# Science and Art of Problem Solving

Science and art are the head and tail of the same coin when it comes to solving real life problems.

With science, we search for similarities of things that appear different. We focus on linearities and direct connections between causes and effects.

With art, we search for differences among things that are apparently similar. We focus on non-linearities and intricate connections between causes and effects.

To solve real life problems we need to be skilled in both science and art. This is because situation governing every problem is unique. That is why most real life problems are complex in nature.

Seen realistically, solving real problems puts science and art together again. Actually, they were never born apart.

Taken together; they lead us to truth.

Something more on Science and Art:
http://www.artic.edu/aic/education/sciarttech/2a1.html

# Analyzing Non-Linear Dynamical Systems

Excerpts from my forthcoming book entitled:

Solving Complex Problems through Vibration Analysis.

An Introduction to understanding Non-Linear Dynamical Systems.

Notice I wrote ‘solving problems through vibration analysis.’ I did not write the usual way of describing, such as: ‘Solving Vibration Problems.’ That way of saying makes no sense since we are essentially using a tool, namely vibration analysis, to solve intricate and complex problems. Similarly, to term some problem as a ‘vibration problem’ makes little sense since any machine, in fact, anything in the entire universe, vibrates. So, is the entire universe a problem? Certainly not.

An Approach.

Since we would primarily deal with complex problems of machines it would be appropriate to understand a possible approach to deal with such problems.

To quote Feynmen, it is simply for the pleasure of finding out things. He wrote a book with the similar name: ‘The Pleasure of Finding Things Out.’

On pages 103 and 104 of this book (published by Penguin), he writes the following: (I am quoting only relevant portions for our purpose of investigating problems with a scientific attitude. Moreover, I shall put my notes for emphasis of certain points by using third brackets).

The other aspects of science that are important and that have some problem of a relation to society, beside the application and actual facts that are discovered, are the ideas and techniques of scientific investigation: the means, if you will. Because I think that it is hard to understand why the discovery of these means, which seem so self-evident and obvious, weren’t discovered earlier; simple ideas- which, if you just try them, you see what happens and so forth. ….

The first is a the matter of judging evidence – well, the first thing really is, before you begin you must not know the answer. [So important. If we start off with the attitude that the answer is known, we are doomed right from the beginning. The real answer would delude us and we would either keep going in circles or be put on hold till we let go of our pre-formed judgements and opinions or what we think to be an answer. A very difficult practice.] So you begin by being uncertain as to what the answer is. This is very, very important, so important that I would like to delay that aspect and talk about that still further along in my speech. The question of doubt and uncertainty is what is necessary to begin; for if you already know the answer there is no need to gather any evidence about it. [Worth remembering that vibration records in various forms taken in different directions and modes from machines act as evidence only of the how the machine is behaving and how one part or element of a machine interacts with another.] Well being uncertain, the next thing is to look for evidence, and the scientific method is to begin with trials. But another way and a very important one that should not be neglected and that is very vital is to put together ideas to try to enforce a logical consistency among the various things that you know. It is a very valuable thing to try to connect this, what you know, with that, that you know, and try to find out if they are consistent. And the more activity in the direction, the better it is.

After we look for the evidence we have to judge the evidence. There are the usual rules about the judging the evidence; it’s not right to pick only what you like, but to take all of the evidence, to try to maintain some objectivity about the thing — enough to keep the thing going — not to ultimately depend upon authority. Authority may be a hint as to what the truth is, but is not the source of information. As long as it’s possible, we should disregard authority whenever the observations disagree with it. And finally the recording of the results should be done in a disinterested way. That’s a very funny phrase which always bothers me — because it means that after the guy’s all done with the thing, he doesn’t give a darn about the results, but that isn’t the point. Disinterest here means that they are not reported in such a way as to try to influence the reader into an idea that’s different than what the evidence indicates. [This approach and scientific movement was started by Galileo. He demonstrated the power of his ways of looking at things].

Just to give a practical feel of how we come to understand complex problems through vibration analysis, let me show you one of my reports as an example of the application of the scientific approach and attitude, we just discussed, and elaborate further on that with reference to understanding complex systems.

Final Report of Investigation into Problems for Autogenous Mill.

