Category Archives: Philosophy

The Confirmation Paradox:

albino raven

In today’s post I will be looking at Confirmation Paradox or Black Raven Paradox by Carl Hempel. Let’s suppose that you have never seen a raven in your life. You came across a raven one fine morning, and observe that it is black in color. Now that you have seen one, you suddenly start paying more attention and you start seeing ravens everywhere. Each time you see a raven, you observe that its color is black. Being the good scientist that you are, you come to a hypothesis – All ravens are black. This is also called induction, coming to a generalization from many specific observations.

Now you would like to confirm your hypothesis. You ask your good friend, Carl Hempel, to help. Carl suggests that you start looking at things around his house that are not black and not raven, like his red couch, the yellow tennis ball etc. He suggests that each of those observations support your hypothesis that all ravens are black. You are rightfully puzzled by this. This is the confirmation paradox. Carl Hempel was a German born philosopher who later immigrated to America.

Carl Hempel is correct with this claim. Let’s look at this further. All ravens are black can be restated as “Whatever is not black is not a raven”. This is a logical equivalence of your hypothesis. This would mean that if you observe something that is not black and is not a raven, it would support your hypothesis. Thus, if you observe a red couch, it is not black and it is also not a raven, therefore it supports your hypothesis that all ravens are black.

How do we come in terms with this? Surely, it does not make sense that a red couch supports the hypothesis that all ravens are black. The first point to note here is that one can never prove a hypothesis via induction. Induction requires the statement to be provided with a level of confidence or certainty. This would mean that the level of “support” that each observation makes depends upon the type of information gained from that observation.

I will explain this further with the concept of information from Claude Shannon’s viewpoint. Information is all around us. Where ever you look, you can get information. Claude Shannon quantified this in terms of entropy with the unit as a bit. He described this as the amount of surprise or reduction of uncertainty. Information is inversely proportional to probability of an event. The less probable an event is, the more information it contains. Let’s look at the schematic below:

ravens

The black triangle represents all the black ravens in our observable universe. The blue square represents all of the black things in our observable universe. The red circle represents all the things in the observable universe. Thus, the set of black ravens is a subset of all black things, which in turn is a subset of all things. From a probability standpoint, the probability of observing a black raven is much smaller than the probability of observing a black thing since there are proportionally a lot more black things in existence. Similarly, the probability of observing a non-black thing is much higher since there are lot more non-black things in existence. Thus, from an information standpoint, the information you get from observing a non-black thing that is not a raven is very very small. Logically, this observation does provide additional support, however, the information content is miniscule. Please note that, on the other hand, observing a black raven is also supporting the statement that all non-raven things are non-black.

When you first saw a black raven, you had no idea about such a thing existing. The information content of that observation was high. After you started observing more ravens, the information you got from each observation started diminishing. Even if you made 10,000 observations of black ravens, you cannot prove (100% confirm) that all ravens are black. This is the curse of induction. This is where Karl Popper comes in. Karl Popper, an Austrian-British philosopher, had the brilliant insight that good hypotheses should be falsifiable. We should try to look for observations that would fail our hypothesis. His insight was in the asymmetry of falsifiability. You may have 100,000 observations supporting your hypothesis. All you need is a single observation to fail it. The most popular example for this is the case of the black swan. The belief that all swans are white was discredited when black swans were discovered in Australia. To come back to the information analogy, the observation of a white raven has lot more information content that is powerful enough to break down your hypothesis since the occurrence of a white raven(albino) is very low in nature. Finding a white raven is quite rare and thus have the most information or surprise.

This also brings up the concept of Total Evidence. The concept of Total Evidence was put forth by Rudolf Carnap, a German born philosopher. He stated that in the application of inductive logic to a given knowledge situation, the total evidence available must be taken as basis for determining the degree of confirmation. Let’s say that as we learned more about ravens and other birds, we came across the concept of albinism in other animals and birds. This should make us challenge our hypothesis since we know that albinism can occur in nature, and thus it is not farfetched that it can occur in ravens as well. The concept of Total Evidence is interesting because even though it has the term “Total” in it, it is beckoning us to realize that we cannot ever have total information. It is a reminder for us to consider all possibilities and to understand where our mental models break down. In theory, one could also make whimsical statements such as “All unicorns are rainbow colored”, and say that the observation of a white shoe supports it based on the confirmation paradox. Total evidence in this case would require us to have made at least one observation of a rainbow colored unicorn.

I will finish with another paradox that is similar to the confirmation paradox – the 99-foot (feet) man paradox by Paul Berent. Up to this point, we have been looking at qualitative data (black versus not black, or raven versus not raven). Let’s say that you have a hypothesis that says all men are less than 100 feet. You surveyed over 100,000 men and found all of them to be less than 100 feet. One day you heard about a new circus company coming to town. Their main attraction is a 99-foot man. You go to see him in person and sure enough, he is 99 feet tall. Now, your hypothesis is still intact since the 99-foot man is technically less than 100 feet. However, this adds doubt to your mind. You realize that if there is a 99-foot man, then the occurrence of a 100-foot man is not farfetched. The paradox occurs since the observation of a 99-foot man strengthens your hypothesis, but at the same time it also weakens it.

Always keep on learning…

In case you missed it, my last post was Know Your Edges:

A “Complex” View of Quality:

Q

I am a Quality Manager by profession. Thus, I think about Quality a lot. How would one define “Quality”? A simple view of quality is – “conformance to requirements.” This simplistic view of quality lacks the complexity that it should have. This assumes that everything is static, the customer will always have the same requirements and will be happy if the specifications/requirements are met. Customer satisfaction is a complex thing. Customers are external to the plant manufacturing the widget. Thus, the plant will always lack the variety that the external world will impose on it. For example, lets look at a simple thing like a cellphone. Theoretically, the purpose of a cellphone used to be to allow the end user to make a phone call. Think of all the variety of requirements that the end user has for a cellphone these days – internet, camera, ability to play games, ability to use productivity apps, stopwatch, alarms, affordability etc. Additionally, the competition is always coming out with a newer, faster, and maybe cheaper cellphone. To paraphrase the red queen from Alice in Wonderland – the manufacturer has to do a lot of running to stay in the same place – to maintain the share of market.

