Simulation Studies of Social Systems – Telling the Story Based on Provenance Patterns

Social simulation studies are complex. They typically combine various sources of data and hypotheses, that are integrated by intertwined processes of model building, simulation experiment execution, and analysis. Various documentation approaches exist to support the transparency and traceability of complex social simulation studies. In particular, provenance patterns can be used to capture central activities and entities of a simulation study. Entities can include, simulation models, experiments, or research questions, and activities – model building, calibration, validation, and analysis. The exploitation of provenance standards enables information on sources and activities, which contribute to the generation of an entity, to be queryable and computationally accessible. In this study, we refine the provenance pattern-based approach to address specific challenges of social agent-based simulation studies. Specifically, we focus on the activities and entities involved in collecting and analyzing primary data about human decisions, and the collection and quality assessment of secondary data. We illustrate the potential of this approach by applying it to central activities and results of the Bayesian Agent-Based Population Studies project and by presenting its implementation in a web-based tool.