Client: yyy, xxxz (I have removed the name of the client for ethical reasons)

Consultant: Reliability Management Consultant Pvt. Ltd (RMCPL)

Dates of Investigation: 20th and 21st May 2014

Investigators from RMCPL: a) Dibyendu De, Director b) Animesh Ray, Deputy Director

Problem description in brief:

The Autogenous Mill, is a roller crusher that is loosely supported on four rollers, which in turn are supported on concrete columns, connected to adjacent columns by beams; suffers from high and rather abnormal vibrations that possibly results in frequent failures of Roller Shaft no. 4, which remains unexplained for the past three years.

Methodology of the Study:

In an equipment, (let us call it a system) failures are bound to happen. These are engineering problems or failures that affect Reliability, Availability and Performance of any manufacturing system. At times a gear-box cracks. At times, a shaft fails. Yet at another time, chains fail. All happening in the same system. Seen at the micro level, failures not only cost a lot of money and effort but also leads to wastage of energy, money and material. Seen from the macro level, failures do eat up a lot of national wealth and human happiness.

The question is how do we respond to such failures? The usual way is to treat such failures independently and try to arrive at an independent root cause for each failure. And this process can go on forever. Quite frustrating indeed without a desirable state of operation in sight.

The other way is to consider all failures simultaneously. In order to do so, each failure has to be related to one another and not treated independently. . The guiding principle is — if something happens in a system it must affect something else.

However, to do so, we need to understand the dynamics of the machine — i.e. how movement of one part tells upon the movement of another part.

So, the process of understanding failures would be something like this:

1. Notice the movement of each part.

2. Notice how movement of one part affects other parts or elements in the system.

3. Determine the forces that act and estimate their nature, direction and magnitude.

4. Determine the interactions and the design criteria and limits for such interactions.

5. Try to relate the dynamics of the system to the failures that are observed.

6. Build the hypothesis that relates all failures based on our findings.

7. Look for evidences through data obtained from dynamic analysis tools such as vibration analysis, thermal imaging, wear debris analysis, lubricant analysis etc.

8. Relate the evidences to the hypothesis and make it more cogent and logically foolproof so that the hypothesis turns a theory that not only explains how the failures happen so as to increase the time span of failure free operation but also provide us tools to predict the conditions under which the failures take place (just before they take place) and the degree of maintenance and care we need to take in the future.

9. We now have, in a relatively short time, a great explanation of all the failures happening in the system along with the predictive and maintenance tools.

10. With the theory in place, it is easy to design the countermeasures (minimal interventions) that would not only prevent the failures but also prolong the working life of the system.

11. The context specific theory, thus obtained, would also help us design the necessary monitoring activities — a) to catch changes (of conditions) in the system, in time, to prevent a breakdown b) improve upon the maintenance plan.

12. By doing this we have now designed in-built resilience and flexibility in the complex system that we are dealing with.

Things that were investigated:

1. Interviews with plant personnel at different levels

2. Physical Inspection of the system at site.

3. Inspection of the vibration records as available at site. (overall displacement values and displacement values with phase readings)

4. Inspection of vibration data, at site, as per additional recommendation by RMCPL (vibration values of displacement, velocity, acceleration in different directions, FFT spectrum, Time waveform)

5. Deeper Investigation and analysis into the problems. The idea is to develop a contextual theory that explains all the failures.

6. Explanation and discussion of the findings and analysis with concerned engineers and managers engaged with the issue.

Observations: (the main points)

1. The failure of the shaft of roller no. 4 is a combination of fatigue failure, tensile loading and energy absorption through repeated impacts forces (impulses).

2. The system’s natural frequency is quite close to the driving frequency.

3. The system is insufficiently damped. In fact, the system is negatively damped.

4. The roller crusher, loosely placed on rollers, automatically moves towards the discharge end

5. Presence of self sustaining vibration seen.

6. The system is subjected to rocking and twisting motion, which is pronounced at roller 4 and motor and gear-box foundation.

7. The system runs in under-loaded condition. That is — it is operated much below its designed capacity.

8. Total frictional force is inadequate to constrain motion in undesirable direction.

Recommended Solutions (viable):

Note: All solutions, whether placed in Short Term, Mid Term or Long Term, are important. In total 18 suggested solutions are placed for consideration in this interim draft report.

Short Term Solutions: (those actions that might be immediately carried out)

1. Change the driving chain along with the pair of sprockets. The looseness of the chain was causing the impacts and tension force that pulls the system towards one direction. Note: It does not help improve the system by either changing the chain or the sprockets.