320px-Alice_queen2

In this line of thinking, quality can be viewed as matching the complexity imposed by the consumer. There are two approaches in Quality that differs from the concept of just meeting the requirements.

1) Taguchi’s idea of quality:

Genichi Taguchi, a Japanese engineer and statistician, came up with the idea of a “loss function”. The main idea behind this is that anytime a product deviates from the target specification, the customer experiences a loss function. Every product dimensional specification has a tolerance for manufacturability. When all of the dimensions are near the target specification, the loss function is minimal resulting in a better customer experience. One of the best examples to explain this is from Sony. The story goes that Sony had two television manufacturing facilities, one in Japan and one in the USA. Both facilities used the same design specifications for television. Interestingly, the televisions manufactured in the USA facility had a lower satisfaction rating than the televisions manufactured in Japan. It was later found that the difference was in how the two facilities approached quality for the color density. The paradigm that the USA facility had was that as long as the color density was within the range, the product was acceptable, whereas, the Japanese facility made a point to meet the nominal value for the color density. Thus, the Japanese Sony televisions were deemed superior to the American Sony televisions.

TV

2) Kano’s idea of quality:

Noriaki Kano is another Japanese Quality Management pioneer who came up with the idea of the Kano model. The Kano model is a great way of looking at a characteristic from the point of the customer. The Kano model has two axes – customer satisfaction and feature implementation. The customer satisfaction goes from satisfied to dissatisfied, and the feature implementation goes from insufficient to sufficient. This two-dimensional arrangement leads to various categories of “quality” such as Attractive quality, One-dimensional quality, Must-be quality and Indifferent quality. Although there are more categories identified by Kano, I am looking at only the four categories identified above.

  • Attractive quality – this is something the customer would find attractive if it is present, and indifferent if it is absent. For example, let’s say that you went to get a car wash, and the store gave you free beverage and snack. You were not expecting this, and getting the free beverage and snack made the experience pleasant. If you were not aware of the free beverage and snack, you would not be dissatisfied because you were not expecting to get the free beverage and snack.
  • One-dimensional quality – this is something that customer would view on a one-dimension. If there is more of it, the customer is more happy, and if there is less of it, the customer is less happy. For example, let’s look at the speed of your internet connection at home. The faster the internet, the happier you are, and the slower the internet, the sadder you are.
  • Must-be quality – this is something that the customer views as an absolute must-have. If you go into a store to buy eggs, you expect the carton to have eggs in it. If the eggs are not there, you are not happy.
  • Indifferent – this is something that a particular customer truly does not care about. The example that Kano gives to explain this in his 2001 paper was the “I-mode” feature on some Japanese cellphones. This feature allowed the user to connect to the internet. When a survey was conducted, most of the middle-aged people viewed this feature indifferently. They could care less that the cellphone could be used to connect to the internet.

Kano

The brilliant insight from the Kano model is that the perception of quality is not linear or static. The perception of quality is non-linear and it evolves with time. Kano hypothesizes that a successful quality element goes through a lifecycle detailed below:

Indifferent => Attractive => One-Dimensional => Must-Be.

A feature that began as indifferent could become an attractive feature, which would then evolve into a one-dimensional feature and finally it becomes a must-be feature. Take for example, the ability to take pictures on your cellphone. This was treated indifferently at the beginning, and then it became an attractive feature. The better the resolution of the pictures taken, the happier you became. Finally, the ability to take sharp pictures became a must-have on your cellphone.

The customer is not always aware of what the attractive feature could be on a product. This is akin to what Ford said – “If I had asked people what they wanted, they would have said faster horses.” Steve Jobs added on to this and said – “People don’t know what they want until you show it to them. That’s why I never rely on market research. Our task is to read things that are not yet on the page.”

Kano had a brilliant insight regarding this as well. In the 2001 paper, “Life Cycle and Creation of Attractive Quality”, he gave the Konica model. Kano talked about the camera that Konica came out with in the 1970s that had built-in flash and the capability to auto focus. At that time, the camera was treated as a mature product and to survive the competition Konica decided to come up with a new camera. Konica engaged in a large survey with the customers with the expectation of coming out with a completely new camera. The R&D team was disappointed with the survey results which only suggested minor changes to the existing designs. The team decided to visit a photo processing lab to examine the prints and negative films taken by consumers and to evaluate the quality of prints and developed films. This is the spirit of genchi genbutsu in lean (go and see to grasp the situation). The team learned that the two main issues the users had were to do with underexposures due to lack of flash and out-of-focus.

Kano notes that:

To solve these problems, Konica developed and released cameras with auto focus and a built-in flash as well as auto film loading and winding functions from the middle to the end of 1970s. This prompted consumers to buy a second and even a third camera. Thereafter, Konica’s business considerably grew and completely changed the history of camera development in the world.

As long as customers are around, quality should be viewed as non-linear, complex and evolving.

Always keep on learning…

In case you missed it, my last post was Lessons from Genkan:

Kufu Eyes:

Huike_thinking-big-569924185f9b58eba49ede26

I came across an interesting phrase recently. I was reading Kozo Saito’s paper, “Hitozukuri and Monozukuri”, and I saw the phrase “kufu eyes”. Kufu is a Japanese word that means “to seek a way out of a dilemma.” This is very well explained in K. T. Suzuki’s wonderful book, “Zen and Japaense Culture.” Suzuki talks about kufu in three sections of the book, and each time he adds a little more detail.

“Kufu is not just thinking with the head, but the state when the whole body is involved and applied to the solving of a problem.”

 “Kufu means ‘employing oneself assiduously to discover the way to the objective.’ One may say that this is literally groping in the dark, there is nothing definite indicated… I am afraid this is as far as any master of Zen or swordsmanship can go with his disciples. He leads them until no more leading is possible, and the rest is left to their own devices. If it is a matter of intellection, the way to the goal may be ‘definitely’ prescribed… The students must resort to something very much deeper than mere intellection – something which they cannot obtain from another.”

