Simonetta Vezzoso ‘Competition Policy in Transition: Exploring Data Portability’s Roles’ (2021) Journal of European Competition Law & Practice 12(5)

Several reform proposals circulated in the last two years recognise that data portability should play an increasingly important role in the digital economy. This paper, available here, explores data portability from an EU competition policy perspective. It points out that data portability can play three distinct roles, namely: (i) enabling switching, (ii) enabling data fluidity (iii) enhancing consumer empowerment and data sovereignty. These different roles are analysed against the background of (a) traditional competition law, (b) a market investigation regime, and (c) an ex-ante regulatory framework targeting large online platforms with gatekeeping power. Section II looks at the regulation of data portability, particularly non-personal data. Data can be either personal or non-personal. Personal data portability is a right under the GDPR. The data portability of non-personal data is foreseen by the EU Regulation on the Free Flow of Non-Personal Data in the European Union (Free Flow Regulation, or FFNPDR, in the following), which entered into force in May 2019. Besides…

Francisco Beneke and Mark-Oliver Mackenrodt on ‘Remedies for algorithmic tacit collusion’ (2021) Journal of Antitrust Enforcement 9 152

There is growing evidence that tacit collusion can be autonomously achieved by machine learning technology. However, outlawing such conduct is pointless unless there are suitable remedies to address them. This article, available here, explores how fines, and structural and behavioural remedies, can serve to discourage collusive outcomes while preserving incentives to use efficiency-enhancing algorithms. Section II provides a brief overview of the properties of deep learning methods and their applications to pricing decisions. Different machine learning methods can be usefully deployed to make pricing decisions. Reflecting statistical analysis, machine learning has the ability to automate pricing decisions by using hundreds or thousands of variables in ways that would be otherwise unavailable to market participants. Reinforcement learning may allow firms to automate pricing strategies according to variables such as reactions by competitors and the impact this has on profits or market share. As an algorithm’s problem-solving capabilities improve, market prices will tend to become more stable and converge to a price…