2. Stop greasing the driving chain as presently done. It not only accelerates wear but also induces slip-stick phenomenon. Keep the chain clear of dust. Spray lubricate the roller joints once a week.

3. Improve the dust collection system and make it highly efficient.

4. Remove the shims placed to level the system in static condition. It makes the situation worse in dynamic condition.

5. Change bearing grease from EP 2 grade to EP 3 grade.

6. Place graphite blocks on friction area of supporting rollers instead of placing those on the Mill tyre.

7. Replace the fluid coupling by non-linear rubber coupling. Design if necessary. This would stop the excitation of the natural frequency of the system and the system would relatively unaffected by the impact forces operating under negative damping.

Mid Term Solutions: (Actions that might be carried out within next 15 to 30 days).

1. Change shaft material of roller from EN 24 forged to EN 36 B (forged).

2. Keep surface roughness of the contact surface within +/- 6 microns (CLA – Center Line Average). This is because endurance limit of high strength steels turns out to be no higher than that of ordinary steels if machined roughly.

3. Chamfer the shoulder areas of the shaft to a fillet radius of R15.

4. Strain harden the above fillet area to raise the load carrying capacity of shafts by 50 – 60%

5. Check for proper interference fit. Shaft Tolerance = n6. Housing bore tolerance = K7 (recommended).

6. Restore the original number of flights within the Mill. And maintain them from time to time so as not to run the mill with lesser number of flights than originally designed.

7. If possible, try to run the mill at higher loads than present.

Long Term Solutions (those actions that might be taken within the next 180 days)

1. Incorporate Duplex Chain drive, silent type.

2. Cross brace the civil structure that includes the four concrete pillars/columns and the independent column on which the gear box rests. The outcome should be such that deflection in any direction should not be more than 50 microns, measured at the top position

of the pillars.

3. Get help of foundation (soil mechanics) and structural specialist as required, to check the adequacy or integrity of soil foundation and civil structure.

4. Ground off the excess tire material from the roller crusher.

Records: (of vibration data)

…..

# 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.

# On Learning and Wisdom in Complex times

Clearly the top question in today’s world of uncertainty and randomness is ‘How do we learn fast enough to gain understanding, insights and wisdom to negotiate  our complex world and act in a ‘blink’?

To start our exploration we might start with the main schools of thoughts. However, be warned that this is just a whistle-stop tour illustrated by my not too well-formed ‘pencil sketches’ of the great rivers of human thoughts on learning. And I, for purely practical purposes, would stick to the Western flow of thoughts on learning since this has indeed come to dominate our present way of thinking about learning all across the world. For want of space and to keep this blog post within reasonable limits (that is not to bore people to death) I would have to keep out for the time being some of the great minds whose thoughts on thinking and learning are as important and significant as of those whom I mention. For example, I left out notable mathematicians and some philosophers like Heraclitus, Hegel, Marx, Kant and others, whom I would like to cover in later blog posts if my time and energy permit to do so.

I would start with Plato. He was a firm believer of the ‘big picture’ as we find him in his book ‘The Republic‘.  To him the ‘big picture’ is some sort of idealized form constructed by the human mind. So, he thought that all of us must go as close as possible to an ideal or an idealized form or an idealized idea. In order to do so we must know something beforehand to understand a big picture to build an idealized picture of the reality (Plato’s Theory of Form).  With that he defined the role of philosophers who must impart the knowledge of forms (or ideas), which is ‘real knowledge’ to the average person who he thought is deluded by changes sensed by his senses (allegory of the cave). In short, he was a suspicious of the knowledge gained from experiences.

However, his student Aristotle, thought differently. He thought that it was best to work up from basic facts and observations to form general principles. I would go on a limb to say that it was the start of the modern scientific way of viewing and learning more about the physical world through direct observation to form general principles that could be applied with repeatable accuracy. Hence such principles turned out to be a set of axiomatic set of principles that might be used to view the world around us.

Needless to say that these two schools of thought clashed with each other in our effort to find a way to learn better. For a long time, others who came behind Plato and Aristotle chose either of these schools as their learning framework.

For example, Descartes, toeing Plato’s line of thinking, tried to create a body of knowledge that would stay independent of experience. His idea was to make knowledge certain without tolerating ambiguity. That was ‘rationalism’ or better known as the domain of ‘bounded rationality’. It was useful at that time of human learning. The idea was to get away from the iron grip of churches who clung to the Platonic view. No wonder the early universities in Europe were founded by churches. Cambridge was possibly the first exception. Under the influence of the King it moved away from teaching canonical laws and Platonic school of learning to classics and mathematics founded on Aristotelian and Euclidean way of thinking.

