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4 Stan. Computational Antitrust 1 (2024)

handle is hein.journals/stfdcmp4 and id is 1 raw text is: ARTICLE Overcoming the Current Knowledge Gap of Algorithmic Collusion and the Role of Computational Antitrust Renato Nazzini* and James Henderson** Abstract. Digital markets are evolving rapidly, and pricing algorithms are becoming prevalent. While they provide many benefits, there is a real threat of new harms and new challenges for antitrust authorities. Computational modelling has demonstrated these risks by showing that in many instances self-learning pricing algorithms lead to collusive outcomes. However, so far there has been woefully little empirical research into the dynamics of pricing algorithms. To provide context for this threat, we first review the usage and types of algorithmic pricing systems and critically examine the established taxonomy of algorithm-based collusion scenarios. We then describe how cartel screening techniques can be applied to algorithmic systems and the consequential logistical challenges and uncertainties. We propose action points needed to fill the knowledge gap. * Professor of Law, Dickson Poon School of Law, King's College London. ** PhD, Barrister, Research Assistant, Dickson Poon School of Law, King's College London.