Advances in the integration of smart technology, computational modelling and statistical platforms has created a new methods environment, approachable modelling and smart methods – AM- Smart for short. The AM-Smart environment is comprised of bespoke tools that facilitate user-driven learning of a topic, creating an intuitive, supported but open-ended environments designed to solve specific tasks. Unlike most statistical platforms, AM-Smart methods focus on a single technique or small network of closely interrelated methods (mostly computational in focus), which help users to simultaneously use and learn new methods. They do so by providing, scaffolding while allowing for user- exploration, rapid and formative feedback and by requiring modest technical skill, while still being rigorous, authentic, and reliable. A major focus of AM-Smart methods is policy evaluation, as demonstrated in participatory systems mapping tools, complex evaluation toolkits, and R shiny programmes and fast ABM modelling. For this talk, I will introduce the world of AM-Smart methods and their value for policy evaluation. I will specifically introduce a package we have developed called COMPLEX-IT.
I am the Director of the Research Methods Centre and Co-Director of the Wolfson Research Institute for Health and Wellbeing at Durham University, UK. I am also Adjunct Professor of Psychiatry (Northeastern Ohio Medical University, USA) Editor of the Routledge Complexity in Social Science series, CO-I for the Centre for the Evaluation of Complexity Across the Nexus, and a Fellow of the UK National Academy of Social Sciences. I am trained as a public health sociologist, clinical psychologist, and methodologist and take a transdisciplinary approach to my work. My methodological focus is primarily on computational modelling and mixed-methods. My colleagues and I have spent the past ten years developing a new case-based, data mining approach to modelling complex social systems and social complexity – case-based computational modelling – which we have used to help researchers, policy evaluators, and public sector organisations address a variety of complex public health issues, from depression and allostatic load to air pollution and brain health to the social determinants of health inequalities. We also developed COMPLEX-IT, designed to increase non-expert access to the tools of computational social science (i.e., cluster analysis, artificial intelligence, data visualization, data forecasting, and scenario simulation) to make better sense of the complex world(s) in which they live and work. As Director of the DRMC, my goal is to facilitate across the university a transdisciplinary and mixed-methods approach to social and health science, grounded in a complex systems perspective.
Sociology -- Research -- Methodology, Human-computer interaction, Assistive computer technology, Ambient intelligence – Design, Artificial intelligence and machine learning, Artificial intelligence -- Government policy, Decision making -- Computer simulation, Machine learning, Geographic information systems, Emerging technologies, Computer software – Development, System analysis
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Castellani, Brian, "Smart Methods for Complex Policy Evaluation" (2022). Systems Science Friday Noon Seminar Series. 114.