It quietly slipped into the Aristotle’s way of scientific thinking backed by Euclidean geometry which exploded to a highly enriched state with the arrival of Newton. No wonder space was rigid in Newtonian science (Euclidean influence), which was revised by another great mind, Einstein. But that happened years later.

But what happened immediately was the meteoric rise of the crown. The king was able to rob political power from the church based on science and its inventions triggered by Newton and his contemporaries.

However, this was soon followed by the fecund period of Scottish Enlightenment led by personalities like David Hume and Adam Smith.  Perhaps it is not a coincidence to find Hume, Smith and Watt working in the same Edinburgh University as contemporaries. It was, as if, their ideas and thought processes rubbed off on each other.

Hume’s thinking differed from Plato and Aristotle. It was, so to say, a much more practical approach to things. He stressed upon the idea that we can only know what we can experience. So this marked a radical departure in the domain of ‘learning’ away from the world of Plato, Aristotle and Euclid.

I think it was “acceptance of the world as it is” ; not what it seemed through the ‘Theory of forms’ or Aristotle’s seeing the world through a set of a priori ‘axiomatic principles’. And this way of thinking was not without great impact. Fair to say that it ushered the scientific age paving the way for an astounding leap in engineering and science birthing industrial revolution that provided a welcome relief to the masses from the church’s feudalistic way of living off the land, which was ruthless, to say the least. This period also saw the coming of Darwin and Maxwell revolutionizing their fields of study and work, which had deeper implications in the modern age in terms of genetics, relativity and the birth of quantum mechanics.

However, that meant that science and scientists soon started ruling our thought processes and learning methods. The age of logic returned. And it had its profound imprints in the way we learned in schools, colleges and industries. There was a scientific temper to everything.

But scientists were also developing their own ‘blind spots’. The most famous example that stood out was that of Einstein coming from the Platonic school of thought. He was so enamored by his own idealized ‘big picture’ that he just could not see the new development of quantum mechanics coming. He simply refused to accept reality since it simply refused to match his ‘idealized form’ of reality. Hence the next phase of development in science was admirably led by Bohr and Heisenberg.

However, it was still the age of logic and it found its way in almost every sphere of human activity from mathematics to economic to management. The attempt was to build a system or a thought process through logic and logic alone.

But logic had its limits proved by Godel’s famous incompleteness theorems. So it wasn’t uncommon to find systems and models developed and built on logic often flounder and fail. Failure through contradictions was evidently built into logic.

All this time the field of uncertainty was developing in fits and starts well hidden from public view by people like Poincaré whose work was later admirably taken up by people like Edward Lorenz and Mandelbrot. This and the work of mathematicians like Markov, Weibull, Kolmogorov and others who firmly established the field of uncertainty, sensitive dependency on initial conditions and complexity. This was further reinforced by behavioral economists like Daniel Kahneman and others. Meanwhile, as Aristotelian logic was re-framed by Fisher and others into rigorous statistical inferences; Bayes and his disciples (most notable in modern times is Nate Silver) extended Hume’s philosophy of ‘experiential learning’ to mathematical beauties.

The field of complexity, ambiguity and adaptation soon began to dominate more practical fields of computers, genetics, biology, engineering, physics, mathematics and even management. From the age of rigid rationality, almost unerring logic & iron clad certainty we suddenly find ourselves in the age of irrationality, uncertainty, ambiguity and complexity.

That is a peculiar predicament. Only logic would no longer be useful. Those who would only like to stick to it would fail too often and fail too soon. Construction of the ‘big picture’ often leaves us in the quandary about how to act. Axiomatic principles are often more questioned than accepted as given. Acting only on experience might prove fatal and inadequate. Models are often proved wrong. And so on…

So what to do?

Undoubtedly this is a difficult age to live. But one thing is certain. There are no longer any fixed ways of looking at our world.

Fortunately, there is one common way of learning that runs through everything. This might be described as follows:

a) Pay attention (Notice)

b) Engage to learn (Engage)

c) Mull the choices we have (Mull).

d) Exchange our learning in ways others might get it (Exchange).

That is NEME, the basis of the discipline of Nemetics.

