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Black swans, fat tails and risk – So what?

Black Swan

I was recently reading a blog by Nassim Taleb, where he decried the lack of understanding surrounding his book, ‘Black Swan’ when it was originally launched in 2007 – apparently too many people thinking it was about the prediction of outliers (black swans).  I’m not a statistician, I certainly wouldn’t be called a quants man, but I have been accused of obsessing over the impact of the unpredictable on KM systems (especially soft systems).  As you will have seen from my posts over the past two years, I believe in human agency and people are inherently unpredictable (we just don’t know enough about the whole person to be able to predict their behaviour with 100% certainty – irritating when you are trying to predict ROI on a CoP implementation strategy), which means that we are building systems to interact with a highly unstable element.

Anyway, back to unnatural swans.  Shortly after reading Taleb in 2007 I started to hear more about ‘fat tails’ – my wife initially thinking this was some sort of code to enable men to pass derogatory comment on the female derriere, but I digress.   So, what about these ‘fat tails’ and how concerned should I be about them?

I’ve attended a number of conference ‘lectures’ over the last four years, where the assumption has more often than not been that everyone in the room is a quants person and can appreciate the pontificating of the psuedo keynote/lecturer (University lecturers who really want to be high-brow consultants are the worst – looks at himself between 2008 and 2012 and worries) – For the best explanation, see Dave Snowden’s videos on the topic.  We then disperse for the break and you can tell the non-quants, we are the ones looking unsure of ourselves, masking our uncertainty by extolling the virtues of power laws and Pareto over the Gaussian distribution (we may not always ‘get it’, but boy we can regurgitate it).  I was at one conference in Hungary where I had enough and asked a group of people who were happily drinking coffee, patting themselves on the back over their ‘regurgitating’ prowess, “yes, but so what?”  People suddenly had to refresh their coffee before the next session… hmmm…had I just been ‘found out’ to be an intellectual light weight or was it them that had been ‘found out’?!?

The basic premise is that black swan events (the high impact, low probability events) are happening more often than we once realised (emphasised by a change in observational lens).  Risk alert! Okay, you have my attention – my eyes are darting around the room and I am looking for the nearest exits.  People are talking about Gaussian Distribution versus Zipf versus Pareto; the warning being that Pareto produces a ‘fat tail’ that demonstrate that high impact, low probability events are happening more often.  My initial reaction, remember, I am not a quants person, was that if the events are happening more often then surely that increases the probability of them happening and in which case, they aren’t so low probability after all…I also considered that perhaps we are just more connected and therefore data availability has shifted the perception of occurrence frequency.

Then there is the ‘high impact’ factor, that really had my attention.  I started transferring findings on earthquakes to the business setting – a popular ploy, well crafted by some who attempt to create the illusion of certainty (a mistake from the outset), but talking about probability (always a bad idea to mix risk with emotion, as happens when you talk in the realms of probability over certainty).  I was also found myself wondering about the relevance of the lens (Pareto versus Gaussian distribution).

Here is the problem as I see it.  First, the idea of the lens (Gaussian versus Pareto), so what?  There is serious debate as to the validity of Power Laws in complex environments; for example a strong argument is presented by Stumpf (Critical truths about power laws, 2012), claiming that power laws arise from infinite systems and real systems are finite.  That said, the same author does go on to say that it is not “knowledge of whether or not a distribution is heavy tailed is far more important than whether it can be fit using a power law”.  He also says, “The fact that heavily-tailed distributions occur in complex systems is certainly important (because it implies that extreme events occur more frequently that otherwise would be the case).  So, power laws might not be valid in complex domains, but their fat tails are important.  Don’t you just love how downright dizzying intellectual debate can be at times.

This isn’t very satisfying. What does this fat tail really mean?  What is the real risk?  Tell me that and I can start to deal with the problem. I need to know detail.  I want to know the proximate cause, but, more importantly, I want to understand the underlying conditions that contribute to it.  Sure, I want to know that a risk exists, but I want data to make sense at the same time.  I want to relate it to my world, not earthquakes (we apparently have 2-300 in the UK per year, but none have ever bothered me – there was one when I was living in Iceland once, but that’s another story). I want to relate it to organisations and the real world they transact in.