‘‘The term kufu is the most significant word used in connection with Zen and also in the fields of mental and spiritual discipline. Generally, it means ‘to seek the way out of a dilemma’ or ‘to struggle to pass through a blind alley.’ A dilemma or a blind alley may sound somewhat intellectual, but the fact is that this is where the intellect can go no further, having come to its limit, but an inner urge still pushes one somehow to go beyond. As the intellect is powerless, we may enlist the aid of the will; but mere will, however pressing, is unable to break through the impasse. The will is closer to fundamentals than the intellect, but it is still on the surface of consciousness. One must go deeper yet, but how? This how is kufu. No teaching, no help from the outside is of any use. The solution must come from the most inner part of oneself. One must keep knocking at the door until all that makes one feel an individual being crumbles away. That is, when the ego finally surrenders itself, it finds itself. Here is a newborn baby. Kufu is a sort of spiritual birth pang. The whole being is involved. There are physicians and psychologists who offer a synthetic medicinal substance to relieve one of this pang. But we must remember that, while man is partially mechanistic or biochemical, this does not by any means exhaust his being; he still retains something that can never be reached by medicine. This is where his spirituality lies, and it is kufu that finally wakes us to our spirituality.’’

In his paper, Saito talked about kufu eyes to explain the process of having a curious scientific mind. Kufu eyes looks at the whole and uses personal intuition than just the analytical thinking process. Kufu eyes pushes you to think further perhaps through thought experiments, and to experiment to truly understand the whole picture. One interesting note I would like to make here is of the great American philosopher Dan Dennett’s “intuition pumps.” An intuition pump is a thought experiment structured to allow the thinker to use their intuition to develop an answer to a problem. Just like a mechanical device, if you can model your thought in a thought experiment, you can push on different buttons and pull on different levers and see what happens.

With kufu eyes, you can observe to gain insight. Siato talked about Taiichi Ohno, the father of Toyota Production System, to explain the concept of kufu eyes further:

… learning engineering and science is not enough. There is a third element: professional intuition, probably the most important, yet most difficult to master, but required for the engineering problem solving process. Taichi Ohno, one of the pioneers who developed Toyota Production System, once declared that the essence of TPS is to develop the well trained ‘‘eyes’’ that can see waste which is invisible to the untrained.

Taiichi Ohno took the task of catching up to the American market when the Japanese worker was assumed to be only 1/8th productive as his American counterpart. The most recent development in manufacturing at that time was the idea of mass manufacturing, which is essentially a push system that led to lots of inventory. Toyota could not afford to carry a lot of inventory. The thinking in those days was to combine similar equipment together and perform operations in isolation. Ohno rearranged the entire layout of the plant he was in charge of, so that the equipment was set to follow the process. The practice at that time was to have one operator manning one piece of equipment. Ohno had one operator man multiple equipment at a time. This led to autonomation or Jidoka. To control the amount of parts produced, Ohno came up with the idea of Kanban. Looking back, Ohno definitely had to employ himself assiduously to discover the way to his objective. He could not just rely on his analytical mind, it was more complex than that. His thinking is clearly stated when he said that efficiency must be improved at every step and at the same time, for the plant as a whole. This is the big picture view that is needed in kufu.

Saito combines the different ideas of total-unit, dedication to the team, holistic view, dialectic approach, and nonlinear thinking to explain kufu. Logic and words have limits. I am inspired by the phrase “kufu eyes”. To me, it means looking outward and inward, looking at the big picture, thinking inside and outside of the “box”, and always pushing to go to the edge of a problem. It means to look with the determination to gain insight. It also means to not fall for status-quo, and to always improve. It also means to go slow but deliberately. It means to not stop until you have solved the problem. And at the same not stop there but keep on improving. This is further explained by Suzuki.

This may be difficult , but when you go on exercising kufu toward the subject, you will after some time come to find this state of mind exercising kufu toward the subject, you will after some time come to find this state of mind actualized without noticing each step of progress. Nothing, however, can be accomplished hurriedly.

I will stop with a wonderful lesson from Suzuki’s book:

When we tie a cat, being afraid of its catching a bird, it keeps on struggling for freedom. But train the cat so that it would not mind the presence of a bird. The animal is now free and can go anywhere it likes. In a similar way, when the mind is tied up, it feels inhibited in every move it makes, and nothing will be accomplished with any sense of spontaneity.  Not only that, the work itself will be of a poor quality, or it may not be finished at all.

Always keep on learning…

In case you missed it, my last post was Hitozukuri:

Distrust Simplicity:

whitehead_painting

In today’s post, I will be looking at the famous quote from the famous English mathematician and philosopher, Alfred Whitehead.

“Seek simplicity, and then distrust it.”

This quote comes from his 1920 collection of lectures, The Concept of Nature. The quote is embedded in the paragraph below:

Nature appears as a complex system whose factors are dimly discerned by us. But, as I ask you, Is not this the very truth? Should we not distrust the jaunty assurance with which every age prides itself that it at last has hit upon the ultimate concepts in which all that happens can be formulated? The aim of science is to seek the simplest explanations of complex facts. We are apt to fall into the error of thinking that the facts are simple because simplicity is the goal of our quest. The guiding motto in the life of every natural philosopher should be, Seek simplicity and distrust it.

I like this idea a lot. We are all asked to keep things simple, and to not make things complicated. Whitehead is asking us to seek simplicity first, and then distrust it. Whitehead talks about “bifurcation of nature” – nature as we perceive it, and the nature as it is. Thus, our perception of reality is an abstraction or a simplification based on our perceptions. We need this abstraction to start understanding nature. However, once we start this understanding process, we should not stop. We should build upon it. This is the scientific method – plan the prototype, build it, assess the gap, and continue improving based on feedback.

