'Abdu'l-Baha says that truth "does not accept division." And yet, in our increasingly interconnected and complex world, truth can often seem very elusive. Appeals to subject-matter expertise alone are insufficient. And calls for increased interdisciplinary decision-making do not address the many barriers to success. In this talk, we will explore system science tools that are being used to help highly interdisciplinary teams make decisions in complex situations. Examples will be drawn from experience in modelling the coronavirus pandemic and in participating in the Institute Process.
I am a researcher, consultant, and educator in the area of complex systems and health. I build systems models, such as agent-based and system dynamics, and use machine learning to study the complex systems of population health, public health, and public policy. I run a consultancy building dynamic models for public health organizations and health companies. I have a PhD in computer science from the University of Saskatchewan, under Dr. Nathaniel Osgood.
Most recently, I have been working with interdisciplinary teams to inform public health policy in response to the pandemic. Tools we use to help the group shape its collective mental model include: causal loop diagrams, agent-based models, system dynamics models, and group model building. I have taught these methods in universities, boot camps, and with government agencies, and implemented them in research and policy situations.
The views expressed in this recording are those of the presenter and do not necessarily represent the views of the Association for Bahá’í Studies, nor the authoritative explications of Bahá’í writings.