Economic data science is about decoding and managing the digital transformation. The interplay between new technologies and human behaviour, both at the individual and the collective level, produces complex feedback loops that offer tremendous socio-economic opportunities but at the same time expose society to unprecedented potential risks.
New kinds of data, behavioural and ecological in the broadest sense, allow us to measure economic activity – outputs, employment, wages, spending, mobility, trade – faster, more precisely and at a more dis-aggregated level than it has previously been possible. This means that individual, business and government decisions can be based on a much more complete and up-to-date picture of what is going on in the economy.
New data also allow us to tackle the full complexity of the socio-technical systems we create and inhabit, and require new methods to support traditional approaches. The interdisciplinary approach in this theme combines machine learning and AI with concepts and tools developed in complex systems science and network science.
- Deploy machine learning and AI techniques to new and existing datasets to decode the digital transformation and support individual, business and government decisions.
- Promote an interdisciplinary approach to the study of the economy, where complex systems science and network science support traditional approaches.
- Address society and the economy as complex adaptive systems – i.e. recognising that feedback loops, 'crises’, and second-order effects are not accidents but rather fundamental properties of the system itself.
- Propose data-informed policies to direct the digital transformation towards a sustainable, open and fair-for-everyone future.