Algorithmic Antitrust

1. Front Matter

   Pages i-v

   PDF

2. Prologue: Algorithmic Antitrust—A Primer

   • Aurelien Portuese

   Pages 1-38

3. Algorithmic Antitrust: A Critical Overview

   • Cento Veljanovski

   Pages 39-64

4. Algorithmic Antitrust and Consumer Choice

   • Liza Lovdahl Gormsen

   Pages 65-86

5. Algorithms, Big Data, and Merger Control

   • Verity Egerton-Doyle, Jonathan Ford

   Pages 87-106

6. Reasons to be Cheerful: The Benevolent Market Power of Decentralised Blockchains

   • Chris Pike, Gabriele Carovano

   Pages 107-120

7. Can Antitrust Trust Blockchain?

   • Giovanna Massarotto

   Pages 121-154

8. The Technology Innovation Time Gap in Competition Law Enforcement: Assessing the European Commission’s Approach

   • Pierre Kirch

   Pages 155-174

9. Epilogue: Algorithmic Antitrust—A Questioning

   • Aurelien Portuese

   Pages 175-176

Algorithms are ubiquitous in our daily lives. They affect the way we shop, interact, and make exchanges on the marketplace. In this regard, algorithms can also shape competition on the marketplace. Companies employ algorithms as technologically innovative tools in an effort to edge out competitors. Antitrust agencies have increasingly recognized the competitive benefits, but also competitive risks that algorithms entail. Over the last few years, many algorithm-driven companies in the digital economy have been investigated, prosecuted and fined, mostly for allegedly unfair algorithm design. Legislative proposals aim at regulating the way algorithms shape competition. Consequently, a so-called “algorithmic antitrust” theory and practice have also emerged. This book provides a more innovation-driven perspective on the way antitrust agencies should approach algorithmic antitrust. 

To date, the analysis of algorithmic antitrust has predominantly been shaped by pessimistic approaches to the risks of algorithms on the competitive environment. With the benefit of the lessons learned over the last few years, this book assesses whether these risks have actually materialized and whether antitrust laws need to be adapted accordingly. Effective algorithmic antitrust requires to adequately assess the pro- and anti-competitive effects of algorithms on the basis of concrete evidence and innovation-related concerns. With a particular emphasis on the European perspective, this book brings together experts and scrutinizes on the implications of algorithmic antitrust for regulation and innovation. 

• Antitrust
• Competition
• Digital
• Algorithm
• Technology
• Innovation
• Machine learning
• Deep learning
• Artificial intelligence
• Collusion

• The Schumpeter Project on Competition Policy, Information Technology and Innovation Foundation, Washington, DC, USA

  Aurelien Portuese

Dr. Aurelien Portuese is Director of The Schumpeter Project at the Information Technology and Innovation Foundation, the world’s leading think tank on science and technology. He is also an Adjunct Professor at the Global Antitrust Institute of George Mason University, and at the Catholic University of Paris.

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