Weber’s Law at the Gemba:

In today’s post, I am looking at Weber’s Law. Weber’s Law is named after Ernst Heinrich Weber (24 June 1795 – 26 January 1878), a German physician who was one of the pioneers of experimental psychology. I highly recommend the Numberphile YouTube video that explains this in detail.

A simple explanation of Weber’s Law is that we notice things more at a lower intensity than at a higher intensity. For example, the light from your phone in a dark room may appear very bright to you. At the same time, the light from your phone in a bright room may seem insignificant. This type of perception is logarithmic in nature. This means that a change from 1 to 2 feels about the same as a change from 2 to 4, or 4 to 8. The perception of change for an increment of one unit, depends on whether you are experiencing it at a low intensity or a high intensity. At low intensity, a slight change feels stronger.

This is explained in the graph below. The green ovals represent the change of 2 units (2 to 4) and the red ovals represent the same change of 2 units (30 to 32). It can be seen that the perceived intensity is much less for the change from 30 to 32 than for the change from 2 to 4. These are represented by the oval shapes on the Y-axis. To achieve the same level of perceived intensity (change from 2 to 4), we need to create a large amount of intensity (~ change from 30 to 60, a difference of 30 units).

All of this fall under Psychophysics. Per Wikipedia; Psychophysics quantitatively investigates the relationship between physical stimuli and the sensations and perceptions they produce. What does all this have to do with Gemba and Lean?

How often were you able to see problems differently when you came to the production floor as an outsider? Perhaps, you were asked by a friend or colleague for help. You were able to see the problem in a different perspective and you saw something that others missed or you had a better perception of the situation. Most often, we get used to the problems on the floor that we miss seeing things. We do not notice problems until things get almost out of hand or the problems become larger. Small changes in situations do not alert us to problems. This to me is very similar to what Weber’s law teaches us. Small changes in intensity do not appear in our radar unless we are at the low intensity area.

A good example is to imagine a white sheet of paper. If there is one black spot on the paper, it jumps out to us. But if there are many spots on the paper, an additional dot does not jump out to us. It takes a lot of dots before we realize things have changed. One of the experiments that is used to demonstrate Weber’s law is to do with dots. It is easier to see the change from 10 to 20 dots, rather than the change from 110 to 120 dots.

Ohno and Weber’s Law:

Taiichi Ohno was the father of Toyota Production System. I wonder how Taiichi Ohno’s perceptive skills were and whether his skillset followed Weber’s Law. I would like to imagine that his perceptive skillset was linear rather than logarithmic. He trained his perceptive muscles to see a small change no matter what the intensity was. Even if he was used to his gemba, he was able to see waste no matter if it was small, medium or large. Ohno is famous for his Ohno circle, which was a chalk circle he drew on the production floor for his supervisors, engineers etc. He would have them stand in the circle to observe an operation, trying to see waste in the operation. Waste is anything that has no value. Ohno was an expert who could differentiate a little amount of waste. Ohno’s Weber’s Law plot might appear to be linear instead of being logarithmic, when compared to a student like me.

What we can learn from Weber’s Law is that we need to improve our perception skills to perceive waste as it happens. We should not get used to “waste”. When there is already so much waste, the ability to perceive it is further diminished. It would take a larger event to make us notice of problems on the floor. We lack the ability to perceive waste accurately. We can only understand it based on what has been perceived already. This would mean that we should go to gemba more often, and each time try to see things with a fresh set of eyes. As the Toyota saying goes, we should think with our hands and see with our feet. Change spots from where you are observing a process. Understand that gemba not only means the actual place, but it also includes people, equipment, parts and the environment. We should avoid going with preconceived notions and biases. As we construct our understanding try to include input from the actual users/operators as much as possible. Learn to see differently.

Final Words:

One of the examples I came up with for this post is about cleaning rooms. Have you noticed that cleaner rooms get messy fast? Actually, we perceive a slight increase in messiness when the room is clean versus when it is not. The already messy room requires a larger amount of mess to have a noticeable difference. What Weber’s law shows us is that our natural instinct is not to think linearly.

Humans evolved to notice and minimize relative error. As noted on an article on the Science20 website:

One of the researchers’ assumptions is that if you were designing a nervous system for humans living in the ancestral environment, with the aim that it accurately represents the world around them, the right type of error to minimize would be relative error, not absolute error. After all, being off by four matters much more if the question is whether there are one or five hungry lions in the tall grass around you than if the question is whether there are 96 or 100 antelope in the herd you’ve just spotted.

The STIR researchers demonstrated that if you’re trying to minimize relative error, using a logarithmic scale is the best approach under two different conditions: One is if you’re trying to store your representations of the outside world in memory; the other is if sensory stimuli in the outside world happen to fall into particular statistical patterns.

