On March 4, 2024, the European Commission fined Apple €1.84 billion “over abusive App store rules for music streaming providers”.¹ In particular, the Commission was concerned about the anti-steering provisions that Apple imposed on these providers. Although the full decision has not yet been published (I am told it could be a matter of months), the public information underpinning this decision is already interesting on several levels. In the following, I explore the good (1.), the bad (2.), and the ugly (3.) of the “App Store Practices (music streaming)” decision based on the information available as of this writing.

Read the full piece here.

Antitrust agencies are getting increasingly interested in understanding digital ecosystems.¹ As someone who has long advocated for examining ecosystems in antitrust (ah, Ph.D. days…),² I can only welcome this development. However, my working hypothesis is that the concept of ecosystems can only be understood through complexity science. And thus far, there appears to be limited interest in complexity among antitrust policymakers and enforcers (although this may be changing with the recent advent of the Dynamic Competition Initiative).³ Against this background, this contribution aims to introduce the critical role of complexity science in developing a functional theory of ecosystems in antitrust law (1.) and expose the perils of ignoring it (2.).

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Abstract

Antitrust law and policy rely on a hypothetical average consumer. But no one is average. With this basic observation in mind, we show how agent-based modeling (“ABM”) allows enforcers and policymakers to bypass imaginary averages by observing interactions between unique agents. We argue that agent-based regulatory and enforcement policies have a greater potential than average-based public policies because they are more realistic. As we show, the realism brought by ABM enables antitrust agencies and policymakers to better anticipate the effects of their actions and, perhaps more importantly, to time their interventions better.

Read at SSRN.

Abstract

Dynamic competition defines an improvement path for antitrust law. Interested in competitive realities more than political activities, the growing body of scholarship studying dynamic competition wants to make antitrust diagnosis and analysis more accurate without sacrificing administrability. At a high level, the dynamic competition approach appears to some as a twenty-first-century equivalent of the Chicago School of antitrust. This article shows that the analogy is only partially correct. Unlike the Chicago School of antitrust law, the dynamic competition scholarship is innovation oriented, empirical, enforcement friendly, and interdisciplinary. More generally, dynamic competition is the natural evolution for all systems of antitrust law that reassess doctrine in light of the progression of economic and technical understanding of competition.

Read at SSRN.

Abstract

The AI Act is poised to become a pillar of modern competition law. The present article seeks to provide competition practitioners with a practical yet critical guide to its key provisions. It concludes with suggestions for making the AI Act more competition friendly.

Read at SSRN.

Abstract

The Federal Trade Commission and the U.S. Department of Justice published a draft update of their merger guidelines in July 2023. This paper reviews the Draft Merger Guidelines from a dynamic competition perspective. We base our findings and recommendations on recent economic literature dealing with innovation.

Abstract

W. Brian Arthur is the father of complexity economics. He is also known for his work on the nature of technology, his experiments with agent-based modeling, and his entrepreneurial approach to science. This article seeks to explore the reasons why a scholar might identify as an “Arthurian,” with the aspiration of encouraging others to embrace Arthur’s research interests and emulate his approach to science.

Abstract

Generative AI is set to become a critical technology for our modern economies. If we are currently experiencing a strong, dynamic competition between the underlying foundation models, legal institutions have an important role to play in ensuring that the spring of foundation models does not turn into a winter with an ecosystem frozen by a handful of players.

Abstract

In the first quarter of 2023, the Stanford Computational Antitrust project team invited the partnering antitrust agencies to share their advances in implementing computational tools. Here are the 26 contributions we received.