Generating Synthetic Population Using Transformer Based Networks

Microsimulation models are widely used in various fields. However, accessing individual-level data can be challenging due to confidentiality concerns and high costs. To address this, researchers often generate synthetic populations based on available data of the target population. In this paper, we present a novel Transformer-based approach for generating both numerical and categorical demographic attributes in synthetic populations. Our model achieves good fit with minor errors on the test set and preserves population heterogeneity, as indicated by the number of unique individuals in the real and synthetic populations (< 0.23% difference). Compared to the actual data, the synthetic population is moderately diverse, with an average distance of 0.00636 and standard deviation of 0.13836 between synthetic agents and real data. Our approach has potential applications in various domains, and we believe it can contribute to the development of more accurate and representative microsimulation models.