As I was reading The Concept of Nature, several other concepts came to my mind. The first one was Occam’s razor – the idea that Entities should not be multiplied unnecessarily. Seek the simplest explanation, when all things are equal. At the same time, we should keep Epicurus’ Principle of Multiple Explanations in mind – If more than one theory is consistent with the observations, keep all theories. I also feel that Whitehead was talking about systems and complexity. As complexity increases, our ability to fully understand the numerous relationships decreases. As the wonderful American Systems thinker Donella Meadows said:

We can’t impose our will on a system. We can listen to what the system tells us and discover how its properties and our values can work together to bring forth something much better than could ever be produced by our will alone.

Seeking simplicity is about the attempt to have a starting point to understand complexity. We should continue to evolve our understanding and not stop at the first abstraction we developed. One of the famous Zen story is about the teacher pointing his finger at the moon. I have talked about this here. We should not look at the finger and stop there. We should look at where the finger is pointing. The finger is the road sign and not the destination itself. The simplicity is a representation and not the real thing. We should immediately distrust it because it is a weak copy. Seeking simplicity is not a bad thing but stopping there is. Simplicity is our comfort zone, and Whitehead is asking us to distrust it so that can keep improving our situation – continuous improvement. Whitehead in his later 1929 book, The Function of Reason, states:

The higher forms of life are actively engaged in modifying their environment… (to) (i) to live, (ii) to live well, (iii) to live better.

Final Words:

In seeking simplicity, we are trying to be “less wrong”. In distrusting our simplified abstraction, we are seeking to be “more right”. I will finish with a Zen story.

A Zen master lay dying. His monks had all gathered around his bed, from the most senior to the most novice monk. The senior monk leaned over to ask the dying master if he had any final words of advice for his monks.

The old master slowly opened his eyes and in a weak voice whispered, “Tell them Truth is like a river.”

The senior monk passed on this bit of wisdom in turn to the monk next to him, and it circulated around the room from one monk to another.

When the words reached the youngest monk he asked, “What does he mean, ‘Truth is like a river?’”

The question was passed back around the room to the senior monk who leaned over the bed and asked, “Master, what do you mean, ‘Truth is like a river?’” Slowly the master opened his eyes and in a weak voice whispered, “OK, truth is not like a river.”

Always keep on learning…

In case you missed it, my last post was Cannon’s Polarity Principle:

Cannon’s Polarity Principle:

arrows

I recently read the wonderful book “On the Design of Stable Systems”, by Jerry Weinberg and Daniela Weinberg. I came across a principle that I had not heard of before called “Cannon’s Polarity Principle”. Cannon’s Polarity Principle can be stated as the strategy that a system can use to overcome noise by supplying its own opposing actions. If a system relies on an uncertain environment to supply the opposing factor to one of its regulatory mechanisms, that mechanism must have a much more refined model. By supplying its own opposing factor, it can get away with a much simpler model of the environment.

This principle is one of those things that is profound yet very simple. The Weinbergs give the example of a sticky knob on a gas stove to explain this idea. If the knob is sticky then it is tricky to raise the flame to the precise point we would like it to be. Due to the “stickiness” we will try to apply much more force than needed and inadvertently overshoot, going past the desired point. The result is that the flame is at a much higher setting. When we try to turn the flame down we are still in the same situation and again go past the point where we would like to be.

What we can do instead is to use one hand to push against the direction we would like and then slowly try to turn the knob with our other hand. With this approach we can be much more refined and be at our desired position. By working “against” our own goal, we make precise adjustment possible in the face of an unknown, but small, amount of stickiness.

This got me thinking. There are several times where we apply opposing forces to slow us down, to take the time to reach the correct decision (precise adjustment). One of my favorite Toyotaism is – Go slow to go fast. This makes a lot of sense in the light of the Polarity Principle. Any time we are doing a root cause analysis, we are prone to a plethora of biases including confirmation bias – selectively looking for ideas that reinforce our thinking, and availability bias – latching on to the first idea because that was the immediate idea we came up with. These biases might make us jump to unwarranted conclusions to address symptoms, and not addressing the root problem(s). The Polarity Principle would advise us to slow down.

I will finish this short and sweet with an apt Zen saying:

The one who is good at shooting does not hit the center of the arrow.

Always keep on learning…

In case you missed it, my last post was Contextual Why:

Contextual Why:

Láminas_8_y_9_del_Códice_de_Dresden

One of the scientists that I have referenced in my posts a lot is the American physicist Richard Feynman. I particularly love his imaginary depiction of Mayan astronomy. Feynman went to Mexico for his second honeymoon and came across a copy of the Dresden Codex (one of the oldest surviving books from the Americas). He was particularly interested in the bars and dots in the codex. He was able to decipher the number system that the Mayans used to depict Venus’ trajectory in the solar system. He was so good at it that he was able to find that some of the versions were actually fakes. Feynman imagined the Mayans counting and putting nuts in a pot to make predictions of where Venus would be on a given day. Feynman was curious whether the Mayans actually knew what was happening (why it was happening) or whether they were going by the rules and making predictions based on a rule-based system of counting and manipulating numbers. Feynman stated that the Mayans may have gotten really good with counting but they must not have understood how the celestial bodies worked.

The push for following rules without understanding the context is unfortunate. Yet this is very prevalent. The rigidity of the rules cannot be sustained when a complex situation arises. The rigidity of rules indicates a direct linear relationship where cause and effect are clearly noted. This is the push for standardization and having one best way of doing things. This leads to stagnation, since this stymies creativity and the push for innovation. Rigid rules always break. Another way to look at this is as the push for robustness – avoiding failure by any means. We will put redundant steps, perform multiple inspections and implement punishments for not following rules. In the complex world, we should accept that things will fail – the push should be for resilience, getting back up in a short time. The rules are dictated top-down since the rules are created by the experts. These rules do not have the requisite variety to tackle the uncertainties of day-to-day dealings. The contexts of these rules do not match the actual context where the action takes place – the context at the gemba. Context is what brings out the meaning in a situation. The focus on rules and efficiency through best practice does not lead to having the requisite variety to change the context as needed to address a problem when it arises. We are involved in complex adaptive systems on a day-to-day basis. We need to change context as needed and adapt to respond to unanticipated events. Evolution requires that we have variety. This response is not always rule-based and is developed depending upon the context. We should allow room for bottom-up heuristics, since these are based on experience and local context.

