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Risk vs. Uncertainty

“Correlations, sigma along with a host of other risk metrics all assume historical data is predictive. If that were true the French would still be sitting safely behind the Maginot Line.”  - Richard Weissman
Keynes and Knight on uncertainty – ontology vs. epistemology

 A response to the above comment and link

The implied assumption in the risk metrics Mr. Weissman refers to is that the present probability distributions constructed from the use of past data will remain constant across time. Even the "Black Swan" concept (I think) implies that non-routine/unexpected change is something that exists in the tails of a static probability distribution where the observer lacks perfect information when deriving their probability distribution.  Instead reality might consist of fully dynamic probability distributions across time which results in the non-routine change. 

Therefore, even if one had access to all the information that exists in the present, the future outcomes can still not be determined beforehand because of the changing relationships in the variables that make up reality.

1. The situation is well defined, the probability distribution is fixed and known, but the outcome is unknown. 

2. The situation is not well defined; the probability distribution is fixed, but unknown. The outcome is unknown (Black Swan).

3. The situation is not well defined; the probability distribution is constantly changing and unknown at any given time. The outcome is unknowable. 

#1 can be called risk. 
#2 can be called epistemological uncertainty.
#3 can be called ontological uncertainty. 

Really, you can call these conditions whatever you would like but they are all different. #2 is closer to how Frank Knight defined uncertainty where as #3 is closer to how Keynes defined uncertainty.

A situation is "well defined" when the variables that affect the probability distribution are known.

In an uncertain situation, the variables are not known as humans possess imperfect knowledge. The difference between #2 and #3 goes one step further. Not only are the variables unknown, but even if they were known, the relationships the variables have with one another is subject to change. 

#2 suggests that if humans possessed perfect knowledge or had a machine that allowed this (advanced computing), all variables in the present could be known and their relationships with one another could be revealed. If this was possible, uncertainty involving the future could be quantified. With perfect knowledge uncertainty can be reduced to risk. Risk can be managed. 

#3 suggests that even if one possesses perfect knowledge of all variables and their relationships with one another, uncertainty could still never be quantified. This is because the nature of the variables is subject to change and their relationships with each other are not fixed. Furthermore, new variables can come into existence. Therefore, even with perfect knowledge, uncertainty is not reducible.

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