Predicting Black Swans – Part II

In the earlier post we dealt with the concept of predicting a ‘black swan‘.

In this post, I intend to explore the concept a bit more: what exactly we monitor to notice a ‘black swan’ in time?

In doing so we would be forced to consider the natural response of a system.

The starting point of our exploration would be to understand how any system, as a whole, whether natural or engineered, would disturbed by a ‘black swan’.  A system is disturbed in three possible ways, which are as follows:

a) A system loses energy till it reaches a tipping point

b) A system gains more and more energy till it crosses the point of system resilience

c) A part of a system emits more energy than it is normally supposed to, that is going beyond the linear response of the part. 

So the natural way to watch a system to expect a ‘black swan’ in time, is to keep a tab on the ‘energy’ of a system in the following ways:

a) Monitor the entropy of a system. As a system functions the entropy of a system gradually rises till it hits a threshold limit indicating the appearance of a ‘black swan’ or an outlier. 

b) Monitor the energy gain of a system till it crosses the ‘resilience’ point to give birth to a ‘black swan’, outlier or a ‘wicked problem’. 

c) Monitor critical parts of a system for excess emission of energy till it goes beyond the linear response of a part. 

It is useful to remember that energy is transferred in ‘quanta‘ or in packets of energy. Therefore, it is natural to expect jumps of energy levels as we record by capturing the different manifestation of energy levels on monitoring trend charts. So when a ‘jump’ is big enough to cross a threshold limit or resilience point or linear response level indicated by its presence outside the Gaussian distribution range  we can be quite sure that a ‘black swan’ or an outlier or a ‘wicked problem’ would soon arrive on the scene. We call such an indicator as a signal.

Therefore, the central idea is to capture such signals in time, just before a ‘black swan’ makes it way to appear on the scene to dominate and change the system.

However, the question is how early can we detect that signal to effectively deal with the inherent ‘black swan’ in a system, which is yet to appear on the scene?

That would be explored in the next post.

3 thoughts on “Predicting Black Swans – Part II

  1. I’m trying to ‘catch up’ here as the whole time I’ve ever considered or understood the phrase “tipping point” it referred to a building up and reaching a ‘maximum’. I’m trying to see it from the perspective that you’ve shown here of it existing on the opposite end of the spectrum.

    Any corresponding references?


  2. Vibration analysis … for compressors, brigdges … and maintenance. Log files and analytics for data networks …. but it got encrypyed, and the observer loses his lenses …. where is your data ,,, and who is listening to all of it?

    Sete for aliens

    Cops for the poor

    Dimoans for the rich

    How many sensors physical and virtual do you think the energy (power) disterbances may come into focus.

    I agree, syudling bees won’t help with tribal customs and ils such an the vile mutalation of young giles in africa … sensors and bees indeed. With all due respect … i see scope creep in science vs human need.

    In the game on nemetics, one choose to participate or not. The truth is not sexy, is is macarbre


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