As a simple example, let’s look at 5S, one of the most commonly identified lean tools, to look into this more. 5S is translated from Japanese as Sort, Straighten, Shine, Standardize and Sustain. The rules are provided to us and they are clear cut. Similar to the Mayan story, do we actually know the context for 5S? Toyota did not have 5S. The last few S’s were added on later. This has now changed into 6S and even 7S. The “sort” step in 5S is to have only the required tools needed at the station. The “straighten” step is to identify/label the tools so that operators from other shifts or job rotations can easily find the tools. The third step is “shine” where the work station is cleaned by the operator. This allows the operator to find any spills or other signs of wear and tear that may not be seen by a cleaning crew. These three steps help the operator to identify problems as they occur, raises awareness and helps to take pride in the work. The fourth step is “standardize” and this is mainly a regulatory function to ensure that the first three steps are followed. The last step is “sustain”, which means to integrate the first three steps so that they become the normal routine and if they are not followed, one feels like something is missing. The context is to help the operator do his or her job better and be effective. The context is that a problem is made visible immediately so that it can be addressed and people can be developed. The context is not following rules. The context is not applying 5S in areas where it does not make sense. The context certainly is not policing people. When the context of what the operator does is not made clear, they do what makes sense to them in their context – at that time with the limited information they have. Empty actions do not have context and are thus meaningless and non-value adding.

Seek to understand the perspectives of your employees. Seek to understand their local context. Seek to make them understand your context, and the context of the shared goals and objectives. Heed to their stories. Develop your employees to see problems.

I will finish with an interesting question that was posed by some French researchers in the late 1970’s.

“On a boat, there are 26 sheep and 10 goats. What is the age of the captain?”

Perhaps, you might see this as a trick question. Perhaps, you may use the two numbers given and come up with the answer as 36. The answer 36 sounds right. The answer that the researchers expected was “I do not have enough information to give the answer.”

To the researchers’ surprise, very few subjects challenged the question. Most of them reasoned in their context and came up with a number that made sense in their mind. We are not trained to ask the contextual questions.

Always keep on learning and ask contextual questions…

In case you missed it, my last post was MTTF Reliability, Cricket and Baseball:

Mismatched Complexity and KISS:

mismatch

*work-in-process*

In today’s post, I will be looking at complexity from the standpoint of organizational communication and KISS. For the purpose of this post, I am defining complexity as a measure of computational effort needed to describe your intent. This idea of complexity is loosely based on Kolmogorov’s definition of “Complexity” from an algorithm standpoint.

To give a very simple example, let’s say that I would like to convey two messages, M1 and M2:

M1 = 010101

M2 = 100111

From the complexity standpoint, M2 requires more effort to explain because there is no discerning pattern in the string of numbers. M1, on the other hand, is easier to describe. I can just say, “Repeat 01 three times.” For M2, I have no choice but say the entire string of numbers. In this regard, I could say that M2 is more complex than M1.

Let’s look at another example, M3:

M3 = 1415926535

Here, it may look like there is no discerning pattern to the string of numbers. However, this can be easily described as “first 10 decimal values of pi without 3. Thus, this message also has low complexity. We can easily see a direct linear relationship or know the content just by observation/empirical evidence.

The examples so far have been examples of low complexity messages. These are easy to generate, diffuse and convey. From the complexity standpoint, these are Simple messages. If I were to create a message that explained Einstein’s relativity, it may not be easily understood if the receiver of the message does not have a good grasp of Physics and advanced math. This is an example of medium complexity or a complicated topic. The relationship is evident with all of the information available.

Now let’s say that I would like to create a message about a complex topic – solve poverty or solve global warming. There is no evident relationship here that can be manipulated with an equation to solve the problem. These are examples of wicked problems – there are no solutions to these problems. There are options but none of the options will fully solve the many intricate problems that are entangled with each other. Such a topic is unlikely to be explained in a message.

The common thread in communication or solving problems is the emphasis on KISS (Keep It Simple Stupid). However, in an effort to keeping things simple, we often engage in mismatched complexity. Complex ideas should not be exclusively conveyed as simple statements. The ideal state is that we use the optimal message – adjust complexity of the message to match the complexity of the content. This is detailed in the schematic below. The optimal message is the 45 degree line between the two axes. A highly complex topic should not be expressed using a low complex message such as a slogan or policy statement. In a similar fashion, a low complexity topic does not need a high complexity message method such as an hour-long meeting to discuss something fundamental.

message diagram

The highly complex topic can use both low and medium message methods to ensure that the complex idea is conveyed properly. The diffusion of the highly complex topic can build upon both low and medium message methods. The diffusion of a highly complex topic also requires redundancy, which means that the message must be conveyed as many times as needed and use of metaphors and analogies. One definition of “communication” from the great Gregory Bateson is – Communication is what the receiver understands, not what the sender says.

A good example to explain this is Toyota Production System. The concept of a production system for the entire plant is a complex concept. Toyota Production System was once called “the Ohno method” since it was not implemented company-wide and there was doubt as to the success of the system being a long-term plan. Ohno’s message was not written down anywhere and the employees did not learn that from a manual or a video. Ohno conveyed his ideas by being at the gemba (actual work place), implementing ideas and learning from them. He also developed employees by constantly challenging them to find a better way with less. Ohno used to draw a chalk circle on the floor for supervisors/engineers to make them see what he saw. He developed the Toyota Production System and with continuous mentoring, nurtured it together with the employees. Today there are over 1000 books at Amazon regarding “Lean Manufacturing”. When top management is looking at implementing lean, the message should match the complexity of the content. Low complex message methods like slogans or placards will not work. Medium complex message methods like newsletters, books etc will not work. This will require constant on-the-floor interactive mentoring. At the same time, slogans and newsletters can aid in the diffusion process.

