AIMSS: An Architecture for Data Driven Simulations in the Social Sciences

Abstract

Abstract. This paper presents a prototype implementation of an intelligent assistance architecture for data-driven simulation specialising in qualitative data in the social sciences. The assistant architecture semi-automates an iterative sequence 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 observations, while at the same time the data collection may be adjusted to reduce uncertainty. For our prototype, we have developed a simplified agent-based simulation 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 automation 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 checking to detect observations that refute the simulation predictions.

Symbiotic Simulation Control in Semiconductor Manufacturing

“…Several applications which use symbiotic simulation have been developed since 2002. These include the application of symbiotic simulation for UAV path planning [7], social sciences [8], [9], and business process re-engineering [10]. In the context of semiconductor manufacturing, symbiotic simulation has already been used in [11] to optimise backend operations.…”

Trust Dynamics: A Data-Driven Simulation Approach

“…DDDAS has been applied in the simulation of physical, artificial or social entities [12,15]. The application of DDDAS for trust management provides dynamism in the detection of misbehaving members and prediction of future ratings.…”

Re-thinking simulation: a methodological approach for the application of data mining in agent-based modelling

“…In Kennedy et al (2007) it is proposed the use of a DM technique, association rules, to validate the simulation output and analyse the real-world data. These rules would discover unexpected relationships among the categorical variables both in the simulation and empirical data.…”

“…This would be useful to define a model that follows a similar evolution and to check whether the simulation evolved like the social process. -The DM technique association rules, following Kennedy et al (2007), may also be applied to discover hidden relationships in categorical data. These relationships would be rather valuable to model the agent behaviour.…”