Project TitleMIning Relationships Among variables in large datasets from CompLEx systems
Project Websitewici.ca/new/research/digging-into-data-did-research/
Start Date1 April 2014
End Date31 March 2016
UK Project ManagerTerence P. Dawson, University of Dundee. t.p.dawson@dundee.ac.uk
Project TeamPI: Prof. Dawn Parker, University of Waterloo, Canada. dcparker@uwaterloo.ca
Co-PI: Prof. C. Michael Barton, Arizona State University, USA. michael.barton@asu.edu
Co-PI: Prof. Tatiana Filatova, University of Twente, Netherlands. t.filatova@utwente.nl
Co-PI: Prof. Terry Dawson, University of Dundee, UK. t.p.dawson@dundee.ac.uk
Dr. Gary Polhill (The James Hutton Institute) gary.polhill@hutton.ac.uk
Lead InstitutionUniversity of Waterloo, Canada
Project PartnersUniversity of Dundee, UK.
Arizona State University, USA.
University of Twente, Netherlands.
Project Planhttp://repository.jisc.ac.uk/6023/
Progress Reporthttp://repository.jisc.ac.uk/6046/


Social scientists have used agent-based models (ABMs) to explore the interaction and feedbacks among social agents and their environments. Agent-based models are dynamic computer simulations of human societies and behaviours in which individuals and their interactions are explicitly represented. This bottom-up structure of ABMs enables simulation and investigation of complex systems and their emergent behaviour with a high level of detail. This detail means that such models have a very large number of variables, creating highly multidimensional “big data” that are difficult to analyse using traditional statistical methods, in part because many of the relationships among the variables are nonlinear.

The project seeks to address this challenge by developing methods and web-based analysis and visualization tools that provide automated means of discovering complex relationships among variables. The tools will enable modellers to easily manage, analyse, visualize, and compare their output data, and will provide stakeholders, policy makers and the general public with intuitive web interfaces to explore, interact with otherwise difficult-to-understand models, and insights into the real-world case studies they represent.

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