C.: AIMSS: An architecture for data driven simulations in the social sciences

This paper presents a prototype implementation of an intelligent as-sistance architecture for data-driven simulation specialising in qualitative data in the social sciences. The assistant architecture semi-automates an iterative se-quence in which an initial simulation is interpreted and compared with real-world observations. The simulation is then adapted so that it more closely fits the ob-servations, while at the same time the data collection may be adjusted to reduce uncertainty. For our prototype, we have developed a simplified agent-based sim-ulation as part of a social science case study involving decisions about housing. Real-world data on the behaviour of actual households is also available. The au-tomation of the data-driven modelling process requires content interpretation of both the simulation and the corresponding real-world data. The paper discusses the use of Association Rule Mining to produce general logical statements about the simulation and data content and the applicability of logical consistency check-ing to detect observations that refute the simulation predictions.