Gannon, John Paul Luke ORCID: https://orcid.org/0000-0002-9025-9846 (2022) Concertation and Automation: Article 101(1) of the Treaty on the Functioning of the European Union and Automated Pricing Algorithms. PhD thesis, University of Leeds.
Abstract
The legal questions raised by the interplay between Automated Pricing Algorithms (APAs) and the prohibition on anticompetitive collusion within Article 101(1) of the Treaty on the Functioning of the European Union (TFEU) present an opportunity to assess, critique, and clarify our understanding of EU competition law. The significant changes in business practice heralded by APAs require us to reassess the jurisprudence, to consider why the answers to the questions they raise are so unclear, and to provide some semblance of clarity to the legal lacunas which they throw into sharp relief. The competition law has always had to consist of rules which seek to articulate prohibited business practices in the context of undertakings consisting of human decision-makers. With the advent of increasingly sophisticated automated decision-makers, they must also be reconsidered and reconfigured such that they are capable of providing undertakings with legal standards governing the design, implementation, and monitoring of artificial agents. This thesis contributes to this process by undertaking a comprehensive analysis of the concepts of agreements and concerted practices within Article 101(1), how these concepts apply in the context of APAs and, where they raise unanswered questions, how they should apply in the future.
This dissertation argues that the existing prohibition within Article 101(1) can be interpreted and adapted, with minimal legal engineering, to address many of the competition problems posed by APAs as they are currently understood within the both the legal and experimental literature, capturing related behaviours as forms of horizontal concertation. The major issues are divided into two main parts: questions concerning mediums through which information flows between undertakings, and questions regarding the mental states of undertakings releasing and receiving that information. By examining these two elements, the dissertation argues that greater flesh can be provided to the mooted problems observed in the literature relating to direct and indirect information exchanges and tacit collusion. In particular, the dissertation provides a legal framework from which to consider when information which passes between competitors should, and should not, be considered a feature of normal conditions on the market for the purposes of Article 101(1), and how the mental states of the undertakings involved are established in order to determine when such contacts constitute an agreement or concerted practice. While significant scope for additional research remains, in particular regarding the exact ways in which the technologies at issue may be leveraged, this research addresses several points of inevitable intersection between the existing law and automated decision-makers as they develop and proliferate, laying detailed groundwork for whatever comes next. It thereby strikes a balance between ‘legal sci-fi’ and sticking one’s head in the sand in the face of forthcoming change.
Metadata
Supervisors: | Akman, Pinar and Whelan, Peter |
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Keywords: | competition law; antitrust; Article 101; algorithms; cartels; cartel control; machine learning; AI; artificial intelligence; agreements; concerted practices |
Awarding institution: | University of Leeds |
Academic Units: | The University of Leeds > Faculty of Education, Social Sciences and Law (Leeds) The University of Leeds > Faculty of Education, Social Sciences and Law (Leeds) > School of Law (Leeds) The University of Leeds > Faculty of Education, Social Sciences and Law (Leeds) > School of Law (Leeds) > Centre for Business Law and Practice (Leeds) |
Identification Number/EthosID: | uk.bl.ethos.865268 |
Depositing User: | Dr John Paul Luke Gannon |
Date Deposited: | 15 Nov 2022 09:36 |
Last Modified: | 11 Dec 2022 10:53 |
Open Archives Initiative ID (OAI ID): | oai:etheses.whiterose.ac.uk:31347 |
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