A General Framework for Analyzing the Effects of Algorithms on Optimal Competition Laws

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 agents of a firm – 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, increase both the frequency and the harms of false negatives and, although less frequently, false positives. Research thus far has largely focused on how algorithms affect specific types of competition rules. This article goes further, to suggest 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-level analysis brings more clarity to a much-needed reboot of our regulatory framework in the age of algorithms.