Predicting BlacK SwaNs

As we know now, the world is full of complex systems. However much we wish, we can’t avoid them.  We see them in our organizations. They are there in our societies.  They can be experienced in ‘cloud bursts’ and in flash floods. We even find them in our families.

They are nagging and they are wicked at times. Wicked in the sense that it often leaves us baffled preventing us from acting skillfully.

The question is how do we deal with them?

It is easy to deal with any complex system if we are able to predict their behavior over time.

However, the idea of prediction is a bit different from our usual idea of prediction. Our usual idea about prediction is, if we know sufficiently about the behavior at any point of time, we would be able to predict the behavior of a system any time in the future.

But that is not what bothers us about understanding and dealing with complex systems. And it is fair to say that such predictions are absolutely impossible with complex systems. Therefore, with any complex system all we are interested about is to detect the appearance of a ‘black swan‘.  A black swan is some sudden unexpected change in the behavior of a system that disrupts the system and brings it crashing to the floor. The crash of 2008 provides an extreme example of a black swan. But such examples are rather common in our lives. Our careers crash. Organizations crash. Our health suddenly crashes. A ‘cloud bursts’. Or for that matter any system is likely to be disrupted any time by the sudden appearance of black swans.

So what might we do about it? Does it help if we notice the change in behavior of each agent or individuals that form the part of the system? For example, does it help if we watch individual behavior of employees in an organization? Or for instance, does it help if we monitor individual performance of school or college students? We know that such methods hardly help improve the system though we are enslaved by such methods by ‘blind faith’. A complex system would keep doing  what it does. That is its role or purpose.

The good news is that the behavior of any complex system can be monitored and predicted for black swans, a little in advance, before it strikes us with full force to bring the system to its knees.

In order to predict black swans we need to know of one very peculiar phenomenon of any complex system. That is a complex system behaves linearly for most of its time when it is free from a black swan or an outlier. Then as a black swan slowly creeps into the system the system suddenly behaves non-linearly. When it behaves linearly it gives us a false sense of security. We feel everything is fine and hunky dory and would stay like that forever. We take pride in our design.

Lulled by our false sense of security, we then forget that non-linearity is just waiting to strike us. And when it strikes we are so much confounded that we rush like headless chickens to ‘fix’ the ‘problem’. And believing in our superior intelligence, we keep ourselves busy ‘fixing’ problem after problem till we drop dead from such heroic efforts.

Mathematically speaking, while linear behavior of any complex system follows Gaussian distribution; the nonlinear behavior follows some sort of power law. So, it is the mix of the two, never one thing or the other.

By understanding this phenomenon clearly, we can ‘predict’ a black swan or an outlier very easily. It is deceptively simple. Simpler than what we perceive it to be.

It is liberating too. Once the presence of a black swan is detected, much before it actually happens, we are left with enough time on our hands to deal with it effectively and skillfully. It also leaves us with the possibility to dramatically improve the system big time.

The truth is there is no ‘randomness’ anywhere. The concept of  ‘randomness’ is a big illusion at best.

So, then, what are we waiting for to improve our lives?

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How To Observe Complex Issues?

I am often asked the method of observing complex issues enabling us to innovate (creating something new) in a complex environment, which might be applicable to diverse domains such as engineering, maintenance, management, strategy, marketing, etc.

Given below are the fundamental principles of Observation based on the science of complexity, which help us investigate or look carefully at any complex problem in any domain and come up with new solutions that create or help support desirable outcomes. Much depends on what we choose to observe and how we see those. Therefore, the principles act as general guideposts for observing reality as accurately as possible.

The Principles of Observation of Complex Systems:

1. Interactions between elements (usually diverse) create complexity and the quality of the interactions change over time. Hence observing interactions is the first important step.

2. Quality of interactions change because some quantity within the interactions change “non-linearly“.  As quantities change monotonically problems appear. This can also help us solve problems. Therefore, observe what changes non-linearly.

3. The “non-linearity” of all complex systems is hidden in the interactions. This changes the system behavior as a whole, which also exhibits non-linearity. The outcome might be ‘desirable’ or ‘undesirable’. So, observation of the non-linear system outcomes and their effects and patterns is the third most important step in observing complex issues.

Hence, the solution lies in taking care of non-linearity. This is not to say that non-linearity would magically transform into something linear that makes it easy for us to manage but we attempt to keep it within some limits or introduce or amplify some other non-linear phenomenon or eliminate or mitigate certain non-linearity as the case might demand. That is the essence of obtaining a solution.

