Algorithmic Antitrust
Springer International Publishing (Verlag)
978-3-030-85861-2 (ISBN)
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.
lt;p> 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.
Prologue: Algorithmic Antitrust - A Primer.- Algorithmic Antitrust - A Critical Overview.- Algorithmic Antitrust and Consumer Choice.- Algorithms, Big Data, and Merger Control.- Reasons to be Cheerful - The Benevolent Market Power of Decentralised Blockchains.- Blockchain Antitrust.- The Technology Innovation Time Gap in European Competition Law: Assessing the European Commission's Approach.- Epilogue: Algorithmic Antitrust - A Questioning.
Erscheinungsdatum | 25.01.2023 |
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Reihe/Serie | Economic Analysis of Law in European Legal Scholarship |
Zusatzinfo | V, 176 p. 2 illus. in color. |
Verlagsort | Cham |
Sprache | englisch |
Maße | 155 x 235 mm |
Gewicht | 290 g |
Themenwelt | Recht / Steuern ► EU / Internationales Recht |
Recht / Steuern ► Öffentliches Recht | |
Recht / Steuern ► Privatrecht / Bürgerliches Recht ► Medienrecht | |
Schlagworte | algorithm • Antitrust • Artificial Intelligence • collusion • Competition • Deep learning • digital • Innovation • machine learning • Technology |
ISBN-10 | 3-030-85861-8 / 3030858618 |
ISBN-13 | 978-3-030-85861-2 / 9783030858612 |
Zustand | Neuware |
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