Final Words:

I have always felt that KISS and Poka-Yoke have a similar story to tell from a respect-for-people standpoint. Poka-Yoke (Error proofing) was initially termed as Baka-Yoke to indicate “fool proofing”. Shigeo Shingo changed it to Poke-Yoke to indicate error proofing after an employee asked him “have I been such a fool?” In a similar fashion, KISS was initially put forth as “Keep It Simple Stupid” (without the comma). Nowadays, this has been changed to “Keep It Short and Simple” and “Keep It Simple Straightforward”.

It is good to keep things simple and to view at a problem from a 10,000 feet level. However, we should not stop there. We need to understand the context and complexity of the problem and then create this information in such a manner that it can be diffused across the organization. This can be repeated as many times as needed. Do not insist on simplicity without understanding the complexity of the problem. Asking to keep things simple is an attempt to keep round pegs in familiar square holes. When there is a mismatch of complexity it leads to incorrect solutions and setbacks. As Einstein may have said, “everything should be as simple as it can, but not simpler”.

We can also view the complexity/message diagram in the light of the Feynman (Nobel-prize winning physicist Richard Feynman) technique of studying hard subjects. Feynman came up with a method where he would start studying and making notes pretending to prepare a lecture for a class. He would use simple terms and analogies to explain the subject. When he got stuck he would go back and try to understand it even better. He would then proceed with making notes. He would repeat the steps many times until he got the concept thoroughly. Moving from High to Medium to Low in the diagram, and going back-and-forth helps to connect the dots and gain a better understanding.

I will finish with another quote, attributed to Lotfi Zadeh (father of Fuzzy Logic):

“As complexity rises, precise statements lose meaning and meaningful statements lose precision.”

Always keep on learning…

In case you missed it, my last post was Flat Earth Lean:

Flat Earth Lean:

pipe

How many interpreters does it take to change a light bulb?

It depends on the context!

In today’s post, I will be looking at what I call “Flat Earth Lean” and “Contextual Lean”. I recently came across the concept of “Flat Earth View” in organizational communication. Matthew Koschmann, currently an associate professor at the University of Colorado, talks about the one-dimensional approach to organization communication where the big picture is not used. It is a linear approach without looking at the contexts or the social aspects. Koschmann explains – “What I mean by a flat earth approach is a perspective that seems correct from a limited vantage point because it works for much of our day to day lives, but ultimately it fails to account for the complexity of a situation. For much of human history we got by just fine thinking the earth was flat, even though it was always round. And even with our 21st century sophistication where we know the earth is round, most of us can actually get by with flat earth assumptions much of the time. But what about when things get more complex? If you want to put a satellite into space or take a transcontinental flight, flat earth assumptions are not going to be very helpful. Remember in elementary school when you compared a globe to a map and realized, for example, that it s quicker to fly from New York to Moscow by flying over the North Pole instead of across the Atlantic? What seems counter intuitive from a flat earth perspective actually makes perfect sense from a round earth perspective.”

I would like to draw an analogy to Lean. Perhaps, the concept of flat earth exists in Lean as well. This could be looked at as the tools approach or copying Toyota’s solutions to apply them blindly. The linear approach implies a direct cause and effect relationship. From the Complexity Science standpoint, the linear relationship makes sense only in the simple and complicated domains. This is the view that everything is mechanistic, utilizing the metaphor of a machine – press this button here to make something happen on the other side with no unintended consequence or adverse effects. In this world, things are thought to be predictable, they can be standardized with one-glove-fits-all solutions, and every part is easily replaceable. Such a view is very simplistic and normally cares only about efficiency. This is an approach that is used for technical systems. There is limited or no focus on context. Hajime Ohba, a Toyota veteran, used to say that simply copying Toyota’s methods is like creating the image of Buddha and forgetting to inject soul in it. In Flat Earth Lean, the assumption is that end goal is clearly visible and that it is as easy as going from HERE to THERE. The insistence is always to KISS (keep it simple stupid). In many regards, this reductionist approach was working in the past. Information generation was minimal and the created information was kept local in the hands of the experts. In today’s global economy, organizations do not have the leisure to keep using the reductionist approach. Today, organizations not only have to ensure that information is diffused properly, they also have to rely on their employees to generate new information on a frequent basis. The focus needs to be shifted to organizations being socio-technical systems where things are not entirely predictable.

Here to There

Karl Weick, an American organizational theorist, advises to “complicate yourself”. He cautions us to not rely on oversimplification. We need to understand the context of what we are doing, and then challenge our assumptions. We have to look for contradictions and paradoxes. They are the golden nuggets that help us to understand our systems. In Contextual Lean, we have to understand our problems first and then look for ways to make things better. Implementing 5S with the aim of being “Lean” is the Flat Earth Approach. Implementing 5S and other visualization methods to make sense of our world, and making problems visible so that we can address them is “Contextual Lean”. If there is such a thing as “going Lean” for an organization, it is surely a collective expression. “Lean” does not exist in isolation in a department or in a cabinet; let alone in one Manager or an employee. To paraphrase the great philosopher, Ludwig Wittgenstein, the meaning of an expression exists only in context. Context gives meaning. Toyota’s “Lean” has limited meaning in relation to your organization since it makes sense only in the context of the problems that Toyota has. Thus, when the Top Management pushes for Lean initiation, it has to be in the context of the problems that the organization has. Understanding context requires self-reflection and continuous learning for the organization. This again is a collective expression and does not exist without involving the employees. Interestingly, Contextual Lean has to utilize Flat Earth approach as needed.

