-991, September.
    7. Florian Peiseler & Alexander Rasch & Shiva Shekhar, 2022. "Imperfect information, algorithmic price discrimination, and collusion," Scandinavian Journal of Economics, Wiley Blackwell, vol. 124(2), pages 516-549, April.
    8. Nick Drydakis, 2022. "Artificial Intelligence and Reduced SMEs’ Business Risks. A Dynamic Capabilities Analysis During the COVID-19 Pandemic," Information Systems Frontiers, Springer, vol. 24(4), pages 1223-1247, August.
    9. Zhang Xu & Mingsheng Zhang & Wei Zhao, 2024. "Algorithmic Collusion and Price Discrimination: The Over-Usage of Data," Papers 2403.06150, arXiv.org.
    10. 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).

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