Digital twins: challenges, pitfalls, and opportunities

Li and O’Donoghue (2013) emphasized microsimulations to cover two areas, the microsimulations per se in terms of what-if-questions as well as synthetic data generation as an important base for performing microsimulations. More and more methods such as data fusion of different surveys, prediction methods, as well as modern ML approaches are applied. However, modelling strategies need to be adjusted accordingly, in particular depending on cross-sectional or longitudinal applications. Further, the increasing attention is laid on the granularity of the modelling. All in all, little attention is laid on the accuracy of the generated data as well as on assumptions and implicit decisions of developers of microsimulation models. The presentation focuses on different aspects of synthetic data generation and so-called digital twins. Special attention will be laid on timely and regional granularity as well as of unobserved heterogeneities of the simulations including uncertainties of the entire modelling process. Additionally, specific data situations and disclosure limitations will be addressed.