Project structure and key questions

The project's overarching goal is to contribute to our understanding of how data and machine learning can be valuable economic inputs.
We study questions related to organization, innovation, and regulation. The project is funded for 48 months.

The machine learning value chain

To think about how firms organize the creation of value from data, we use the framework of the Machine Learning Value Chain. Models and algorithms transform data inputs, with the help of computational infrastructure, to arrive at valuable insights. We distinguish between upstream and downstream firms along the value chain, and consider regulatory intervention.

Machine learning and data-driven business models

Market structure and competition in web technologies

Evaluation of regulatory efforts in the data economy

  • What is the structure of the upstream machine learning industry?

  • How can downstream firms innovate with data and machine learning?

  • What is the industrial organization of the web tech industry, with respect to business models, geography, market power?

  • Dynamics: How does upstream competition affect the downstream market?

  • How does privacy regulation affect data collection and the value created by data?

  • How does internet access regulation affect welfare of internet service providers, content providers and consumers?