This post is based on things I learned from, or began researching due to, a talk by Troy Magennis at LKNA ’14.
I had never thought deeply enough about risk to really differentiate between types. Since LKNA ’14, I have learned about aleatory and epistemic risk.
- Epistemic: uncertainty due to gaps in knowledge
- Aleatory: uncertainty due to variability or randomness [like throwing dice or flipping a coin]
Differentiating between the type of risk is important because they are mitigated in completely different ways. Epistemic risk is the easier type to deal with because it is something that can be overcome. We can endeavor to fill our gap in knowledge that is causing the uncertainty which creates the risk. However, we can’t remove randomness from a process like flipping a coin. We can, however, learn to handle aleatory risk better using past historical data with probabilistic models. The more we understand the probabilities, the better we can assess the risk we are facing. This is where Troy’s teachings on subjects such as Monte Carlo simulations are very helpful.
This is a topic I want to dive into at a much deeper level. A couple of pages I have found helpful:
- Epistemic Uncertainty (netjeff.com)
- Herding Cats: Both Aleatory and Epistemic Uncertainty Create Risk
What have you read that has taught you well in this area of uncertainty, risk and probabilistic forecasting?