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Algorithms and Antitrust: a Framework with Special Emphasis on Coordinated Pricing

Roman Inderst & Stefan Thomas

Journal of Competition Law and Economics2025https://doi.org/10.1093/joclec/nhaf014article
AJG 1ABDC B
Weight
0.37

Abstract

The debate about algorithmic collusion has solidified to a state where agencies like the European Commission or the UK CMA have acknowledged its relevance for the cartel prohibition in their latest Horizontal Guidelines. In addition, national legislators in Germany and Italy have, only recently, enacted special antitrust provisions to address, among other things, algorithmic collusion even outside the scope of the cartel prohibition. We argue that established legal principles behind the definition of cartel conduct are challenged by the means and forms of how algorithms can impact pricing. We put forward that key for any case analysis under the cartel prohibition and the new type of legislation is a counterfactual assessment, which reflects the capabilities of artificial intelligence-based pricing technology. Such counterfactual assessment hinges on the type of pricing algorithms and the effects that the blocking of certain functions of algorithmic pricing would have on consumer welfare. We develop a taxonomy of cases for the cartel prohibition, and we describe paradigms for the development of remedies within the scope of the new legislation.

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https://doi.org/https://doi.org/10.1093/joclec/nhaf014

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@article{roman2025,
  title        = {{Algorithms and Antitrust: a Framework with Special Emphasis on Coordinated Pricing}},
  author       = {Roman Inderst & Stefan Thomas},
  journal      = {Journal of Competition Law and Economics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1093/joclec/nhaf014},
}

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Algorithms and Antitrust: a Framework with Special Emphasis on Coordinated Pricing

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.