Perhaps, all this means that we learn to see waste and solve problems on a logarithmic scale. And as we get better, we should train to see and solve problems on a linear scale. Any small amount of waste is waste that can be eliminated and the operation to be improved. It does not matter where you are on the X-axis of the Weber’s law plot. I will finish with an excellent anecdote from one of my heroes, Heinz von Foerster, who was also a nephew of Ludwig Wittgenstein. I have slightly paraphrased the anecdote.

Let me illustrate this point. I don’t know whether you remember Castaneda and his teacher, Don Juan. Castaneda wants to learn about things that go on in the immense expanses of the Mexican chaparral. Don Juan says, “You see this … ?” and Castaneda says “What? I don’t see anything.” Next time, Don Juan says, “Look here!” Castaneda looks, and says, “I don’t see a thing.” Don Juan gets desperate, because he wants really to teach him how to see. Finally, Don Juan has a solution. “I see now what your problem is. You can only see things that you can explain. Forget about explanations, and you will see.”

You become surprised because you abandoned your preoccupation with explanations. Therefore, you are able to see. I hope you will continue to be surprised.

In case you missed it, my last post was OODA Loop at the Gemba:

I also encourage the readers to check out my other similar posts:

Drawing at the Gemba

The Colors of Waste

Maurice Merleau-Ponty’s Lean Lessons

OODA Loop at the Gemba:

In today’s post, I am looking at OODA Loop, the brainchild of Col. John Boyd, a highly influential American military strategist. OODA is an acronym for Observe, Orient, Decide and Act. Boyd did not write any book detailing his ideas. However, he did write several papers and also gave lectures detailing his ideas. Boyd was a fighter pilot with the US Air Force. He was famously dubbed as the “40-second Boyd.” Legend goes that he could defeat any pilot who took him on in less than 40 seconds.

Francis Osinga, in his excellent book “Science, Strategy and War”, explained the OODA loop as:

OODA stands for observation, orientation, decision, action. Explained in brief, observation is sensing yourself and the world around you. The second element, orientation, is the complex set of filters of genetic heritage, cultural predispositions, personal experience, and knowledge. The third is decision, a review of alternative courses of action and the selection of the preferred course as a hypothesis to be tested. The final element is action, the testing of the decision selected by implementation. The notion of the loop, the constant repetition of the OODA cycle, is the essential connection that is repeated again and again. Put simply, Boyd advances the idea that success in war, conflict, competition even survival hinges upon the quality and tempo of the cognitive processes of leaders and their organizations.

The OODA loop is generally shown as the schematic below:

John Boyd’s final version of the OODA loop is given below:

From Osinga:

(Boyd) was the first to observe that the common underlying mechanism involved tactics that distort the enemy’s perception of time. He identified a general category of activities to achieve this distortion, the ability to change the situation faster than the opponent could comprehend, which he called “operating inside the Observation– Orientation–Decision–Action (OODA) loop.”

Boyd wonderfully explains the idea of getting inside the opponent’s OODA loop in his paper, “Destruction and Creation.”

Destruction and Creation:

Boyd starts with explaining that we have conceptual models of the external world, the reality. We interact with reality, and we update this model based on our continuous interaction. He stated:

To comprehend and cope with our environment we develop mental patterns or concepts of meaning. The purpose of this paper is to sketch out how we destroy and create these patterns to permit us to both shape and be shaped by a changing environment. In this sense, the discussion also literally shows why we cannot avoid this kind of activity if we intend to survive on our own terms. The activity is dialectic in nature generating both disorder and order that emerges as a changing and expanding universe of mental concepts matched to a changing and expanding universe of observed reality.

Boyd said that we are in a continuous struggle to remove or overcome physical and social environmental obstacles. This means that we have to take actions and decisions on an ongoing basis for our survival. We have to keep modifying our internal representation of reality based on new data. He called this destruction and creation, which he further detailed as analysis and synthesis. We have to use a reductive process of taking things apart, and assembling things together to gather meaning.

There are two ways in which we can develop and manipulate mental concepts to represent observed reality: We can start from a comprehensive whole and break it down to its particulars or we can start with the particulars and build towards a comprehensive whole.

Readers of this blog might see that the ideas of analysis and synthesis are very important in Systems Thinking. Boyd was an avid reader and he was able to see similar ideas in various fields and bring them all together. His sources of inspiration varied from Sun Tzu, Toyota to Kurt Godel.

Boyd continued that the acts of analysis and synthesis require verification to ensure that the newly created mental representation is appropriate.

Recalling that we use concepts or mental patterns to represent reality, it follows that the unstructuring and restructuring just shown reveals a way of changing our perception of reality. Naturally, such a notion implies that the emerging pattern of ideas and interactions must be internally consistent and match-up with reality… Over and over again this cycle of Destruction and Creation is repeated until we demonstrate internal consistency and match-up with reality.

Boyd brilliantly brings in the ideas of the great logician, mathematician, and analytic philosopher Kurt Godel. Godel in 1931 shook the world of mathematics and logic with his two phenomenal theorems – the Incompleteness Theorems. He proved that in any formal systems there will always be statements that cannot be proven within the logical structures of the system, and that any formal system cannot demonstrate its own consistency. Godel’s ideas were so powerful that the great polymath von Neumann is said to have remarked, “it’s all over!”

