Quantifiable Uncertainty (Risk) vs Unquantifiable Uncertainty
Our blindspot: we ignore unquantifiable uncertainty.
Quantifying some of that quantifiable uncertainty is still useful
Is the world we want to model stationary, at all?
The 3 types of models
Is optimization an illusion in practice?
There are no models without assumptions
How about modelling when an existing relationship is likely break?
This is a a reminder for me that world is not as simple and stationary as us, data-informed people want them to be.
Questions:
- Where is unquantifiable uncertainty greatly ignored?
- Where do we assume stationarity, no change, status quo, where that change can have extreme consequences?
I think it could be important in complex, non-deterministic systems, especially where we don’t have a lot of data, and where we need to measure phenomena over time.
- Economics/finance
- Social sciences
This started out as a collection of notes of Radical Uncertainty, but since then, I’ve added a lot of parallel/contradicting and my own thoughts.