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. )

Complexity – as I see it and understand..

Introduction

There are simply many ways of viewing and understanding the subject of Complexity. It is overwhelming at times and might confuse any reasonably interested person.

As of now we have different and often differing views on Complexity. And when it comes to understanding and applying complexity in organizational settings it becomes bewildering since there is no dearth of viewpoints, perspectives, techniques, which are often in conflict to each other.

The main reason as it appears to me is that the subject of Complexity is not made up of a single body of knowledge like Physics or Maths or Economics or Philosophy. It is mix of many things. In a way it is good that it makes the discipline extremely rich but flexible at the same time. But nonetheless it can also make things a bit weird at times.

In addition the progress in the field of complexity has tended to take the ‘scientific ‘ slant trying to make things as objective as possible, usually by the help of mathematics. That puts off many who are not mathematically inclined. The reason for taking this mathematical view is not hard to understand. The development of ‘scientific management’ was started by engineers and as we all know engineers love to understand the purpose of a system through measurement, make sense of things through their calculations and then love to predict the future through their created algorithms.

Hence this practice of measure, calculate and predict has become the standard norm in understanding systems. Nothing is wrong with this rigorous approach (myself being an engineer) but at times things are taken a bit too far than necessary – forgetting the basic fact that understanding and responding to complexity is quite unlike any rigorous scientific experiment that usually deals with a few variables or parameters and examines how these variables behave under certain specific conditions (specified by us) strictly in isolation of other factors.

But the study of complexity is completely different. In fact it is just the opposite. It is complex just because there are simply too many variables/parameters playing around in a system – constantly interacting and colliding with each other like atoms and quarks to produce something new. It is an amazingly creative play.

So the study of  complexity is not about observing the movement of individual trajectories of a few variables/parameters operating under specific conditions. It is more to do with observing the trajectory of the ensemble of numerous objects, parameters and variables playing in unison and harmony under their own operating conditions –  not conditions specified by us. It is something like watching a wave rise up at the beach as if to greet us. At that time we are not even trying to see individual strands of water. Well that is impossible. We are seeing the movement of the whole wave as such where the trajectories of individual strands or strings of water combine to produce the entire wave form. In other words we are no longer interested in how a few variables/parameters will behave in different conditions. The conditions are given or as we might say self-created by the system itself – not by us.

The usual way to understand a discipline is to go through most of the theories a discipline offers, master them to a great extent and then go out and see the world with a new pair of lens to make sense of what is happening to decide as to what might then be done about it – i.e improve a situation, maintain it or destroy it.

However, my preferred way to view and understand the discipline of complexity is to turn this usual approach of mastering theories stand on its head. I have seen all systems, even similar systems differ from each other even while operating under quite similar settings. Their behavior differs, which means that every single system or part of the system that we care to observe is unique in its own way. If that be so a few simple rules, laws or theories would not be able to explain such uniqueness expressed by a complex system in variety of ways.

Therefore we would be forced to create an explanation of each system operating in its own unique conditions. This means we would not be able to apply our learned theories or any equation in a straightforward manner. Why is that? That is because of the numerous interactions that keep playing within a system. So there would be many laws, rules, principles interacting with each other to produce what a system produces. So, every time we see a new behavior we are forced to first explain it properly (as close as possible) and then possibly predict its behavior in the short-term since long-term prediction of complex system is impossible.

Hence our earlier approach to viewing the world was ‘Predict first; Explain later; only if the prediction doesn’t match the theory’. In case of complex systems it is just the reverse – ‘Explain first; Predict later, where only prediction in the short-term is allowed.’ This creates a big difference in the way we engage with our world. It means that we don’t have a set of equations to be applied uniformly and normally. The best we can possibly do is to have the data from the present behavior and then create the maths around it. That calls for mathematical thinking not mathematical prowess or great mastery of mathematical techniques.

A Question

However, my question is can we have a balanced view of complexity that might be equally appealing and easily understood by people from both science and humanities? Can it be made simple for every one to understand and apply such understanding not only in organizations but also in our everyday mundane and ordinary lives? If that is possible it would be truly useful to us. Else useless. If it could be made useful then it would not only help us adapt to changes effectively but also help us create something new and better for ourselves and the world at large.

Let us take a live case… 

To illustrate the process let us work through a case in very general terms. Presently I am exploring the complexity of system called Blast Furnace. This system is totally unpredictable. No one is quite sure what exactly is going on inside the system and when and why something would happen.

