This most recent publication is especially important to me. I’ve followed the Competition Policy International for years and based my PhD research proposal on texts from many of its editions. This influential journal mainly publishes thought pieces from recognised scholars. This month, I joined the leading experts to write for a special Computational Antitrust issue.
Abstract: Computational antitrust is expanding competition authorities’ ability to collect, organize, and analyze information about market behavior. Yet the practical value of these methods depends not only on the volume of data processed or the sophistication of the tools used but also on a theory of economic activity that identifies which market processes matter, what information they generate, and how to interpret it. This essay argues that complexity economics provides such a framework by treating markets as complex adaptive systems shaped by decentralized interaction, emergent properties, feedback loops, heterogeneous agents, and evolutionary adaptation. These characteristics shift attention from market conditions at a given moment to the processes through which competitive constraints form and change. Computational methods can make those processes more observable and testable by reconstructing adjustment sequences, relationships among participants, behavioral differences, and possible market trajectories. Examples from pricing analysis, network research, procurement screening, and agent-based modeling illustrate how this approach can connect dispersed observations with legally relevant explanations. However, the same features that make dynamic markets informative also limit what computation can establish. Emergence creates ambiguity about causal mechanisms; adaptation destabilizes historical relationships; and feedback makes future trajectories sensitive to initial conditions and assumptions. Computational outputs therefore require interpretation, comparison with alternative explanations, and assessment under the legal standards. Rather than replacing conventional economic analysis or automating enforcement decisions, a complexity-informed approach helps authorities determine which information to preserve, which analytical methods to use, and how far the resulting inferences can legitimately go.
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