Nicolo Zingales ‘Antitrust intent in an age of algorithmic nudging’ (2019) Journal of Antitrust Enforcement 7 386

This article, available here, surveys EU case law on the role of anticompetitive intent in abuses of dominance, with the goal of understanding how intent can be relevant to the assignment of liability for anticompetitive algorithmic outcomes. The role of subjective intent in EU antitrust analysis remains controversial. Some argue that evidence of intent is an invaluable tool in the antitrust arsenal, allowing agencies and litigants to address anticompetitive conduct where facts are ambiguous or evidence of harm to competition inconclusive. Others warn against relying on intent. First, ‘sales talks’ encouraging employees to beat – and indeed eliminate – competitors is common and merely indicative of a (competitively desirable) aggressive business strategy. Secondly, banning any exhortation to compete aggressively would encourage firms to deploy more subtle forms of inducement when engaged in anticompetitive conduct, while favouring those with the resources to develop such strategies. The law seems to follow a middle path in this debate, suggesting that the notion of subjective…

Peter Georg Picht and Gaspare Tazio Loderer on ‘Framing Algorithms: Competition Law and (Other) Regulatory Tools’ (2019) World Competition 42(3) 391

Algorithmic market conduct, and intervene where algorithms risk distorting competition. In effect, the collusive potential of algorithms and algorithm-driven resale pricing have already been the subject of enforcement. However, it is still not clear whether competition law has, in its present form, the necessary tools and techniques adequately to control algorithms. This article, available here, looks at what other areas of the law, which are more advanced in this respect, can teach competition law. Its second section looks at how financial markets regulation and data protection law deal with algorithm-based market activity. Financial markets were among the first to deploy algorithms broadly and intensely. As a result, financial market regulation developed a comparatively detailed set of rules on algorithmic trading early on. European data protection law is another area that already has in place certain elements of a legal framework for algorithmic (market) activity. This includes the General Data Protection Regulation (GDPR) and the ePrivacy Regulation. These two regulatory regimes share…

Italy’s Big Data Report

This is a report published by Italian competition authority, together with the telecommunications regulator and the data protection authority, on how to address big data. It is available here. In my analysis below, I will focus on the elements of the report that touch or focus on competition law. I would also emphasise that this is not the first competition authority in Europe to look at data – the joint Franco-German report in 2016 also looked at the intersection between competition and data. The decision to pursue an interdisciplinary study arose from a recognition that the characteristics of the digital economy are very often such that it touches on the competences of the three authorities. The relationship between competition, privacy and pluralism requires a particularly close coordination between different regulators, not only to ensure effective regulatory action but also to identify and reconcile possible trade-offs between the values protected by different regulatory schemes. Furthermore, joint action will allow a better understanding of…

Ariel Ezrachi and Maurice E. Stucke ‘Sustainable and Unchallenged Algorithmic Tacit Collusion’ Oxford Legal Studies Research Paper No. 16/2019

This piece is similar to last week’s papers in that if focuses on the challenges posed by algorithmic tacit collusion, but arguably goes further. In previous work, the authors outlined four scenarios where algorithms may be used to facilitate collusion. There is a consensus that their first two scenarios – Messenger, where algorithms help humans collude; and Hub and Spoke, where a common intermediary provides the algorithm and the pricing decision mechanism that could facilitate collusion – pose competition issues that should be addressed under existing rules. Their third and fourth scenarios have proved more controversial. Under the third scenario, called Tacit Collusion on Steroids – The Predictable Agent, companies could unilaterally use algorithms with the intent to facilitate conscious parallelism (also known as tacit collusion). Under the fourth scenario, called Artificial Intelligence, God View, and the Digital Eye, algorithms may arrive at this anticompetitive outcome on their own. Tacit collusion is beyond the reach of the competition laws of…

German Monopolies Commission ‘Algorithms and Collusion’, Chapter I of the XXII. Biennial Report

The Monopolies Commission is a permanent, independent expert committee which advises the German government and legislature as regards competition policy-making, competition law and regulation. The chapter is already one year old, and can be accessed here. In data-intensive sectors such of the digital economy, pricing algorithms can facilitate collusion by automating collusive behaviour. For example, algorithms can stabilise collusion by allowing the collection of information on competitors’ prices and sanctioning deviations from collusive market outcomes more quickly. The use of pricing algorithms can also render explicit anticompetitive agreements or concerted practices dispensable. As a result, difficulties with determining whether a concerted practice is actually taking place will increase with the use of pricing algorithms. The Monopolies Commission considers that the use of pricing algorithms makes it necessary to strengthen market monitoring through sector inquiries. Since consumer associations are most likely to have indications of collusive overpricing, the Monopolies Commission recommends that consumer associations obtain the right to initiate competition sector…