The discipline of Nemetics tends to answer three vital questions that help in this age of complexity.

a) What is going on? — NEME

b) What does it mean? – Design Kata

c) What might we do about it? – Rapidinnovation

Possibly that leads us to give us a way of living in these complex and turbulent times.

a) Strategy — Follow your aspirations but check the facts and re-purpose if need be.  (Feel)

b) Take failures of any system as the starting point of learning and leadership. Learn to face failures and fears through improvisation and innovation. (Think)

c) Learning is a personal responsibility. It is about personal mastery. Collaboratively learn through self-study, observations, thoughts of others, interactions with peers and mentors and feedback from your own work since learning, understanding and gaining insights might not possibly happen in one stroke.  In other words keep learning and improvising to pavé the way to arrive at wisdom. (Innovate through improvising).

Luckily all that can happen in a blink through perseverance and patience aided by the power of emergent complexity of our 800 MB human genome in a self organizing way that can beat the best super computer of the world.

That is what we can really rely on.

# Design is about Changing the World; Not Selling Stuff

It generally feels so good to see a talented person getting interested in the work one does.

I felt the same when a pal of mine shared a paper entitled, ‘The Osmotic Bubble: Design Synchronicity: Unconscious Learning Through Osmosis: How Emotions and Intuition Empower Us to Imagine“, written by Niberca Polo

In Michael Josefowicz’s (her teacher in design) words:

“She teaches design as a praxis. She is the one who designed the Digit Bcorp website and the logos for Digit, Arrival City News and ACPress.  She is also a latina with deep roots in Dominican Republic with an understanding that design is about changing the world. Not selling stuff.  I have worked with +niberca lluberes over the course of many years since she was my student at Parsons. Awesome smart, gr8 dna, and a Latina spirit who has absolutely no time for bullshit. :-)”

Now the relevant snippet from her wonderful paper: —

Quote

The International Nemetics Institute (TINI’s), in India is doing very interesting work on what they call Emotional Entrepreneurship, and the relationship between “feel+think+design”. “Nemetics (Notice, Engage, Mull, Exchange, Train/educate) is a biomimicry model of information transfer” (https://rgbwaves.wordpress.com/institute/).

The term neme—sensori stimuli—is used to combine memes and genes in the service of understanding complex systems—where art and design are considered complex systems in dynamic interaction within networks (Josefowicz, 11.18.2012). When stimulating the Bilateral Brain in a learning environment that fosters intuition through sensory experience and emotions (nemes) design students will be able to learn how to design intuitively, and acquire tacit—unspoken, implied—knowledge, that can be archived in the long-term (implicit) memory through a process of unconscious (implicit) learning. Johnson in Emergence (2012) describes learning in a cellular scale as the iteration of circuits (neurons) where “memory creates a mental vocabulary” (pg. 133). Tacit knowledge is the source of our intuition—our gut feeling (Gigerenzer 2007)—and our capacity to adapt to new environments and situations, and evolve as one with technology and nature (Reber 1993).

Unquote

# 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. )

# Beautiful story of Challenges and Adaptations in Complexity

Here is a beautiful story of a Complex Creative System. Venice is beautiful. But what lies hidden is still more beautiful. The constantly unfolding story of  interactions between the sea, weather, people, houses, ideas and actions to adapt to changing situations would delight and inspire anyone to live better. The city and its people are constantly creating and re-creating their destiny in such clever ingenious ways. It is a fascinating but continuing story that is being told for centuries.

That is what complexity is all about.

Complexity is always beautiful as it is infinitely creative. It constantly creates new situations and therefore demands us to be aware of what is happening all around compelling us to be insanely creative too. Else we would soon be out of step with the system’s creativity. Going out of sync with system’s creativity spells disaster. Systems would never listen to us; we need to listen to systems.

However, the fundamental process of being and remaining creative is to follow the principles of 1) Design/Redesign 2) Maintain 3) Destroy what is unnecessary.

There is no harm embracing complexity. In fact, that is precisely the story of our human survival through intelligent adaptation. Nothing more. Nothing less.

# What Lovers of Complexity have in Common?

The discipline of Complexity is extremely cross disciplinary in nature.