Let me explain the ‘transforming process’ that happens in a Blast Furnace. Imagine something like a big vertical stove of around 30 to 50 meters high. Hot air is admitted through the bottom of the stove. This hot air moves up the stove due to the pressure difference existing between the bottom and top of the stove (the bottom is at higher pressure than the top).  This provides the energy to the system. From the top iron ore and coke are periodically fed into the stove. This mix, called a burden, takes time (around 6 hours for a mini blast furnace) to come down slowly over a distance of say 30 meters. As it descend through the air the iron ore and coke come together to form layers of rings. Meanwhile the coke starts burning as it interacts with the hot blast air. The heat from the coke melts the iron ore which at a particular stage transforms into a liquid. This molten iron is then tapped regularly from the bottom portion of the furnace ready to be transformed into other products.

Is there something to learn from this?

From this simple description I would like to highlight six fundamental principles of Complexity, which are the following:

1) All complex systems are ‘transforming processes’, which are non-linear and dynamic. (don’t confuse the term ‘process’ with the step by step process we are generally used to in trying to achieve a desired objective). This means unless and until the system is moving it is not complex at all. Complexity arises out of movements initiated by interactions. And as you can see the process that happens within a blast furnace is a ‘transforming process’. Raw materials through various interactions are transformed into molten metal. And different materials through various interactions behave in a particular way. Moreover, material through their interactions produce different shapes.

2) Complex systems need energy to sustain itself. It is simple to understand this. Without energy there can’t be any movement. So as soon as energy flow is stopped complexity disappears. Once energy is admitted into a system the energy spreads all around forcing the different elements within and without the system to interact (even with the environment). Energy can be in different forms. Even communication between different persons within and without in an organization is a form of energy exchange that sustains complexity. Money is also a form of energy exchange sustaining complexity.

3. Interactions between different elements within a system creates complexity. And such interactions are non-linear in nature. The vortex effect arising from the interaction between coke and hot air is an example of non-linearity. Similarly the burden of coke and iron ore taking around 6 hours time to fall through a distance of say 30 meters is another example of non-linearity. This is because in normal case, if I drop a piece of iron ore from a height of 30 meters  it would take a few seconds to reach the ground. So it is fair to say that interactions and non-linearity are the driving forces in complex systems. If any one of these are absent there can’t be any complexity anywhere.

4. The elements in a complex system act in groups or ensembles. For instance the coke forming a spiral cone or the burden forming layers of rings as it descends through the furnace is an ensemble that has its own particular behavior. If it were a simple system every particle of coke would have acted as an individual having its own individual trajectory. Whereas in complex system they come together to behave as a group exhibiting group behavior. Hence we observe behavior of an ensemble not individual behavior of the interacting objects that make up the ensemble.

5. The movement within a complex system is first initiated by valid or authentic constraints. For example, the pressure difference between the top and the bottom forces the hot blast air to climb up. Similarly, the burden climbs down owing to potential difference. Likewise the hot blast air meets with a resistance as it makes its way through the spiral of coke that forms at the bottom of the furnace. Technically this is called the ‘dead man’. So long it is alive the system would work. Remove the ‘dead man’ and the system collapses in a moment. So the movement in a complex system is facilitated by Energy, Constraints and Resistance (resistance in a system is technically known as damping).

6. The shapes these interactions produce is fascinating to see and think about. The interaction of the coke with the hot blast forms a spiral cone of coke through the vortex effect. This spiral cone of coke admits or blocks the passage of hot blast in the furnace. The interaction of the air pressure with the burden not only makes the burden come down slowly over time but also forces it to aggregate as layers of rings like donuts. The spiral shape of the coke cone is also like donuts. Mathematically this shape is known as a ‘torus’.  The reason that it does so is due to the principle of ‘sync’. The materials acquire a uniform collective frequency and amplitude in a complex system.

Let’s go back to the live case… 

Now let me go back to my description of the ‘transforming process’ happening within the blast furnace.

Engineers are very wary of the blast furnace. Why so? Because it operates in peculiar and unpredictable ways. For instance the blast furnace can suddenly ‘hang’ (the burden refuses to descend any further). For for example, the quantity of molten iron that is tapped out from the furnace around every hour varies quite a lot in quantity. Or the furnace can suddenly ‘slip’, meaning that the burden suddenly accelerates its movement and comes down too quickly. It appears that it has a mind of its own. This high degree of unpredictability is simply charming to blast furnace engineers. That is why blast furnace engineers refer to a blast furnace as ‘she’ (no offence meant to women). But you get the idea quickly. No? Engineers aren’t quite sure what causes this. But the discipline of complexity can provide some answers to this vexing issue, which I shall now try to explain with six more fundamental concepts on Complexity.

What do we learn now? 

