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MIning Relationships Among variables in large datasets from CompLEx systems (MIRACLE)

Computer display screens

The bottom-up structure of ABMs enables simulation and investigation of complex systems and their emergent behavior with a high level of detail; however, such models create large non-linear multidimensional “big data,” which are difficult to analyze using traditional statistical methods. This international collaborative project addresses these challenges by developing algorithms and web-based analysis and visualization tools that provide automated means of discovering complex relationships among variables.

Social scientists have used agent-based models (ABMs) to explore the interaction and feedbacks among social agents and their environments. The bottom-up structure of ABMs enables simulation and investigation of complex systems and their emergent behavior with a high level of detail; however, the stochastic nature and potential combinations of parameters of such models create large non-linear multidimensional “big data,” which are difficult to analyze using traditional statistical methods.

This international collaborative project is addressing these challenges by developing algorithms and web-based analysis and visualization tools that provide automated means of discovering complex relationships among variables. The tools will enable modelers to easily manage, analyze, visualize and compare their output data, and will provide stakeholders, policy makers and the general public with intuitive web interfaces to explore, interact with and provide feedback on otherwise difficult-to-understand models.

Team Members:

C. Michael Barton, Arizona State University School of Human Evolution and Social Change
Tatiana Filatova, University of Twente, the Netherlands
Terence P. Dawson, University of Dundee Centre for Environmental Change and Human Resilience, U.K.
Dawn Cassandra Parker, University of Waterloo School of Planning, Canada