For me there are three things I want to know when it comes to low (but increasing) probability, high impact events – From the following it could be said that the first two are conditional (and could add noise to judgement), whereas the third could lead to a more considered conclusion:

1.  Relative risk (what is the risk of occurrence in my sector versus any other sector?)

2. Absolute risk (what is the risk of occurrence over a period of time?)

3. Natural frequency (risk communicated by frequency, e.g. 1 in 1,000)

There you have it.  This is what I want to know when I talk about risk to organisations (with particular emphasis on natural frequency).

The problem, we can talk about historic data from past events, but implications and impact will not be transferable (we can invest hundreds of thousands on Lessons Learned Information Systems and, in terms of these type of high impact/low probability events, we either don’t capture what we can really transfer in the short-term and the big lessons, well, we don’t know if they have been ‘learned’ until the next big event occurs – at which time the LLIS will be archived and nobody will know how to access it).

We can talk about frequency, but we cannot predict the next event or a likely time-frame for that event.  We can raise awareness, but we offer nothing ‘tangible’.  We play on emotion and, as I have said before, when we play with emotion we can often make unsound decisions.

The bottom line, from a non-quants person, there is something distinctly unsatisfying about the fat tail.  I agree with Stumpf, in that it certainly seems important, especially in considering complex systems and developing resilience, but, much like the restaurant that your friends have all raved about, raising your expectations to unreasonable highs (Lescargot Bleu in Edinburgh, that means you – twice!), it is just a little unsatisfying.  Then there is Dave Snowden’s concept of Probable – Possible – Plausible and the world suddenly starts to make more sense

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7 comments on “Black swans, fat tails and risk – So what?

  1. Stephen Bounds
    December 11, 2012

    Hi David,

    I have some thoughts on this (including an appearance from Schrödinger’s Cat), but it’s a bit long for a comment. See here: Be interested to see what you think!

    • David Griffiths
      December 12, 2012

      Hi Stephen and an interesting post (I also responded on your blog).

      The only thing I would say is that my post was more leaning towards the concept of ‘fat tails’ and the apparent increase in low probability/high impact events; my assertion being that the mere existence of a fat tail tells us nothing (my, “so what” question) – to say nothing of the question of power law validity in the complex domain.

      What is much more interesting to me is talking in terms of natural frequency, which is primarily used in the medical field to allow for clearer thinking in the decision-making process. This is where I slightly disagree, where you say, “The question is not ‘how likely is this to occur?'”. I believe that the ‘likelihood’ of the event directly effects the decision weighting process and therefore weights the response… in terms of CBA and energy (time) and resources (people, finance, tech) applied to solving a potential problem.

      Good discussion, as always…


      • Stephen Bounds
        December 12, 2012

        Hmm. I see where you’re going with natural frequency. It’s attractive, but to illustrate a possible problem consider this:

        7 in 1000 people will develop invasive ovarian cancer at some stage in their lives

        A true statement; however, it’s not a useful statement if you are a man! Similarly, natural frequency is most useful for decision-making in organisations where we know that the organisational population is more or less homogenous on the observation in question.

        To pre-answer a counter-argument: It’s true that if we have no data on risk factors or other causes of variance, natural frequency is a good place to start. But it’s not yet clear (at least to me) that organisations are similar enough across the spectrum to justify such a broad brush approach.

      • David Griffiths
        December 12, 2012

        And there is my argument for ‘context’…as with the fat tail. Put in context, your example is of no interest to me and therefore I see no risk. However, in a ‘fat tail’ context the meaning in the data (the fat tail just being structured data) does not exist. The ‘fat tail’ just bunches the ,cancer’ data (to use your example) into one phenomenon, it is only by separating it out that I can make a coherent, logically weighted, decision.

  2. gastonbilder
    January 9, 2013


    Great post and enlightened discussion. I am a lawyer (clearly that marks me as a non quant) and have read some of Taleb’s books. I tend to agree with you that “We can talk about frequency, but we cannot predict the next event or a likely time-frame for that event. We can raise awareness, but we offer nothing ‘tangible’. We play on emotion and”,… “often make unsound decisions.”.

    Even if you have managed to calculate a natural frequency / probability and you think that you have figured out correctly the context, there is still room for a black swan.

    • David Griffiths
      January 10, 2013

      Thank you for the comment and glad you liked the blog… The key is how prepared we are to deal with environmental disturbances, as opposed to getting into the prediction game, which, I believe, Taleb didn’t do a good enough job of communicating when Black Swan first came out.

  3. Pingback: Synchronization versus collaboration:: uncertainty v risk « Get "fit for randomness" [with Ontonix UK]

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