Archives For Ontological Risk

Donald Trump

Image source: AP/LM Otero

A Trump presidency in the wings who’d have thought! And what a total shock it was to all those pollsters, commentators and apparatchiks who are now trying to explain why they got it so wrong. All of which is a textbook example of what students of risk theory call a Black Swan event. Continue Reading…

Crowely (Image source: Warner Bro's TV)

The psychological basis of uncertainty

There’s a famous psychological experiment conducted by Ellsberg, called eponymously the Ellsberg paradox, in which he showed that people overwhelmingly prefer a betting scenario in which the probabilities are known, rather than one in which the odds are actually ambiguous, even if the potential for winning might be greater.  Continue Reading…

4blackswans

One of the problems that we face in estimating risk driven is that as our uncertainty increases our ability to express it in a precise fashion (e.g. numerically) weakens to the point where for deep uncertainty (1) we definitionally cannot make a direct estimate of risk in the classical sense. Continue Reading…

Inspecting Tacoma Narrows (Image source: Public domain)

We don’t know what we don’t know

The Tacoma Narrows bridge stands, or rather falls, as a classic example of what happens when we run up against the limits of our knowledge. The failure of the bridge due to an as then unknown torsional aeroelastic flutter mode, which the bridge with it’s high span to width ratio was particularly vulnerable to, is almost a textbook example of ontological risk. Continue Reading…

I’ve rewritten my post on epistemic, aleatory and ontological risk pretty much completely, enjoy.

4blackswans

Or how do we measure the unknown?

The problem is that as our understanding and control of known risks increases, the remaining risk in any system become increasingly dominated by  the ‘unknown‘. The higher the integrity of our systems the more uncertainty we have over the unknown and unknowable residual risk. What we need is a way to measure, express and reason about such deep uncertainty, and I don’t mean tools like Pascalian calculus or Bayesian prior belief structures, but a way to measure and judge ontological uncertainty.

Even if we can’t measure ontological uncertainty directly perhaps there are indirect measures? Perhaps there’s a way to infer something from the platonic shadow that such uncertainty casts on the wall, so to speak. Nassim Taleb would say no, the unknowability of such events is the central thesis of his Ludic Fallacy after all. But I still think it’s worthwhile exploring, because while he might be right, he may also be wrong.

*With apologies to Nassim Taleb.

Interesting article on old school rail safety and lessons for the modern nuclear industry. As a somewhat ironic addendum the early nuclear industry safety studies also overlooked the risks posed by large inventories of fuel rods on site, the then assumption being that they’d be shipped off to a reprocessing facility as soon as possible, it’s hard to predict the future. 🙂