InDesign document
©2019 Charlene Brown
I won`t attempt to summarize everything I learned in a ‘Forecasting the Future’ class at UVic that I Zoom-attended earlier this month. Actually I did try to summarize everything, but ended up with way more information than I’d ever try to squeeze onto this blog. So I’ll just write about the three topics that relate to Predictive Visualization – Complex Systems, Confounding Factors and the Role of Artificial Intelligence.
Complex Systems:
A system is complex if it has diversity, connection, and interdependence.
· Non-adaptive complex systems follow rules of behavior and equations of physics, and can be modeled and predicted. Climate projections are non-adaptive complex systems that should be relatively easy to model, except for some randomness and chaos.
In climate modeling, parameterization – a succinct mathematical description of a complex process – can replace factors like randomness and chaos that are too small-scale or complex to be represented physically. Millennials would describe parameterization as a hack, by which they would mean a clever, subtle, even mystical computer program – a digital poem, rarely appreciated by non-hackers.
· Adaptive systems adopt new rules when circumstances change and are harder to model and predict.
Chaos theory deals with adaptive complex systems whose behavior is highly sensitive to slight changes in conditions, so that small alterations can result in unintended consequences. Techniques are emerging to make predictions using chaos theory
