Perhaps the most important input for AI and related technologies is the data needed to train and test models. However, unequal distribution of access to data among firms raises questions about the viability of merits-based competition when a small number of gatekeeper platforms have a significant data advantage. One contributing factor to this concentration of data has been a wave of mergers in which firms were acquired for the apparent purpose of access to their data – data-driven mergers.
The current approach to merger control puts significant weight on concerns mediated through the elimination of competition and an associated increase in prices, leaving the door open for a more permissive stance on mergers resulting in a significant concentration of access to data. One problem faced by the authorities seeking to govern these mergers is the lack of a coherent and general framework for thinking about how data affects the impact of a merger on markets. This project seeks to provide such a framework.
A challenge for those wishing to model the competitive effects of data is that data is used in many different ways and under a variety of business models. A key objective of this project will be to develop a model that is sufficiently general to encompass a wide variety of different market situations in which data plays a role. It will then use this model to study how market outcomes are affected by a data-driven merger.
The questions to be addressed include:
This programme supports research on AI, Government and Policy.
