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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.
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
Machine learning and data-driven business models
Market structure and competition in web technologies
Market structure and competition in web technologies
Evaluation of regulatory efforts in the data economy
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?
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