Multipurpose synthetic population for policy applications

While privacy preservation is a major topic today, until recently, striking the balance between usefulness and detail in data was achieved by aggregation on linear scale. New methods for handling analytics however allow to close this gap and to preserve both privacy and knowledge. Compared to other privacy-preservation techniques, synthetic data can have the best value/effort performance. Synthetic population models facilitate application of novel methods for data-driven policy formulation and evaluation, representing a unique opportunity. This report showcases several applications of structured population such as population activity-based modelling, knock-on effects of selective lock-downs during the COVID-19 pandemic, investigative analysis of existing policy instrument design in the energy transition domain, and applications for synthetic cancer patient records. The text carefully weighs pros and cons of synthetic data in these policy applications to provide actionable insights for decision makers on opportunities and reliability of advice based on synthetic data. Such data can become unifying bridge between policy support computational models, provide data hidden in silos, and become the key enabler of artificial intelligence in business and policy applications in Europe. Synthetic data have potential help controlling unevenness and bias in algorithmic governance and enable better targeted policies with small regulatory footprint.