Creativity in Solving Complex Problems

The other day, at the end of my seminar on “Solving Complex Engineering Problems” a delegate asked me as to whether the entire process of solving complex problems can be automated in some way by means of a software instead of relying on human creativity.

Such a response wasn’t unexpected. In the corporate world the word “creativity” is often looked at with suspicion. They would rather prefer structured and standard approaches like “brainstorming” at 10.00 am sharp or team work or collaborative effort, which in my opinion do little to help anyone solve complex problems or even address complex problems correctly.

That might be the single most important reason why “complex problems” remain unresolved for years affecting profitability and long term sustenance of an organization. Failing to resolve complex problems for years often earns such problems the sobriquet of “wicked problems”, which means that such problems are too tough for “any expert” to come to grips with.

What they sadly miss out is the role of creativity in solving complex problems, which no automation or technology can ever replicate. They miss this because most organizations systemically smother or mercilessly boot out any remnant of creativity in their people since they think that it is always easier to control and manage a regimented workforce devoid of even elementary traces of creativity.

So, is managing creativity and creative people a messy affair? On the surface it seems so. This is simply because we generally have a vague idea of what drives, inspires and really sustains creativity?

Creativity is not about wearing hair long or wearing weird clothes, singing strange tunes, coming to office late and being rude to bosses for no apparent reasons. These things hardly make anyone creative or help anyone become a more creative person.

Actually, things like “being attentive and aware”, “sensitive”, “passionate”, “concerned”, “committed” and above all “inventive” just might be the necessary ingredients to drive, inspire and sustain creativity.


Though there are many ways of describing and defining creativity what I like best is – “creativity is the expression of one’s understanding and expression of oneself” – deeper the understanding better the expression of creativity.

When we look at creativity in this manner it is obvious that we are all creative though the expression and its fidelity might vary to a great extent. Clearly, some are simply better than others.

Further, if creativity may be thought about as a process, then the inputs and the clarity of understanding of ourselves are more valuable elements of the system than the outputs that the process anyway consistently churns out (remember the uncountable hours we spent in organization meeting, discussing and brainstorming to solve complex problems).

In these days of economic depressions, organizations can really do themselves a huge favor if only they pay more attention to facilitating such inputs to people rather than get overtly worried about control and management by conformity.

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.

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?

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 .. 2

Root cause analysis
Root cause analysis

Fair to say I love Root Cause Analysis (RCA) and I deeply hate RCA.

That doesn’t look very helpful or fair enough. Isn’t it?

I shall try to explain my love and my concern..

For a simple and complicated systems RCA is rather helpful. No doubt about it.

But for Complex Systems RCA is a complete disaster.

I try explaining this but people seem to have a hard time getting it. They keep trying to apply RCA to all sorts of applications such as a) Project Management b) Equipment Failures in industries c) Human-Machine interfaces and a host of other things..

The funny thing is they consistently fail to explain why something happens in a complex system through RCA but then they consistently keep trying to apply the famous Why-Why…technique till they “ungorgeously” drop dead.

And why not?

I suspect we internally feel very insecure if we fail to find the cause or predict an outcome with any degree of accuracy when something either goes wrong or goes for a booming success. Otherwise we seem to feel insulted. Our intellect is insulted. Our imagined self esteem gets a shock of its life. Our education, training and mastery over many subjects are questioned…..

So why does RCA which works so well for simple and complicated systems miserably fail when dealing with complex systems or for that matter complex adaptive systems?

The distinction is deceptively simple —

1) For simple and complicated systems — events that happen are due to resultants.

2) For complex systems — events that happen are due to emergence

And there is a gulf of difference between resultant and emergence. Resultants lead to specific outcomes. Whereas Emergent events are simply depictions of the varied outcomes that might arise from a set of interactions. How? In a complex system change in quantity of any parameter within a set of interactions changes the emergent behavior of the system.

Therefore, for simple and complicated systems we ask ‘Why‘. That is we predict the outcome as accurately as possible. Perfectly simple.

