A Computational Framework for Interoperating Uncertainty Quantified Social System Models

Accurate uncertainty modelling of social activities is impossible using available geographic information data and typical geographic information system algorithms due to the Uncertain Geographic Context Problem (UGCoP). UGCoP worsens when data as vague or obsolete, competing social models are available, and parameter values are not fully known. This research reduces UGCoP by explicitly representing uncertainty in input data, algorithms, and visualization tools using Monte Carlo methods. Rich contextual social information is retained by storing dozens of demographic attributes from surveys and censuses of all people in the study area. To include this detail requires large-scale modeling involving demographic and land use forecasting models, agent-based models, transportation dynamic models, and other computationally complex operations necessitating parallel algorithms and distributed computing systems. Named the Framework for Incorporating Complex Uncertainty Systems, it includes multiple free and open-source software tools, especially the Object Modeling System, to allow easy inclusion of additional models written in multiple third and fourth generation programming languages. This research presents the space-time uncertainty quantified modeling environment, multiple model components, and web browser visualization tools necessary to inspect all data and results of this extendable social and infrastructure system of systems analysis approach. This research will be demonstrated with a case study in the Philippines supporting risk analysis.