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Journal Article

Remedies for Algorithmic Tacit Collusion


Beneke,  Francisco
MPI for Innovation and Competition, Max Planck Society;


Mackenrodt,  Mark-Oliver
MPI for Innovation and Competition, Max Planck Society;

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Beneke, F., & Mackenrodt, M.-O. (2020). Remedies for Algorithmic Tacit Collusion. Journal of Antitrust Enforcement. doi:10.1093/jaenfo/jnaa040.

Cite as: http://hdl.handle.net/21.11116/0000-0007-DCF6-C
There is growing evidence that tacit collusion can be autonomously achieved by machine learning technology, at least in some real-life examples identified in the literature and experimental settings. Although more work needs to be done to assess the competitive risks of widespread adoption of autonomous pricing agents, this is still an appropriate time to examine which possible remedies can be used in case competition law shifts towards the prohibition of tacit collusion. This is because outlawing such conduct is pointless unless there are suitable remedies that can be used to address the social harm. This article explores how fines and structural and behavioural remedies can serve to discourage collusive results while preserving the incentives to use efficiency-enhancing algorithms. We find that this could be achieved if fines and remedies can target structural conditions that facilitate collusion. In addition, the problem of unfeasibility of injunctions to remedy traditional price coordination changes with the use of pricing software, which in theory can be programmed to avoid collusive outcomes. Finally, machine-learning methods can be used by the authorities themselves as a tool to test the effects of any given combination of remedies and to estimate a more accurate competitive benchmark for the calculation of the appropriate fine.