Flat Earth and Contextual Lean have some similarities to the late American business theorist Chris Argyris’ ideas of Single and Double Loop learning. Single Loop learning is the concept of correcting an error by using the existing mental models, norms and practices. Argyris gives the example of a thermostat to explain this – Single loop learning can be compared with a thermostat that learns when it is too hot or too cold and then turns the heat on or off. The thermostat is able to perform this task because it can receive information (the temperature of the room) and therefore take corrective action. Double Loop Learning, on the other hand, involves a reflective phase that challenges the existing mental models, norms and practices, and modifies them to correct the error. In Chris Argyris’ words –If the thermostat could question itself about whether it should be set at 68 degrees, it would be capable not only of detecting error but of questioning the underlying policies and goals as well as its own program. That is a second and more comprehensive inquiry; hence it might be called double loop learning. Single Loop Learning has some similarities to Flat Earth Lean in that it wants to take a simplistic approach and does not want to modify the mental models. It wants to keep doing what is told and to use an old analogy – only bring your hands to work and leave your brains outside. Single Loop Learning is a superficial approach to solve problems symptomatically. Double Loop Learning has some similarities to Contextual Lean in that it is not one-dimensional and results in modifying the mental models as needed. It is a continuous learning and adapting cycle. Argyris also believed that organizations learn when its people learn – Organizational learning occurs when individuals, acting from their times and maps, detect a match or mismatch of outcome to expectation which confirms or disconfirms organizational theory-in-use.

I will finish with a fitting contextual story about change.

Mulla Nasrudhin was now an old man. People used to gather around to hear him talk. One day a young man asked for some words of wisdom.

Mulla replied, “When I was young I was very strong minded- I wanted to awaken everyone. I prayed to God to give me the strength to change the world. As time went on, I became middle aged and I realized that I did not change the world. Then I prayed to God to give me strength so that I can at least change those close around me. Now that I am older and perhaps wiser, my prayer has become simpler. I say – God, please grant me the strength to change at least myself.”

Always keep on learning…

In case you missed it, my last post was The Purpose of Visualization:

A Fuzzy 2018 Wish:

2018

I wanted to write a good post for the New Year (2018). I have been thinking about a good “New Year’s” subject to write about for a while now. It is not easy to find topics to write about, and even if I find good topics, it has to pass my threshold level. As I was meditating on this, I came to think about procrastination and ambiguity. With these thoughts, I came to the topic for the post today. My post today is about the importance of “fuzzy concepts”. I am using the term fuzzy concept in a loose sense and will not go into depth or specifics.

We like to think in boxes or categories. It makes it easy for us to make inferences and aids in decision-making. “She is tall” or “He is short”; “this is hard” or “this is easy”. This is a reductionist approach and from a logic standpoint, this type of thinking is called “Boolean logic” and is based on a dichotomy of true or false (0 or 1). Something is either “X” or “not X”. This type of thinking has its merits sometimes.

In contrast, Fuzzy logic helps us in seeing the “in-between”. The fuzzy logic approach utilizes a spectrum viewpoint. It starts as 0 at one end and slowly increases bit by bit all the way to 1. We can express any point between 0 and 1 as a decimal value.

spectrum

In the picture above, the left most point is white (0), and as we go towards right it changes the color to black (1.0) at the extreme right. Any point in between is neither white nor black. It is just in-between and we can identify the gradient as a value between 0 and 1.

In this vein, if I am to get myself to write a post for the New Year, I could be either prepared and ready OR not prepared and ready. I could wait for a long time for the inspiration to strike or to have an epiphany that would add value to the post. From a Boolean standpoint, this is black and white thinking. I have to wait until I am fully ready (1) to write the post. If I am not ready (0), I should not write the post.

The fuzzy thinking is not recent. In fact, there is an old Greek paradox called Sorites paradox, which is attributed to Megarian logician Eubulides of Miletus. The word “Sorites” is derived from the Greek word soros, which means “heap”. The paradox is as follows – if you have a heap of sand, and you take away a grain, would that heap still be a heap? What would happen if you keep taking grains away? At what point does it cease being a heap? We can express this in the Boolean logic as:  (1) = Heap, and (0) = No Heap. However, if we use the fuzzy logic, we could define what a full heap means and what “no heap” means. Anything in between can be defined as a “partial heap”. Fuzzy logic helps us to add a matter of degree to any statement.

The fuzzy logic concept goes really well with continuous improvement philosophy and the thought that lean is a journey and not a destination. We will never be 100% complete with our improvement. We are always incomplete with our improvement, and it is okay that we are incomplete. We have to keep on improving. We do not have to wait until we have the perfect idea or the expensive machinery or tool to start improving our processes. We do not have to wait for others to start on the improvement journey. In a Zen-like fashion, wherever we are, there we are – the right place to start improving. We will always be between 0 and 1 in terms of perfection of the process. We will always be on the journey and never at the destination. Taiichi Ohno, the father of Toyota Production System, had a great saying that encapsulates the fuzzy concept – Don’t seek perfection. 60 percent is good enough!

I will finish with a story I read online from an anonymous source.

The family was driving to their destination for their holiday. The child asked his father, “Are we there yet?”

The father replied, “No son. We are always here.”

I wish all of my readers a Fuzzy 2018. You are exactly where you are to start exactly what you want to start. Wherever you are, there you are!

Always keep on learning…

In case you missed it, my last post was A Merry Happy Christmas and Attractors:

Divine Wisdom and Paradigm Shifts:

cancer

One of the best books I have read in recent times is The Emperor of All Maladies by the talented Siddhartha Mukherjee. Mukherjee won the 2011 Pulitzer Prize for this book. The book is a detailed history of Cancer and humanity’s battle with it. Amongst many things that piqued my interest, was one of the quotes I had heard attributed to Dr. Deming – In God we trust, all others must bring data.

To tell this story, I must first talk about William S. Halsted. Halsted was a very famous surgeon from John Hopkins who came up with the surgical procedure known as the “Radical Mastectomy” in the 1880’s. This is a procedure to remove the breast, the underlying muscles and attached lymph nodes to treat breast cancer. He hypothesized that the breast cancer spreads centrifugally from the breast to other areas. Thus, the removal of the breast, underlying muscles and lymph nodes would prevent the spread of cancer. He called this the “centrifugal theory”. Halsted called this procedure as “radical” to notate that the roots of the cancer are removed. Mukherjee wrote in his book that the intent of radical mastectomy was to arrest the centrifugal spread by cutting every piece of it out of the body. Physicians all across America identified the Radical Mastectomy as the best way to treat breast cancer. The centrifugal theory became the paradigm for breast cancer treatment for almost a century.