Emilio Calvano, Giacomo Calzolari, Vincenzo Denicol and Sergio Pastorello ‘Artificial Intelligence, Algorithmic Pricing and Collusion’ Centre for Economic Policy Research, London

Algorithmic pricing is not new, but newer software programs are much more “autonomous” than their precursors. Powered by Artificial Intelligence (AI), pricing algorithms can develop their pricing strategies from scratch, engaging in active experimentation and adapting to the evolving environment. In this learning process, they require little or no external guidance. Taken together with the diffusion and evolution of pricing algorithms, these developments raise various issues for competition policy, particularly as regards tacit collusion. While so far no one has brought an antitrust case against autonomously colluding algorithms, antitrust agencies are discussing the problem seriously. In addition to the OECD, competition authorities in the US, Canada and UK have held roundtable or issued papers on the topic. This paper, available here, tries to understand whether tacit collusion arising from AI should be a real concern by looking, for the first time, at the emergence of collusive strategies among autonomous pricing algorithms. It takes an experimental approach, by constructing AI pricing agents and…

Francisco Beneke and Mark-Oliver Mackenrodt ‘Artificial Intelligence and Collusion’ (2019) International Review of Intellectual Property and Competition Law 50 109

Current technological developments in the field of artificial intelligence (AI) have added further complexity to the discussion of whether, in the absence of overt communications, mere tacit coordination between competitors should be outlawed. Whereas some commentators argue that the dangers posed by AI should tip the balance towards making tacit coordination illegal, there are others who are either not entirely persuaded of the plausibility of such dangers or who point out that a competition rule focusing on mere inter-firm interdependence is not administrable. This paper, available here, reviews this debate with a view to establishing whether successful price coordination achieved by self-learning algorithms should be punishable under EU competition law, and whether the current regulatory framework is suitable. Section 2 explains how AI relates to antitrust. AI is expected to arise from certain types of software algorithms. An algorithm is merely a specified sequence of steps for producing a solution to a problem. Software is a composition of individual algorithms…

European Parliament Report on ‘Competition issues in the Area of Financial Technology (FinTech)’

This Report, which can be found here,  provides an interesting overview of potential competition issues in this sphere, while acknowledging ‘the discussion about the competition problems is still hypothetical‘. Even as I am unable to summarise the (136 pages) Report, it is worthwhile emphasising that the authors believe that the application of competition law to potential anticompetitive behaviours in the FinTech sector faces several challenges, the most relevant being the difficulty in applying existing tools and methodologies to new market phenomena such as: (i) many providers operating in multi-sided markets, with concomitant difficulties in terms of market definition and identifying market power; (ii) the possibility of network effects operating as barriers to entry, together with restrictions on interoperability and the adoption of standards; (iii) the role that access to data can have in restricting competition. As far as it goes, these observations are in line with widespread concerns about digital platforms more generally – and with the recent report on the…

Christopher Townley, Eric Morrison and Professor Karen Yeung ‘Big Data and Personalised Price Discrimination in EU Competition Law (2017) Yearbook of European Law 36 683

This paper – which can be found here – seeks to determine whether ‘algorithmic consumer price discrimination’ can amount to an abuse of a dominant position. It is structured as follows: Section 2 explains how ‘big data’ allows for greater personalisation of prices, and how recourse to digital algorithms facilitates personalised pricing. The paper seeks to identify whether ‘algorithmic consumer price discrimination’ enhances or diminishes economic efficiency. To do so, the paper reviews, in detail, the main economic theories on price discrimination, which have already been summarised when describing the paper reviewed in the post below. The authors observe that price discrimination can have rent-transfer effects (from consumers to producers), allocation effects (reflecting consumers’ willingness to acquire the product) and output effects (by pricing some consumers into the markets and/or out of the market). Which of these effects predominates in imperfectly competitive markets is a very hard question. Ultimately, the effects of price discrimination will have to be assessed on a…

Inge Graef  ‘Algorithms and fairness: what role for competition law in targeting price discrimination towards end consumers?’

This paper – which can be found here –  tries to identify when algorithmic price discrimination will be anticompetitive. Price discrimination is not per se unlawful or anticompetitive; on the contrary, price discrimination  may be efficient and lead to increased output. However, personalised pricing is commonly felt to be unfair – and it is undisputed (in Europe, at least) that some forms of price discrimination can be anticompetitive. This paper seeks to distinguish between those situations when algorithmic price discrimination is anticompetitive and those in which it is not. The paper is structured as follows: Section 2 looks at how price discrimination can harm competition. Two types of harm are identified: (i) primary line injury occurs where the supplier’s conduct discriminates against competitors in markets in which the supplier also operates; and (ii) secondary line injury takes place when a supplier discriminates between a number of its customers as against one another. While behaviour giving rise to primary line injury…