Septiembre 21, 2026
Read Ted Tatos and Hal Singer’s Article for the Competition Policy International Antitrust Chronicle
In the article, they write: “Though now a part of the modern vernacular, machine learning mechanisms that motivate algorithmic pricing, such as neural networks, random forests, and boosting, remain “black box” terms to many antitrust practitioners and legal scholars alike. Such informational barriers impede proper evaluation of these pricing methodologies, potentially either failing to detect anticompetitive conduct or inadvertently condemning innocuous or even procompetitive practices. Determining the relative likelihood of either outcome requires, inter alia, a working understanding of the analytical methodologies, many of which remain unnecessarily shrouded in a level of complexity akin to mysticism.”
In their new essay for the Competition Policy International (CPI) Antitrust Chronicle, Econ One experts Ted Tatos and Hal Singer aim to dispel some of this confusion and assist regulators, practitioners, and scholars in their interactions with the machine learning methods that motivate artificial intelligence.