7. Small difference in the interactions produce such unpredictability. Such small difference in interactions between different elements (useful to visualize the relationship between different elements as vibrating strings) create the uncertainty in the behavior of the ensembles of various elements like the coke, burden etc. The uncertainty that happens over time in a non-linear fashion is exhibited by the formation of new patterns displaying new forms of behavior. If it were linear it would have been highly predictable. Isn’t it?

8. It is interesting to note that such differences are in the form of fluctuations and the group behavior that is displayed is also in the form of fluctuations (hence the image of a vibrating string might be helpful).  Owing to the interactions such fluctuations suddenly amplify (again a display of non-linear behavior) to create new forms and new behaviors both of which are highly unpredictable in long-term.

9. So when a complex system is operating normally, i.e no unusual behavior is observed the system can be said to be operating in equilibrium, i.e. it continues to behave the way it was behaving a moment earlier (very linear). In such a situation the fluctuations either die out or remain in a steady state situation. It implies that the damping factor or the resistance is uniformly applied on the system and there is no non-linear changes in energy content or constraints.

10. However, when the energy content or any (or all) constraint(s) in the system or the damping in the system changes the fluctuations suddenly amplify to produce complex uncertain behavior of the system. Then it is said that the system is suddenly operating away from equilibrium conditions. When this happens the system breaks the symmetry of time (where the past is not the  same as the present) to create a new behavior or a new form on its own (technically know as self-organization). Fair to say that it is the ‘difference that creates the difference’.

11. When such time symmetry is broken then something called ‘bifurcation’ occurs. In plain language it means the system opens up two possible paths to proceed. But what path it cares to take is not known or can be predicted in advance with any degree of accuracy. The system ‘chooses’ the path. Do we have an example of this in our description of the blast furnace? See that the furnace can either ‘hang’ or ‘slip’. These are the two possible paths that are available to the system. What path it would take and why it cares to do so is not know. It is completely up to the system’s ‘collective and contextual intelligence’ to decide the path it prefers to take. Certainly this is out of human control. I love the associated philosophical meaning. While dealing with complex systems human beings have no control. It is much like what keeps happening within our own bodies and minds. We can also see enough examples of that happening in Nature. Enough to say that it is ubiquitous.

12. We may conclude that all non-linear dynamic complex transforming processes are very creative and are highly intelligent.  It possibly helps us to realize that the causes of complexity lie within the dynamics of the system. They are neither outside the system nor separated by time and space.

Is there a simple way to negotiate complexity?

But how do we deal with such complex systems? It is clear that we can’t force our way through complex systems or make it behave the way we like. A possible way that appears to me is to befriend a complex system, listen to what it wants to do and then take creative measures to make it flow in our desired direction or decide to flow with the system (adaptation) or create new directions (creation).

The possible path to effortlessly flow along with any complex system might be stated in a simple way, which is as follows.

a) Pay close attention to the changes in behavior of the system.

b) Pay close attention to the difference in the fluctuations arising from the numerous interactions that go on in a complex system to note when these die out or when they amplify.

c) Change/Re-design, Maintain, Destroy the Energy content of the system, Constraints of the System or Damping of the system. These are the only ways to play with any creative, intelligent complex systems.

It hardly matters whether we approach a complex system through qualitative understanding or through quantitative mathematical techniques. It is all the same. But while trying to tackle through quantitative techniques it is useful to remember that we don’t start with an equation and then apply it to predict as to what might happen. We start by observing the changes in behavior and difference in fluctuations and then form necessary equations and algorithms to explain and predict in the short-term.

These 12 principles can be applied to any complex system with ease.

Conclusion

To conclude Complex Creative Systems is philosophically very appealing to me. Human beings are always in search of meaning, purpose and prediction. I believe that we do so since it is our innate nature to transcend our small self in order to reach the infinite to be joyful and happy.

For this, we have tended to look at the past to find causes. Or we have projected our mind to see the future, however dimly. Then we have tried breaking down parts of the system (reductionist method) to create meaning. Or we have tried hard looking at the whole (holistic) to find the purpose.  Both approaches are frustrating and stressful to say the least when we use them to deal with complex systems.

Understanding of Complex Creative Processes offers us a new way — to be in the present moment and be there. It is the place where the past, the present and the future converge and meld into one. It transcends both reductionist and holistic approach simply by paying attention to the given moment. That is happiness in the true sense of the word. The ability to do something worthwhile from that sets us free to change ourselves and possibly the world. The future is unknowable but at the same time can be created. Isn’t that powerful?

I agree with the Upanishads, ‘You are That!”

