A general framework for analyzing the effects of algorithms on optimal competition laws

Michal S. Gal; Jorge Padilla

doi:10.1515/til-2025-0008

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A general framework for analyzing the effects of algorithms on optimal competition laws

Michal S. Gal and Jorge Padilla

Published/Copyright: July 23, 2025

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From the journal Theoretical Inquiries in Law Volume 26 Issue 1

Abstract

Competition laws are influenced by economic presumptions regarding how markets operate. Such presumptions generally relate to how humans interact, such as how human decision-makers—whether acting as individuals or as a firm’s agents—gather information, send signals, and deal with complex, uncertain, or fast-changing market environments. The exponential growth in the use of algorithms by market participants to perform a myriad of tasks is challenging such presumptions. The lowering of access barriers to real-time data on market conditions, coupled with semi-automated decision-making by sophisticated and autonomous robo-economicus, requires us to rethink the economic presumptions embedded in our laws. Indeed, as we show, in many cases, the application of existing legal presumptions to markets in which decisions are made by sophisticated algorithms operating on big data increases the instances and harms of false negatives and, albeit less frequently, false positives.

While research thus far has focused on the effects of algorithms on specific types of competition rules, this article suggests a general framework for identifying such effects. We employ decision theory to help determine how competition laws should be optimally framed in the age of algorithmic decision-making. As we show, once the use of sophisticated AI-empowered algorithms is assumed, legal presumptions with regard to some types of conduct must be changed. We suggest a typology of six different effects, ranging from no effect at all to a need for new prohibitions. Our theoretical analysis is aided by real-world examples, including cases where the introduction of sophisticated algorithms affects the choice between rules versus standards, the content of the prohibition, or procedural rules. We hope our meta-analysis brings more clarity to a much-needed reboot of our regulatory framework in the age of algorithms.

^* Michal S. Gal (LL.B., LL.M., S.J.D) is Professor of Law, University of Haifa Faculty of Law, Honorary Doctor, University of Zurich and past president of the International Association of Competition Law Scholars (ASCOLA) (2016-2023). Jorge Padilla (B.Sc, Ph.D) is Senior Managing Director, Head of Compass Lexecon EMEA and Member of its Global Executive Committee. He teaches competition economics at the Toulouse School of Economics (TSE). Many thanks to Josef Harrington, Giorgio Monti, Steven Salop, Haggai Porat, participants in the TIL conference in Milan, and to the TIL editorial board for superb discussions and comments, and to Omer Tshuva for excellent research assistance. This work was supported by ISF grant no. 2737/20. Any mistakes remain solely the authors’.

Published Online: 2025-07-23

Published in Print: 2025-06-26

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https://doi.org/10.1515/til-2025-0008