Dealing with challenges associated to digital platforms is currently one of the top three priorities according to the European Commission’s Digital Single Market strategy. Platforms can create immense value for the economy, online commerce and drive up productivity. But, at the same time, they have been disruptive and source of regulatory controversy. By design, in the core of platforms’ business models there are algorithms which are based on machine learning principles and use personal data as input to match efficiently and at real time supply with demand. This project analyses the dynamic impact of digital platforms on markets and consumers and addresses challenges that are associated with their disruptive operation, using a novel and multi-level economic approach. At the same time, it also explores the impact of algorithmic design and automated systems in decision making, market competition and society. The research agenda is separated in 3 chapters. The first chapter deals with algorithmic competition and evaluates whether big data raise entry barriers and what the incentives of algorithmic systems to discriminate are. It also assesses policy measures to increase algorithmic transparency and accountability. The second chapter deals with the dynamic nature of digital platforms. A firm with significant market power today might not be in the position to conserve its market power tomorrow because of the entry of, or drastic innovation by competitors. The chapter develops a methodology that defines a robust measure of future potential competition. It also provides insights over the creation and expansion of digital platforms in EU and US and illustrates firms' equilibrium market strategies in fast growing markets. Chapter 3 deals with the impact of automation on employment. By estimating the impact of the introduction of robots in EU industries on labor, it identifies the associated labor displacement and productivity effects and the optimal policy response.