References:

1. End of Certainty by Ilya Prigogine

2. Web of Life by Fritjof Capra

3. Sadhana by Rabindranath Tagore (Tagore Omnibus)

4. Complexity and Management by Ralph D. Stacey and others..

Note:

If you are interested in understanding the approach to flow with complex systems you might like to go through another post of mine, which is ‘How to Embrace Complex Systems with a Smile’ http://wp.me/p2CS2f-A

Problems, Landscapes, Habits; Leadership in the 21st Century

Excerpts from forthcoming book ‘Dancing on Peaks; Resolving Wicked Problems – A Nemetical View of Life

……

Fortunately, not all problems that we face in life are wicked. For most of these, though relatively few, we can get over them with our effort and practice. And we can do that so well indeed that they don’t seem like a problem any more. Like for instance, my getting to my desk, booting up my laptop, connecting it to the net and then letting my fingers fly over the keyboard at great speed to write this book is a simple problem. Though years back it took me some time and effort to master the process today it is effortlessly simple and predictable. But I remember my first brush with the computer, which was over two decades back and those were tense moments. It took me hours and some training to figure out MS-DOS and hours of brutal typing practice with some coaching from a friend. Resolution of such problems doesn’t require much thought. These can be easily mastered through controlled and dedicated effort guided by mentors if possible. The solution to such problems are known and are easily available. These I call the “library type of problem”. The operating context is predictable. It is something similar to mastering maths. A teacher or mentor is available and the answers are at the back of the book. We can refer to such ‘library type problems’ as problems of ‘flat landscape‘ since it is akin to walking in the park. Such problems can be easily mastered through the ‘habit of memory’.

Then there are problems that are slightly different to ‘library’ problems. It might be something like this – how can I get from my house to my office (10 km) in the shortest possible time and expense without sacrificing comfort. Given the information, such type of problems are straightforward problems. The problem opens up choices and a fairly intelligent choice has to be made. However, the result is always not guaranteed. Sometimes things can go wrong and we can be thrown off our desired intention. Such straight problems are fairly easy to tackle. And with some experience these can be tackled quite well. Hence I call these ‘experience type of problems. Such problems can be framed like – how to climb Mount Everest safely. There is one particular objective to be achieved. Once that can be done the problem no longer exists. More the experience better are the choices we can make and better can be the associated planning. And with better choices, planning and action the targeted outcome is achieved easily. Adopting best practices in the field also helps a practitioner. Hence such ‘experience problems‘ are problems of ‘single peak landscape‘. Such problems can be mastered through the ‘habit of planning and making choices‘.

Then there is a third type of problem which is continuous in time. We achieve something and then prevailing situation demands that we achieve something more. It is like scaling a mountain range, like the Himalayan range, which is full of peaks. We climb one peak and then we try to climb the next peak and then figure out how to reach the next. Sometimes we can get from one peak to the other peak quite easily, if they are nearby with a reliable connection between them. At other times we might have to take a detour, climb down from a peak and then scale up another. In real life this might resemble improving productivity or opening up new markets in a closed economy. While the economic environment doesn’t change much we strive to become better and better from our existing position. These are not very easy problems to resolve. It is similar to a cricketer who excels playing at local level and then aspires to excel playing his game at regional level before trying to move up and play at the national level. This is where complex problems start to surface. It would need enhanced cognitive skills, a basic level of contextual intelligence, ability to learn from mistakes, strategizing, refining intentions, better decision-making skills, emotional balance and continuous moment to moment adaptation without losing a sense of direction over long periods of time. Such type of complex problems may be termed as problems of ‘Rugged Peaks landscape‘. Such type of problems can be mastered through the ‘habit of time and learning‘.

However there is a fourth type of problem that needs constant adaptation in a complex environment. Such systems are called Complex Adaptive Systems. And the problems in this category can be seen as ‘adaptive type of problem’. Continuing our analogy of the ‘rugged peak’ problems, let us imagine for a moment that the ground below us continuously dances and also gives away at time. So the peaks, which were rather stationary in the previous case now start having different heights at different points of time. The peak that appears small suddenly grows big and the bigger peak suddenly drawfs in relation to the peak we are presently on. Nothing remains constant in both Space and Time. These are real ‘wicked‘ problems. Everything is dynamic leaving us clueless about both position and the rate of change (velocity) at any given instant. It might be better to call them the problems of ‘Dancing Peak landscape’.