But for a complex system we ask ‘How might this happen?’ In this case we anticipate or foresee possible outcomes (in the short term only). We must have patience and courage to Explain. Might go wrong at times. Perfectly simple too.

So I become cautious and worried whenever I hear anything close to these:–

a) Ah! Here is the root cause of….

b) This is a simple case of human neglect…

c) Oh! This is a plain case of lubrication failure…

d) Fatigue is the reason for this failure…

e) The supplier was at fault for…

f) We don’t know how to manufacture or operate or construct…..

This is because more than 90% of events that keep happening around us are neither simple nor complicated. They are complex.

Do you share my caution or worry?

Want to read more on this?

Here are some related articles-



The cartoon is taken from Facebook page: Life is a Bitch:

Note: This is a part of the course on Rapidinnovation:

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?


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

Feeling/Thinking in Nemetics – A Challenge

Here is a challenge in Complexity Thinking as in Nemetics (2nd stage): –

How or by what method would we be able to figure out  or feel possible or approximate directions to the following questions around a complex adaptive system or a complex creative system — human being.

1. Around what time human beings or their ancestors were able to stand erect?

2. Around what time human beings started to cover their bodies or wore clothes or dresses?


Any complex adaptive/creative system has many elements in it which are interdependent and coexist and co-evolve along with other elements over time.  As one thing changes the other changes too. The trick is to feel the ecology and then think of elements that were present in the ecology of the human adaptive system right from the earliest times and have evolved along with human adaptation through the ages.



(note – such feeling/thinking is needed in the Engage stage of Nemetics, which comes after the Notice or Attention stage of Nemetics)

Self Organized Nemetic Environment for Learning (SONEL)

Have Fun & Learn
Mr. Shukla and I with the group of 28 who challenged Vibration Level 2 and Level 1 certification at PMI, Noida during last week of August 2012. 22 of these curious and brave souls successfully met the challenge.
Mr Shukla (from NTPC) is on my right and Mr. Anil Sahu is on my left. Both were my co-facilitators. As usual I am the shortest of the lot in dark green shirt.

Self Organized Nemetic Environment for Learning (SONEL) is a learning environment based on self inquiry of a person’s own problems helping one to instantly learn from those; supported by rich dialogs that build on each others ideas, questioning and peer learning aided by finding specific information from the internet.

Let me illustrate this through a practical example of how it is done.

The Story

NTPC (National Thermal Power Corporation) is the largest provider of power in India. They have a mission, which simply is to keep availability of their plants at the highest possible level. Since power plants like any other plant is a complex system it is not possible to plan out operation and maintenance activities in such a way to avoid sudden breakdowns and outages.

In order to do so they would have to base their actions on their understanding of the complexity or complex behavior of the plant and machinery. That is how we can work with any complex system, which creatively on their own keep changing their behavior. So the strategy is fairly simple — a) Understand the complex behavior of a complex system b) Spot an incipient failure growing or emerging c) Model that possible failure to determine what best can be done d) Take action to eliminate the failure or avoid it or reduce the risk of the failure to the minimum possible level e) Keep monitoring for the next emergence to appear.

In technical terms such a strategy is known as Condition Based Maintenance. That is the traditional name.  I don’t see any reason as to why it should not be called Complexity Based Management since the principles just remain the same. The same principles can be used over and over again to understand, decide and act in any complex environment, such as in organizations. I shall leave that discussion for some other day.

Coming back to our story one of the vital tools to implement such a strategy of knowing things in the now is Vibration Analysis, a very powerful tool since all machines and complex systems are dynamic and therefore vibrate in some way or the other. The tools and instruments of vibration analysis faithfully record the amount and the nature of vibration in various ways. However, only a human being can make sense of such records and form an understanding of what is going on. But the depth of understanding would vary from person to person depending on a person’s feel for complexity and understanding of the subject of vibration.

NTPC realized this very vital gap of enhancing human understanding in the whole strategy. This gap can only be filled through insightful and in-depth learning from a person’s intimate understanding of complexity informed by his/her practice. Hence they decided to expose as many people as possible to the subject of vibration analysis where they learn and apply their understanding to maintain a healthy level of plant availability.

