This short piece serves as an introduction to Thibault Schrepel & Teodora Groza’s working paper
entitled “Computing Innovation Competition” available here.
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For many years, scholars in the field of competition law have been discussing the role of innovation. A quick search on Google Scholar for the terms “antitrust” and “innovation” brings up over 290,000 results, with more than 15,000 added since 2023 alone. Most of the research in this area focuses on how competition encourages innovation. It’s no surprise that the more specialized concept of “innovation competition”—which can be described as competition driven by the creation and implementation of new technologies, products, services, or business models—is also gaining traction.
Figure 1: Google Ngram (July 23, 2024)
Unlike traditional price competition, “innovation competition” recognizes that innovation plays a crucial role in shaping competitive dynamics. Some scholars even argue that “innovation has become the leading form of competition.” This perspective introduces the idea of “dynamic competition,” where companies compete not just on their current offerings, but on the potential of future innovations.
The expansion of digital platforms and ecosystems is pushing research forward in this area. While competition regulators are increasingly eager to understand how innovation influences market competition, the lack of a comprehensive innovation theory makes it difficult for them to establish a framework focused on innovation. Instead, they often rely on a case-by-case evaluation without standardized economic measures. The issue is that this individual approach is becoming overwhelmed by the growing complexity of cases, data, and variables they must manage.
In response, competition authorities are turning to computational tools to streamline data analysis, improve the quality of their assessments, and foster healthier market competition. This raises an important question: can antitrust authorities effectively consider innovation competition when using computational tools? These tools typically excel at processing quantifiable metrics like prices and outputs, but the qualitative nature of innovation makes it harder to adapt such computational approaches. This study investigates the growing tension between the need for computational antitrust approaches and the prima facie difficulties in incorporating innovation into these models. Upon a closer look, it finds deep synergies between computational antitrust and innovation competition.
Thibault Schrepel & Teodora Groza
