The combination of big data with technologically advanced tools, such as pricing algorithms, is increasingly diffused in everyone’s life today. This is changing the competitive landscape in which many companies operate and the way in which they make commercial and strategic decisions.
While the size of this phenomenon is to a large extent unknown, a growing number of firms are using computer algorithms to improve their pricing models, customise services and predict market trends. This phenomenon is undoubtedly associated to significant efficiencies, which benefit firms as well as consumers in terms of new, better and more tailored products and services.
However, a widespread use of algorithms has also raised concerns of possible anti-competitive behaviour as they can make it easier for firms to achieve and sustain collusion without any formal agreement or human interaction. This paper focuses on the question of whether algorithms can make tacit collusion easier, both in oligopolistic markets and in markets which do not manifest the structural features usually associated with the risk of collusion.
This paper discusses some of the challenges algorithms present for both competition law enforcement and market regulation. In particular, the paper addresses the question of whether antitrust agencies should revise the traditional concepts of agreement and tacit collusion for antitrust purposes, and discusses how traditional antitrust tools might be used to tackle some forms of algorithmic collusion. Recognising the multiple risks of algorithms and machine learning for society, the paper also raises the question of whether there is need to regulate algorithms and the possible consequences that such a policy choice may have on competition and innovation.
Algorithms are fundamentally affecting market conditions, resulting in high price transparency and high-frequency trading that allows companies to react fast and aggressively
Algorithms have the potential to enable the same outcomes as traditional hard core cartels through tacit collusion by providing companies with powerful automated mechanisms to monitor prices, implement common policies, send market signals or optimise joint profits with deep learning techniques
Given the multi-dimensional nature of algorithms, policy approaches should be developed in co-operation with competition law enforcers, consumer protection authorities, data protection agencies, relevant sectorial regulators and organisations of computer science with expertise in deep learning.