Based on these three principles of observations we can see more deeply into all changes that happen in the real world. This leads us to few more principles of observation, which are the following:

4. Changes happen due to interactions of physical forces, chemical reactions or electromagnetic interactions or human interactions or interactions between nations or communities or interactions between organizations and customers or economy and the market etc.

5. These create specific patterns and shapes which if we are lucky might be captured as data or information. However, such data or information are always relational else no movement would ever happen (consider the force of walking vs the force of friction — they are in tandem – one never leaving the other — forming a movement).

6. So when we look at relational data and trend them we find a pattern. Such patterns can be of various types and shapes (like spirals, waves, cracks etc. )

7. Since real world systems work far from equilibrium conditions (that is interdependence of diverse elements – where everything affects everything else) such relational trends either run in ‘sync’ or ‘out of sync’. (that is ‘in phase’ or ‘out of phase‘ – even happens in all machines and human activities like a group of people walking in the park or even in heart cells).

8. When they are in ‘sync’ things might be normally considered as ok i.e. the system is stable. When they are out of sync (or out of phase) it indicates an impending change, which might be desirable or undesirable.

9. The impending change is predicted by a sudden amplification of one of the relational parameter that tends to bring movement to a halt (consider friction either suddenly going up or down while walking and how it would immediately stop movement).

10. Such ‘out of phase’ and ‘amplifications’ are the hidden imperfections in any complex issue, identification of which help us solve or resolve any complex issue.

11. Fortunately, we can understand complexity by starting out anywhere in the system to find out the critical pairs of relational parameters that cause movement. In all cases it is the energy that causes all movements of the system. So for machines it might be understood by temperatures or vibration or wear. Similarly for human interactions it might be considered as movement of people or money or goods or output of something — any suitable variable depending on the context and our area of interest. Suppose the movement is spiral (because of the swirling nature of the hot gases or fluids with high energy) the movement is best captured by the two temperature points which would either be in ‘sync’ or out of ‘sync’. These two temperature points form the ‘critical pair of relational parameters’.
 
12. By trending such ‘critical pair of relational parameters’ we attempt to make the ‘invisible’ patterns very ‘visible’ enabling us to make or take the right decisions.

 
I have tried to explain the 12 principles of observation based on which investigation of any complex system may be carried out. Once the patterns are understood we can then create proper algorithms that enable us to take decision quickly and precisely. 

Learning Complexity — Leadership Series – 1

Here is one of many toys I use in my classes on Leadership in Complexity to demonstrate complexity through play. It is a simple and common toy – a double pendulum. It is interesting to see how interactions between few elements really produce complexity. So, the question that I ask at the beginning of a session – ‘Can we predict what is going to happen?

We have made a video demonstration of it. It is about 5 mins. Hope you would find it engaging. You may choose to skip it if you like. I suggest a try. While you are viewing it mentally start predicting what might happen the next instant…

Predicting Complexity? ( <– click on the adjoining link to view the video)

What do you find?

Is complexity predictable or not?

On the face of it it appears that it isn’t predictable at all. The movements of the loose limbs of the double pendulum simply go crazy. It is not or nearly not possible to predict. Every time we think something like this might happen it usually turns out to be something else. It appears that there are no definite patterns about it. It is too random to make sense. No doubt this is what always happens in complex adaptive systems.

But then I show how complexity can be predicted along with many of its principles.

At first it feels rather strange to realize how all complex systems or complex adaptive systems are inherently predictable as an ensemble in the short run and how they all follow the same rules of the game.

That is really fascinating. It gives us tremendous hope to embrace complexity with faith. There is no point in ignoring complexity since we are entangled with it every moment of our lives. But once we embrace it knowing fully well how to read, learn and go about it —  life is simple indeed. The objective of learning about complexity and applying its principles is to make life simpler; not more complex.

That promises us an alternative way to lead our own lives through creativity and adaptation.

This alternative Leadership path can be summed up by three simple rules, which are —

1. Explain what is happening.

2. Institute methods to Foresee what might happen in the short term

3. Envision desired Interventions to make the system flow in the right direction.

Three of the best designed interventions that I found are a) Education b) Interactions c) Design. These give long term ongoing benefit for many.

So what do you feel and think about it?

Acknowledgement:

(I personally thank my colleague Trichur for prototyping complexity through this model. )