So, every year NTPC organizes this all India SONEL event in Vibration Analysis Level 3 to Level 1 for their plants scattered all over the county. A suitability criteria is given, which basically sums up as ‘Are you practicing vibration analysis and complexity on the field?”. Against this criterion practitioners who are interested in challenging Level 3, 2 or 1 certification apply. Along with their applications they submit two case studies reflecting their failed struggle to understand the nature of complexity. The more intense their struggle as reflected in their cases better are their chances of being selected for the course. The idea is one can really learn very deeply from his/her cases and struggles. Only 25 students are admitted per batch.

Each candidate admitted to the course then submits at least two more case studies 15 days before the start of the course. From all the submitted cases (around 100 in number) the course work is carefully designed with a collation of appropriate questions to be used as triggers for live on-going classroom dialogs and peer learning.

Then a 4 day live workshop is conducted in the style of a concert. The only difference being that participants perform while I and my co-facilitators take up the role of the conductor. One by one participants lay bare their individual cases to the entire class with the hope of seeking a resolution of their problems.  They learn from the questions that are posed to them.  They learn from the ideas of others. They learn from the successes and mistakes of other practitioners who are their peers. Through the interactive sessions facilitators spot more weakness in the crowd and note them down for addressing them later. After some time through the rich dialogs the participants learn whatever they want to learn and decide on their actions customized to the problems they faced.  The atmosphere of serious play is constantly charged up further through more questions, interjections, explanations, suggestions and guidelines if any.

The questions for the final exam are formulated from whatever is happening live in the class and from the case studies.  The participants challenge the test by the end of the 4th day. Each participant is allowed to carry one A4 sheet of paper with their own notes, whatever they like to the examination hall. Obviously, I haven’t seen them using those notes since the final test is not a test of their memory, which I know they have in plenty. They get certificates as per the bands they achieve. For example, 90% and above get Level 3, 70 to 90% get Level 2 and 50 to 70% get Level 1. Below 50% get a certificate of participation.

Next year those in Level 2 come back to challenge Level 3 and those with Level 1 or with a participation certificate come back to challenge Level 2 or Level 3 along with a new batch of fresh candidates.  They can participate in the events twice a year. There is another interesting thing that happens on the side. A two hour video conferencing is conducted for all the plants for those participants who have already secured Level 3 certification.  It is a type of a feedback session trying to gauge as to how well the participants are doing. In most cases I found that their peers talk highly about them and their subsequent achievements in practice. That makes me proud indeed. Why not? Continued Peer Recognition is the best certificate in the world.

  1. Self organized learning is learning on ‘my problems‘ through interactions with other human beings (including those on the net).
  2. The role of the facilitator is important. Conducting such a Self Organized Nemetic Environment for Learning (SONEL) is a tough job. One has to be on his/her toes through all sessions — in sharply creative and attentive mode.
  3. The quality of the interactions is important to achieve the learning goals. That is the job of the facilitators.
  4. Three facilitators work in tandem. One facilitator is drawn from the participating organization and one more knowledgeable facilitator is drawn at random from any other organization. In this case Mr. Shukla is from NTPC and Mr. A Sahu is from Birla Copper (shown in the opening photo)
  5. The learning follows the non-linear Nemetic process of a) Noticing/discerning changes b) Engaging with interdependent constraints c) Mull about the deep and rich interactions d) Exchange value through adaptation, re-design, actions etc. (NEME)
  6. This time around in Dec I shall tweak SONEL a bit more. The participants would set their own questions for an interim exam.
  7. This certification is now widely sought in India and employers are happy to see this certification for employment.
Vibration Level 2 certification.

Frontiers & Challenges of Complexity Discipline

I think that the frontiers and challenges of complexity as a discipline has been very well highlighted by Steven Strogatz in his book Sync on page 287 (Ref 1). Strogatz is a mathematician whom I admire for his intuitive approach to maths, which I believe might make maths popular amongst the masses.