However, the lovers of Complexity science share some common traits, which are:

a) A feeling for Movement. Usually such a feeling is embodied in the person developed through moment to moment awareness by paying attention.

b) A feeling for Flow. Usually they have an intuitive feel for velocity (speed), position and nature (divergence or convergence or incompressibility). This is also embodied by the person which is usually visible through her/his actions, behaviour, choices and general responses to a situation in hand.  Such a person does not hold onto concepts and ideas very rigidly.

c) A intuitive understanding of Patterns, Phases, Relations, Interconnectedness, Symmetries, Breakage of Symmetries, Amplification and Resonance.

d) A great feel for Non-linearity along with their readiness to deep dive to ‘see’ the invisible interconnected dependence of several non-linear phenomena at play, which often happens rather silently.

# Great Lessons from Traffic Jams! Are You Serious?

Summary

Recently, I have been playing with a Traffic model (basic) on Netlogo and thinking as to what we might learn from such a self organizing event in order to adapt and survive in a better way. In this post, I would like to explore whether the lessons of adaptation gained from this simple model can be applied to a broad base of applications covering different fields.

A general screenshot of the game/model is given below.

What I want to do is to show how the “RGBwaves” concept, the central theme of Nemetics (1), is applied to obtain deeper understanding of patterns of the events (Red Wave). Then I would like to examine the reason that drives such behavior (Green Wave). Having done that I would then like to explore the nature of the collective intelligence (Blue Wave) and how to change that for better adaptation in complex environments. Once done, we would then try to see whether the insights along with the wisdom this model (2) offers can be usefully applied to different fields of study in some practical manner. Along with this I would also explore what precisely needs to be changed or adapted. In short, we would like to explore enough to get to the bottom of things enabling effective adaptation.

It might be useful to notice that the R (Red), G (Green) & B (Blue) waves are interconnected and interdependent, i.e. one affects the other. Hence a change of modulation or adaptation in any wave can bring about effective changes or results. However, deciding about which wave(s) to tweak and by how much (adaptation) to bring about long-term on-going collective benefits would of course depend on the nature and character of the situation we face.

However, one need not be overwhelmed by the RGB waves and how to get around them. The whole concept of RGB waves is carefully embedded in the following NEME poster that starts with the observation of the R wave at the ‘Notice’ stage and gently leads us to the B wave at the ‘Exchange’ stage. We would then explore events through the NEME process (or the Nemetic process as it is called) as depicted below.

A Brief understanding of the Nemetic Process

In the Notice stage we actually observe the R wave – that is its ’emergence’, ‘movement’ and ‘shape’.

In the Engage stage we observe the objects and the fields they create through their interconnections and interdependence.

At the Mull stage we find the ‘rules’ that govern the behavior of the individual and the group. We understand the given constraints under which the whole system operates and then use models to explore through mathematical visualization different possibilities that emerge to help us get to the bottom of things. For this we look to the G wave.

Finally, the Exchange stage surfaces our crucial concern for adaptation that is done through tweaks of different waves or modulation of the waves through the process of redesign, which is iterative in nature. This is done against authentic constraints by involving the stakeholders involved to co-create solutions. Usually the best solutions are obtained by leveraging the most crucial part of a phenomenon where care is taken to keep both effort and costs to the bare minimum. The idea is to effectively resolve a given paradox to obtain on-going benefits.  This involves both exploration and exploitation of the B wave (collective intelligence). In case of our traffic jam problem the paradox is – the system always moves forward but the jam grows longer backwards. The same paradox can be extended to in the case of a developing economy — as an economy grows poverty grows too.

The Experiment

In this case we are trying to notice ‘traffic jam’ as emergence. The movement that we are noticing is the flow of traffic along with its speed, acceleration,  deceleration and the number of cars on the road. In terms of movement we also notice whether the movement approaches a fixed outcome (fixed attractor), or is the movement periodic (sync attractor) or quasi-periodic (a torus attractor) or something quite strange is happening (strange attractor). We also notice the shape, which in this case turns out to be a tube (3) containing the stretch of road (a highway), cars which are in some way coupled to one another through invisible strings. In this case we consider that this tube is not connected to other tubes operating in the same space and time. And the issue that we are about to observe is Spacio-Temporal in nature i.e. the event is playing out in both Space and Time, which makes things complex indeed calling for instant moment to moment adaptation.

Let us start the exploration through modeling. In this case we have a fixed number of cars running on the road against time. We can of course change the number of cars plying the road during the experiment to see whether that affects the outcome. Our car of interest is the red car. The rule is simple. If there is a clear stretch of road ahead we accelerate else we decelerate if the space between cars is insufficient to maintain speed. So, the driver takes the decision to either accelerate or decelerate depending on the situation.