There were skeptics of this theory. The strongest critics of this theory were Geoffrey Keynes, a London based surgeon in the 1920s, and George Barney Crile, an American surgeon who started his career in the 1950s. They noted that even with the procedures that Halsted had performed, many patients died within four or five years from metastasis (cancer spreading to different organs). The surgeons overlooked these flaws, as they were firm believers in the Radical Mastectomy. Daniel Dennett, the famous American Philosopher, talks about the concept of Occam’s Broom, which might explain the thinking process for ignoring the flaws in a hypothesis. When there is a strong acceptance of a hypothesis, any contradicting information may get swept under the rug with Occam’s Broom. The contradictory information gets ignored and not confronted.

Keynes was even able to perform a local surgery of the breast and together with radiation treatment achieve some success. But Halsted’s followers in America ridiculed this approach, and came up with the name “lumpectomy” to call the local surgery. In their minds, the surgeon was simply removing “just” a lump, and this did not make much sense. They were aligning themselves with the paradigm of Radical Mastectomy. In fact, some of the surgeons even went further to come up with “superradical” and “ultraradical” procedures that were morbidly disfiguring procedures where the breast, underlying muscles, axillary nodes, the chest wall, and occasionally the ribs, part of the sternum, the clavicle and the lymph nodes inside the chest were removed. The idea of “more was better” became prevalent.

Another paradigm with clinical studies during that time was trying to look only for positive results – is treatment A better than treatment B? However, this approach did not show that treatment A was no better than treatment B. Two statisticians, Jerry Neyman and Egon Pearson, changed the approach with their idea of using the statistical concept of power. The sample size for a study should be based on the power calculated. Loosely stated, more independent samples mean higher power. Thus, with a large sample size of randomized trials, one can make a claim of “lack of benefit” from a treatment. The Halsted procedure did not get challenged for a long time because the surgeons were not willing to take part in a large sample size study.

A Philadelphia surgeon named Dr. Bernard Fisher was finally able to shift this paradigm in the 1980s. Fisher found no reason to believe in the centrifugal theory. He studied the cases put forth by Keynes and Crile. He concluded that he needed to perform a controlled clinical trial to test the Radical Mastectomy against Simple Mastectomy and Lumpectomy with radiation. The opposition from the surgeons slowly shifted with the strong advocacy from the women who wanted a less invasive treatment. Mukherjee cites the Thalidomide tragedy, the Roe vs Wade case, along with the strong exhortation from Crile to women to refuse to submit to a Radical Mastectomy, and the public attention swirling around breast cancer for the slow shift in the paradigm. Fisher was finally able to complete the study, after ten long years. Fisher stated that he was willing to have faith in divine wisdom but not in Halsted as divine wisdom. Fisher brusquely told a journalist – “In God we trust. All other must have data.”

The results of the study proved that all three cases were statistically identical. The group treated with Radical Mastectomy however paid heavily from the procedure but had no real benefits in survival, recurrence or mortality. The paradigm of Radical Mastectomy shifted and made way to better approaches and theories.

While I was researching this further, I found that the quote “In God we trust…” was attributed to another Dr. Fisher. Dr. Edwin Fisher, brother of Dr. Bernard Fisher, when he appeared before the Subcommittee on Tobacco of the Committee on Agriculture, House of Representatives, Ninety-fifth Congress, Second Session, on September 7, 1978. As part of presentation Dr. Fisher said – “I should like to close by citing a well-recognized cliche in scientific circles. The cliche is, “In God we trust, others must provide data.“ This is recorded in “Effect of Smoking on Nonsmokers. Hearing Before the Subcommittee on Tobacco of the Committee on Agriculture House of Representatives. Ninety-fifth Congress, Second Session, September 7, 1978. Serial Number 95-000”. Dr. Edwin Fisher unfortunately was not a supporter of the hypothesis that smoking is bad for a non-smoker. He even cited that people traveling on an airplane are more bothered by crying babies than the smoke from the smokers.

fisher

Final Words:

This past year, I was personally affected by a family member suffering from the scourge of breast cancer. During this period of Thanksgiving in America, I am thankful for the doctors and staff who facilitated her recovery. I am thankful for the doctors and experts in the medical field who were courageous to challenge the “norms” of the day for treating breast cancer. I am thankful for the paradigm shift(s) that brought better and effective treatments for breast cancer. More is not always better! I am thankful for them for not accepting a hypothesis based on just rationalism, an intuition on how things might be working. I am thankful for all the wonderful doctors and staff out there who take great care in treating all cancer patients.

I am also intrigued to find the quote of “In God we trust…” used with the statement that smoking may not have a negative impact on non-smokers.

I will finish with a story of another paradigm shift from Joel Barker in The Business of Paradigms.

A couple of Swiss watchmakers in Centre Electronique Horloger (CEH) in Neuchâtel, Switzerland, developed the first Quartz based watch. They went to different Swiss watchmakers with the technology that would later revolutionize the watch industry. However, the paradigm at that time was the intricate Swiss watch making process with gears and springs. No Swiss Watch company was interested in this new technology which did not rely on gears or springs for keeping time. The Swiss watchmakers with the new idea then went to a Clock convention and set up a booth to demonstrate their new idea. Again, no Swiss watch company was interested in what they had to offer. Two representatives, one from the Japanese company Seiko, and the other from Texas Instruments took notice of the new technology. They purchased the patents and as they say – the rest is history. The new paradigm then became Quartz watches. The Swiss, who were on the top of watch making with over 50% of the watch market in the 1970s, stepped aside for the Quartz watch revolution marking the decline of their industry. This was later termed as the Quartz Revolution.    

Always keep on learning…

In case you missed it, my last post was The Best Attribute to Have at the Gemba: