FOSSR Deliverable 5.8 - Complex modelling and artificial populations for agent-based simulations

The FOSSR open-cloud will provide plenty of data to support social research and policymaking. However, details and abundance of data might not be enough to understand the emergence of the dynamics of collective phenomena such as, for instance, segregation or polarization. Agent-based modeling is a computational method that serves this scope, enabling the construction of artificial societies comprising virtual agents that simulate human cognitions and behaviors to interact with their context. By studying the interaction between agents as a drive to collective behavior, the method enables experimentation on the actual emergence of collective phenomena. Synthetic populations are a set of algorithms to assure that artificial populations are representative of their target population in terms of attributes of agents and their distribution, which includes also the reconstruction of data from independent sources of information. The goal of WP5.5 is to enable algorithms for synthetic populations and synthetic reconstruction in the FOSSR open cloud, providing a service for researchers who will use data from the FOSSR infrastructures to build reliable agent-based models. In this deliverable, we report our activities to the date. We provide an overview of complexity science, agent-based modeling, and their contribution to the FOSSR infrastructure. We show our formalization of the Iterative Proportional Fitting algorithm for synthetic reconstruction and its validation, and steps towards the integration in the workflow of the future FOSSR infrastructure in collaboration with other WPs.