In this experiment the variables I have tuned are: a) acceleration b) deceleration c) number of cars on the road. Given below is a representative sample set of the outcomes that might be just enough for the critical insight to pop out of the dark.

To go along with the experiment just think which of the above three factors (number of cars on the road, acceleration or deceleration) would cause a traffic jam or snarl. It can be more than one variable or parameter or a combination. Make a guess to start with.

I hope reflecting on these three representative samples (5) from the experiment (R waves) would help us see the underlying G (Green) and B (Blue) waves.

Interpretation of the findings

Incidentally, I saw the following:

1. Clearly the pattern is that of a ‘saw tooth’ wave (represented by the red graph, which shows the velocity of the red car at various points of time). Seeing such a ‘saw tooth’ wave instantly points us to the ‘quasi-periodic’ nature of the movement of traffic flow. This is also the characteristic signature of a ‘Torus Attractor’ (4), which is commonly found in reality. In simple terms it means that system after some time dances around lock stepped with this attractor and that is what keeps producing the events (R Wave) in a quasi periodic manner. The dance is at times periodic and at times non-periodic.

2.  The acceleration and deceleration, the apparent opposites, combine to produce the boundary and the effects that are visible. So the boundary that is created by these opposites create the authentic constraints under which the system operates.

3. By tweaking the values of the authentic constraints the quality of the system changes. For instance it becomes clear that bigger the gap between ‘acceleration’ and ‘deceleration’ more would be the number of traffic jams with longer lengths that extend backwards (both frequency and amplitude of the jams increase). For certain ratios of acceleration and deceleration the jams minimizes or maximizes (I have not shown those ratios here to keep the post to a manageable size). However it is interesting to note that as we increase the minimum group velocity the incidences of jam reduces and so does the length of jams. When that happens the quasi-periodic nature of the event suddenly transforms to that of a periodic nature. That is what determines the group behavior or the ‘collective intelligence’ (the B wave).

So far so good. But how do we use this insight of ‘raising the minimum group velocity’ for practical applications in different fields? Let us explore.

A) Engineering Technical

The same happens for a Blast Furnace of a Steel Works. Whether the furnace would ‘hang’ or ‘slip’ (big problems for Blast Furnace) worries both Blast Furnace engineers and management alike. With this insight when I looked at the phenomenon I saw the same pattern of jamming followed by sudden opening up. The system is caught by a Torus attractor characterized by a quasi periodic movement. And the most surprising thing I noticed was that when deceleration was high the undesirable event continued for longer duration. And this was specifically induced when the blast temperature was below a certain limit. There is more to this which I may be blogging about later. But as of now we seem to have cracked a century old unsolved problem opening up a way to predict and take appropriate actions as to when the Blast Furnace would ‘hang’ or ‘slip’.

B) Production and Organization systems

Production shops are designed for ‘flow’. But as is often the case, we see material (finished as well as work in progress) jamming up the shop floor, which signals the most important source of productivity and profitability problems. So far, managers have tried many methods. But every technique seem to work for some time and then suddenly stops giving results. Why is that? This is because manufacturing units often operate under ‘dancing landscape’. Now ‘dancing landscape’ is a term used in complexity science which means that the system is operating in an uncertain environment. How is that? Most business units operate in uncertain conditions. Orders vary, customers vary, the level of service demanded varies, order sizes vary, logistics vary etc. But the system that is designed to offer goods and services often remain invariant. So naturally the system and its operating environment quickly goes out of ‘sync’ and trouble ensues.

However the insight gained from Traffic flow might help. The simple idea would be to increase the average speed of the manufacturing unit and not to concentrate specifically on any particular point, station or unit. Increasing the group velocity is the determining factor in design and operation of a business unit keeping in ‘sync’ with the operating environment. As of now the improvement ideas have revolved around increasing the velocity of the slowest unit, not the group as a whole. Well managers might now think differently.

C) Flood, Stock Market, Education, Economic Health.

I am putting all of these issues together since the basic nature of these seemingly unrelated systems is exactly the same. Can we predict floods much in advance so that we can save lives in a more effective manner? We presently do it by measuring the rise of water level. But what happens if we measure the velocity of a river at different points along its length in order to detect a significant change in the deceleration component enabling us to predict a flood much in advance to save valuable lives? I believe it would be  possible to do so.