In this book we would focus specifically on such problems. Such problems need a high degree of contextual intelligence, where previous experience would hardly be of any use. Sharp cognitive skills would be needed that would call for taking various perspectives at different levels along with a high ability to reflect, ability for deep understanding, instant strategy, quick actions and strong adaptation skills. This type of problem can’t be easily tackled by the habits of ‘memory’, ‘planning’, ‘making choices’, or by habits of ‘time’ and ‘learning’. Taking on such types of problems would need the habit of ‘practice of preparedness, attention and serendipity’, that is the habit of a ‘prepared attentive mind’ moving from moment to moment in time. This in Nemetics we call as ‘attentive contextual intelligence’, which is a mix of collective intelligence, combined with feelings, intuition, rationale and intelligence of an individual.

Finding such problems is not difficult. Actually such problems occupy most of our lives; problems for which we don’t have the answers and can’t predict when such type of problems would surface. And they are dynamic in nature. Slight changes in global economy throw national economies out of gear. It affects business operations, which must quickly adapt in order to survive. Customers change. Markets go topsy-turvy. Profitability goes under tremendous squeeze and the notions and targets of productivity and performance change continuously. Job markets fluctuate. Nature of jobs are redefined. Personal lives get affected. Even Nature gets affected. Climate changes. Plants and animals get affected. It then appears that we are caught in a deep and frightening whirlpool.

Under such situations, there are no answers at the back of the book. There are hardly any choices to quickly select from. There is no question of optimization. Experience hardly helps. Dedicated hard work might prove useless. Agility and resilience might have no real meaning. There is only one answer but we are left clueless. There are no best practices to follow, no techniques to use, no process to adopt, no framework to guide our minds. We either get it or we don’t. If we get it wrong we are doomed to be sucked into the whirlpool even deeper till we suffocate to death. If we get it right we live to see another day and perhaps another new moon. However, the only wherewithal we might have to rely on is the quality of our feelings and thinking brought together through the habit of ‘practice and serendipity’ or simply having a ‘prepared attentive mind’ since the need is to adapt moment to moment. Or simply stated, our contextual intelligence can come to our rescue to maintain balance.

In order to develop and apply such contextual intelligence to wicked problems operating in a ‘dancing peak’ landscape, Nemetics is an option. Nemetics is a flexible thought model that allows us to synthesize mathematical thinking, subjective insights and feelings to re-design our lives for the better. The objective of the flexible thought model is to make sense of complex adaptive systems and to act upon them. It may be effectively applied to various fields like organizations, manufacturing systems, engineering, organizational sociology, economics, design, system design, system reliability and even to psychology and a host of others fields.

In short Nemetics can be best described as a study of origins of the various complex phenomena within which we exist. Or in other words it is the ontological inquiry in general that seeks the transcendental truths operating behind everyday phenomenon.

This practice of Nemetics stems from the fundamentals of complexity science as applied to complex adaptive systems and is based on the time-tested principles of Engineering, Chaos, Complexity Science and humanities like social and economic systems.

Since the aim of Nemetics is to gain direct knowledge of the transcendental the fundamental premise is praxis for the simple reason that the theory of such complex emergence (a term which we shall deal with later) simply might not exist. It has to be worked out. The idea is to move from practice to theory and then to practice again.

In other words we first explain the situation, then act upon it and then only predict the outcome as a way of reflecting on our thought process and our decisions. We do so through attentive reflection. It is a practice to train the eye and mind to be prepared and attentive to spot emergence, engage with its structure and behavior, mull about the drivers that drive complexity and then exchange that helps to adapt to complexity.

Life is then in perpetual beta – no hanging on to assumptions, beliefs and opinions. That points to adopting a stance of nuanced but effective adaptation based on ‘attentive contextual intelligence’. It is a tall order, which asks us to do what is needed to be done and then keep adapting and tweaking as time goes on and situations change.

That is what Leadership of the 21st Century would look like. Problem solving would grow lesser in importance. Problem solvers would be passe. Problem and paradox resolution would take prominence. And persons who can resolve complex problems and have the ability to predict in the short-term would be highly regarded and would be in high demand. That can only be done by people who can gain direct knowledge of transcendental truths through their highly developed contextual intelligence. They with their highly trained minds would be simply priceless!

Summary:

  1. Types of Problems: Library problems, Experience Problems, Complex Problems, Complex Adaptive Problems.
  2. Types of Landscapes: Flat, Single Peak, Rugged, Dancing Peaks
  3. Habits: Time, Planning & Making Choices, Time & Learning, Attentive Contextual Intelligence
  4. It is not unusual to find combinations of ‘Type of Problems’, Landscapes and Habits co-existing within the same situation.
  5. Whole of life is nothing but a series of changes and issues waiting for resolution, facilitation, modification and nurturing to leverage us to new dimensions and states.
  6. Leaders of the 21st century would posses an unusually high degree of ‘contextual intelligence’ to reach essence of complex situations in a wink and know how to deal with those.