I quote from his book the two relevant paragraphs, which I feel are important.

“…. I don’t want to leave you with a false impression. Sync is just a small part of a much larger body of thought. It is by no means the only approach to the study of complex systems. The chemist Ilya Prigogine and his colleagues feel that the key to unlocking the mysteries of self organization lies in a deeper understanding of thermodynamics. They see the emergence of order as a victories uphill battle against entropy, as a complex system feeds itself on energy flowing in from the environment. The community of physicists interested in pattern formation see fluid mechanics as its paradigm, where the rolling of a turbulent fluid gives rise intermittently gives birth to coherent structures like helices and plumes, rather than degenerating into a bland, uniform smear. The physicist Hermann Haken and his colleagues view the world as a laser, with randomness and positive feedback conspiring to produce the organized forms that occur all around us. Researchers at Santa Fe Institute are struck by the ubiquity of evolution through natural selection, not only in biological population, but in immune systems, economics, and stock markets. Others conceive the universe to be a giant computer, running a cryptic program whose discovery would constitute the end of science.

But for now, these are mostly pipe dreams. We’re still waiting for a major breakthrough in understanding, and it could be a long time in coming. I think we may be missing the conceptual equivalent of calculus, a way of seeing the consequences of the myriad interactions that define a complex system. It could even be that this ultracalculus, if it were handed to us, would be forever beyond human comprehension. We just don’t know.”

That is quite a grim reminder that not only reveals a quick glimpse of the unchallenged frontiers of the discipline of Complexity but also throws at us a challenge at the same time.

But I think a very likely discipline that has been missed out or the practitioners of the discipline hasn’t yet explicitly joined in is Engineering, especially the wing that practically deals with non-linear dynamics — the discipline called Condition Monitoring. Having my roots in that discipline I think that the new maths of complexity is mostly likely to be worked out or generated from this field. It is not that the maths doesn’t exist. One thing that is quite mature in the field of Condition Monitoring is  “prediction of emergence in complex systems”. That is how the field got its name. The prediction, as it should be, is always done in the short term taking into account the ensemble as a whole. Admittedly, most of the maths is graphical. But I don’t see any issue with the graphical maths since it does the job so well indeed, which is a) short term prediction b) understanding and interpreting the interactions at play at any given moment along with interpretation of how new orders are created.

The most interesting thing is that it does not stick to one world-view as most streams, outlined above, have done. It sees the world from multiple perspectives – both Newtonian and Non-Newtonian — waves & vibrations, dynamics and non-linearity, sync & resonance, thermodynamics & fields, flow & fluid mechanics, randomness and feedback, wear and electrons, chaos and evolution, determinism and probabilistic. Undoubtedly it is tall order. A true master in this field (though such masters are difficult to spot since they mostly live like recluses) can easily flow from one perspective to another or hold multiple perspectives together at the same time while observing a phenomenon (I am referring to only one rather secretive school; with a handful of practitioners having not more than 10 masters). There are no inhibitions or ideological hold ups.  That is where the masters draw their strength from.

But the maths that might be created wouldn’t look the same as we are used to. And it must not be so. It must not be ‘calculus’ that can predict the past, present and future for all times to come from a single observation. But at the same time it must not overlook the role of ‘differences’ and ‘integration’. It also must not be so universal that it can be applied to all or many frames of references. It must not look like ‘laws’. However, at the same time it must be simple enough for people to make sense to gain insights that would help them to model, adapt, innovate, re-design and predict. What more is needed?

The good news is that Nemetics (a branch-out from that secretive school of practice) research is rather close to creating that practical maths. At least, as of now, it can predict an emergence in the short term for the most complex of all cases close to 85% accuracy. For relatively simpler cases of complex systems the success of prediction is now close to 100%. And that is quite an achievement.

It is based on interactions of three vital components — Energy, Damping and Constraints, without which no real life complex system or transforming process can exist.


1. Sync, Steven Strogatz, Penguin Books, 2003