Similarly, no one has got the prediction of stock markets right. That is what appears to be the case till date. We gather trends in different ways. We trend the rise or fall of  stock indices. We trend the rise and fall of the major stocks. We do all that in order to predict, which at its best is at times awfully off the mark. What we might do instead (only a suggestion) is to trend the acceleration and deceleration of the various market indices to predict better. I believe this would be useful.

How do we know how well a class or a school is doing? Noticing the highest and the minimum scores might be a way. Measuring percentile scores might yet be another way. I am sure most schools employ some sort of ranking measures and scores to keep tab on their effectiveness of teaching and growth in the capability of students. But what happens if we choose to consider trending the ‘acceleration’ and ‘deceleration’ of such scores over a period of time (since education is also a dynamic process)? May be we would have a better understanding of how the students are faring and how well the school is doing. I believe this would provide a useful understanding in the field of education.

The same insight might be applicable to Economic forecasts and predictions as well. Presently we measure the strength of an economy by measuring its GDP, PPP, growth rates etc. What might happen if along with the usual measures we measure the ‘acceleration’ and ‘deceleration’ of the economy. My guess is that it would reveal the presence of the same Torus attractor, which would not only help us to predict which way the economy is going but also help us understand better the actions needed to make necessary improvements. This too I believe would be very effective.

Conclusion

However, it is fair to say that though I have personally experienced how this insight helped me improve Engineering, Production and Organizational systems I haven’t yet applied the insight to other potential fields I highlighted like Flood Management, Stock Market predictions, Educational effectiveness and Economic health. But I believe that it would work just as successfully as it worked in engineering, production and manufacturing systems. What gives me this confidence? Simply because the pattern these systems generate would be just the same since they would all be governed by the same type of attractor, ie. the Torus attractor.

Notes

1. Nemetics is a thought model that allows us to take maths and insights from a designed model and apply them to any complex adaptive system – psychology, organizational sociology, economics, engineering, design, system design, manufacturing systems, system reliability etc. (contributed by Michael Joseforwicz)

2. Use of models (physical, mental, software) in Nemetics is a way to train the eye and mind to see the world in its dynamical state using a nemetical lens to understand the imperfection in the performance so as to take actions to adapt effectively. The contention is that every aspect of the physical and social worlds are best framed as Complex Adaptive Systems. Given that fact a content independent way to see and analyze CAS through models and maths is invaluable for whatever you choose as your speciality. (contributed by Michael Joseforwicz)

3. Tube is taken as a topological shape (a very common shape) where action takes place through independent actors (drivers in this case) which distorts or de-shapes the tube over time through a dynamic process.

4. It is important to explain the concept of attractor in simple terms since this is possibly the first time Chaos and Complexity which are generally treated separately are being brought together by linking the concept of ‘attractor’ to group or network behavior as seen in complexity. There are four types of attractors as we know today. These are the following:

a) Fixed attractor – think of a damped oscillation, It tries to settle at one point only. Example: A married man falls in love with a lady who is not his wife. His mind is occupied 24×7 with this new lady. Nothing on earth can buzz him off the fixed point. He is hell bent on bedding this lady.

b) Oscillating Attractor: I call this a ‘sync’ attractor — an attractor which brings in harmony. It is a periodic wave like a sine wave or a cos wave. It is something similar to – get up in the morning, go to office, come back from office, have dinner, do facebook, go to bed, make love … and this continues day after day.. So periodic. Or it is like a woman’s menstrual period.

c) Torus attractor — the characteristic is ”quasi periodicity”. Simple idea — I like to take holidays two times a year. But it is not very periodic. In the first year it is in July and December. In the next year it is in June and Nov. Third year it is in May and October. Note the quasi periodicity . Appears random but it retains the frequency of twice a year.

d) Strange Attractor – Now we don’t know what exactly might happen. Such an attractor is unusual and appears all of a sudden or is not anticipated by us. Hence the term ‘strange’. Examples – heart attack, epilepsy, etc.

5. I chose the screen shots from my experiment to depict two things — 1) the saw tooth wave (the red graph) and how the group velocity of the cars changes while moving within the constraints defined by the upper and lower limit of group velocity determined by the acceleration and deceleration of the cars. Notice how the red graph varied as I changed the acceleration and deceleration of the group thus affecting the group dynamics.