Emilio Calvano, Giacomo Calzolari, Vincenzo Denicolò, Joseph E. Harrington Jr. and Sergio Pastorello ‘Protecting consumers from collusive prices due to AI’ (2020) Science 370 Issue 6520

This paper is available here. The efficacy of a market system is rooted in competition. Nothing more fundamentally undermines this process than collusion, when firms agree not to compete with one another and consumers are harmed by higher prices. The increasing delegation of price setting to algorithms has the potential to open a back door through which firms could collude lawfully. Such algorithmic collusion can occur when artificial intelligence (AI) algorithms learn to adopt collusive pricing rules without human intervention, oversight, or even knowledge. A first section looks at human collusion. Collusion among humans typically involves three stages. First, firm staff with price-setting authority communicate with the intent of agreeing on a collusive rule of conduct. Second, successful communication results in the mutual adoption of a collusive rule of conduct. A crucial component of this rule is retaliatory pricing: each firm raises its price and maintains that higher price under the threat of a “punishment,” such as a temporary price…

Zach Y. Brown and Alexander MacKay ‘Competition in Pricing Algorithms’ Harvard Business School Working Paper, No. 20-067

Increasingly, retailers have access to better pricing technology, especially in online markets. Using hourly data from five major online retailers, the authors show that retailers set prices at regular intervals that differ across firms. Faster firms appear to use automated pricing rules that are functions of rivals’ prices. These observations are inconsistent with the standard assumptions about pricing technology used in the empirical literature. Motivated by this, the present paper – available here – considers a model of competition in which firms can differ in pricing frequency, and can choose pricing algorithms rather than prices. Relative to the standard simultaneous price-setting model, pricing technology with these features can increase prices. A simple counterfactual simulation implies that pricing algorithms can lead to meaningful increases in mark-ups, especially for firms with the fastest pricing technology. Section 2 highlights key features of pricing algorithms used by online retailers. The authors identify three characteristics of the use of high-frequency price data for over-the-counter allergy…

Cento Veljanovski on ‘Pricing Algorithms as Collusive Devices’ (2020)

This paper, available here, undertakes a critical review of the prospect that self-learning pricing algorithms will lead to widespread collusion independently of the intervention and participation of humans. It reviews the arguments and evidence that self-learning pricing algorithms pose a new and significant threat to competition and antitrust enforcement. It argues that there is no concrete evidence, no example yet, and no antitrust case that self-learning pricing algorithms have colluded, let alone increased the prospect of collusion across the economy. Part I explains why algorithmic collusion may be a problem. Academic lawyers, who argued that algorithmic pricing poses a real threat to competition which cannot be dealt with by existing antitrust provisions, initiated a debate over the threat posed by algorithmic pricing. The prospect that pricing algorithms can facilitate collusion by firms is not the principal worry of this academic literature. Rather, the concern is with a class of machine-based algorithms that can collude without human involvement. Through self-learning and…

Stefan Thomas ‘Harmful Signals: Cartel Prohibition and Oligopoly Theory in the Age of Machine Learning’ (2019) Journal of Competition Law & Economics 15 (2-3) 159

Information can be used by competitors to collude or to compete, and the challenge for competition law is to spot the differences. Signalling and any other type of informational exchange outside the scope of cartels are an emanation of tacit collusion. Tacit collusion, however, is generally considered unobjectionable, because firms are deemed to have the right to adapt intelligently to their rivals’ conduct. The law puts different labels on what is ultimately the same economic phenomenon, that is, conduct that leads to supra-competitive outcomes. The traditional legal approach for distinguishing between illicit collusion and legitimate oligopoly conduct is to rely on criteria that relate to the means and form of how rivals interact, such as elements of “practical cooperation” or findings of anticompetitive intent. This article, available here, contends that, outside the scope of classic cartel agreements, it is not possible to properly distinguish between illicit collusion and legitimate independent conduct by relying on proxies such as elements of practical…

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…