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.

Dealing with Authority Bias – A Blind Spot

In the old days of the airlines, the captain of an aircraft was the king. His commands could not be doubted or challenged not even by the co-pilot who was second in command. Even if the co-pilot noticed an oversight he could not openly point that out to his/her captain. Sometimes it was out of fear. Sometimes it was out of respect. Such a behavior caused many fatal accidents to take place. For example, when a very well respected captain of a Russian (former USSR) plane allowed his teenage son to take control of the plane his co-pilot lacked the power or the courage to point out the obvious flaw in decision making. The young child committed some mistake in controlling the plane, which lead to a fatal accident killing everyone on board. Such a behavior of blindly following authority, termed as ‘Authority Bias‘ was rampant in the airline industry.

Since this behavior was discovered, nearly every airline has instituted something called ‘Crew Resource Management‘ (CRM), which coaches pilots and their crews to discuss any reservations they have openly and quickly. This was a very creative way to slowly deprogram the authority bias. Needless to say, CRM has contributed more to flight safety in the past twenty years than any technical advances have.

Many companies are light years away from such foresight. Especially at risks are firms with domineering CEOs and JV partners. In such cases, employees are more likely to keep their opinions and judgments to themselves and not express or exchange their thoughts, observations and opinions openly — much to the detriment of the business. Authorities routinely crave recognition. So they constantly find ways to reinforce their decisions and their status. Slowly this sort of demand on employees leads to what is known as ‘groupthink‘ or ‘hivethink‘, which is perhaps one of the most dangerous phenomenon to emerge in any organization.

One of my clients was suffering from this ‘Authority Bias‘. They were almost bleeding to death. They had had a domineering JV partner, who was the technology provider to the firm. They would simply not allow anyone to have their say in anything. They always had their way. They blamed their partner for being lazy and undisciplined and what not. As a result, employees just closed themselves from doing anything on their own. They actively disengaged from work. Performance and profitability nose-dived to the point where the domineering JV partner decided to quit.

Soon after they were gone, performance rose to unexpected levels and stayed there. Productivity improved by more than 10 times. The organization saw profits for the first time in five years of their operations.

And they managed to survive.

But how does one check for the blind spot of ‘Authority Bias’ in an organization?

Rise and Fall of Nokia in India: Missing Patterns

On 28th March 2013, Nokia’s senior VP (India, Middle East, Asia) D Shivakumar quit the company after serving it for eight long years.

Shiv was known for his personal conviction on the importance of leadership. His conviction ran so deep that he sponsored many leadership programs throughout the region.

However, his tenure in India saw mixed results. While Nokia gained in brand image yet it suffered in sales.

Why was that?

Firstly, it completely missed out the emerging market of dual sim wave till it was too late. While competitors launched dual sim models in quick succession Nokia had nothing to offer. When it finally entered the market it was just too late. By that time their competitors have already grabbed 60% of the market share leaving Nokia with little or no elbow room to leverage. It substantially weakened Nokia’s leadership position.

Secondly, the company also failed to notice the emergence of smart phones with Android and Apple OS.

Nokia paid a price for not noticing two significant new market patterns in time – dual sim and smart phones. Their once enviable share of 60% of the market share quickly eroded to less than 40% in a matter of say two years. It now seems that this slide is irreversible.

All because leadership failed to see emerging patterns and act in time. And their aspiration did not match the aspiration of their consumers.

A costly mistake indeed.

Do you think ‘seeing patterns’ is leadership’s number 1 job?

 

Note: 11th Feb 2014:

That the above analysis made about a year back was correct is confirmed by this article dated 11th Feb, on Nokia’s attempt to stop the  slide http://tinyurl.com/pevtwho 

My prediction is they would still not be able to stage a comeback. They missed a few more vital perspectives in their strategy.

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. )