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.