Boyd used ideas from Godel, Heisenberg’s uncertainty principle and entropy to further explain his OODA loop. Boyd explained Godel’s ideas as:

“You cannot use a system’s own workings to determine if a system is consistent or not…One cannot determine the character and nature of a system within itself. Moreover, attempts to do will lead to confusion and disorder.”

This was the great insight that Boyd had. One has to continuously stay in touch with his environment to have a consistent internal representation of reality. If the link to the environment is cut off, then the internal representation gets faulty, and the continuous destruction and creation of the internal representation is then based on faulty references.

“If I have an adversary out there, what I want to do is have the adversary fold back inside of himself where he cannot really consult the external environment he has to deal with, if I can do this then I can drive him to confusion and disorder, and bring him into paralysis.”

Boyd stated:

According to Gödel we cannot— in general—determine the consistency, hence the character or nature, of an abstract system within itself. According to Heisenberg and the Second Law of Thermodynamics any attempt to do so in the real world will expose uncertainty and generate disorder. Taken together, these three notions support the idea that any inward-oriented and continued effort to improve the match-up of concept with observed reality will only increase the degree of mismatch. Naturally, in this environment, uncertainty and disorder will increase as previously indicated by the Heisenberg Indeterminacy Principle and the Second Law of Thermodynamics, respectively. Put another way, we can expect unexplained and disturbing ambiguities, uncertainties, anomalies, or apparent inconsistencies to emerge more and more often. Furthermore, unless some kind of relief is available, we can expect confusion to increase until disorder approaches chaos— death.

Orient – the Most Important Step:

In the OODA loop, the most important step in OODA is the second O – Orient. This is the step about our mental models and internal representation of the external world. This is where all the schema reside.

Boyd wrote:

The second O, orientation—as the repository of our genetic heritage, cultural tradition, and previous experiences—is the most important part of the O-O-D-A loop since it shapes the way we observe, the way we decide, the way we act.

From Osinga:

Orientation is the schwerpunkt (center of gravity). It shapes the way we interact with the environment.

In this sense, Orientation shapes the character of present observations-orientation- decision-action loops – while these present loops shape the character of future orientation.

Chet Richards, friend of Boyd, writes about orientation:

Orientation, whether we want it to or not, exerts a strong control over what we observe. To a great extent, a person hears, as Paul Simon wrote in “The Boxer,” what he wants to hear and disregards the rest. This tendency to confirm what we already believe is not just sloppy thinking but is built into our brains (Molenberghs, Halász, Mattingley, Vanman. and Cunnington, 2012) … Strategists call the tendency to observe data that confirm our current orientations “incestuous amplification”.

Final Words:

OODA loop is a versatile framework to learn and understand. We already use the concept unconsciously. The knowledge about the OODA loop helps us prepare to face uncertainty in the everchanging environment. You can also see in today’s world that intentional misinformation can heavily disorient people and distort reality.

We should always stay close to the source, the gemba, to gather our data. We should keep updating our mental models, and not rely on old mental models. We should not try to find only data that corroborates our hypotheses. We should continuously update/improve our orientation. We should start learning from varying fields.

We should allow local autonomy in our organization. This allows for better adaptation since they are close to the source. The idea of not being able to adapt with a fast changing environment can also be explained by Murray Gell-Mann’s maladaptive schemata. From Osinga:

One of the most common reasons for the existence of maladaptive schemata is that they were once adaptive, but under conditions that no longer prevail. The environment has changed at a faster rate than the evolutionary process can accommodate.

In case you missed it, my last post was AQL/RQL/LTPD/OC Curve/Reliability and Confidence:

Cybernetics and Design – Poka Yoke, Two Hypotheses and More:

In today’s post I am looking at “Design” from a cybernetics viewpoint. My inspirations for today’s post are Ross Ashby, Stafford Beer, Klaus Krippendorff, Paul Pangaro and Ranulph Glanville. The concept I was originally playing around was how the interface of a device conveys the message to the user on how to interact with the device. For example, if you see a button, you are invited to press on it. In a similar vein, if you see a dial, you know to twist the dial up or down. By looking at the ideas of cybernetics, I feel that we can expand upon this further.

Ross Ashby, one of the pioneers of Cybernetics defined variety as the number of possible elements(states) of a system. A stoplight, for example, generally has three states (Red, Green and Yellow). Additional states are possible, such as (blinking red, no light, simultaneous combinations of two or three lights). Of all the possible states identified, the stoplight is constrained to have only three states. If the stoplight is not able to regulate the traffic in combination with similar stoplights, acting in tandem, the traffic gets heavy resulting in a standstill. Thus, we can say that the stoplight was lacking the requisite variety. Ashby’s Law of Requisite Variety states that only variety can destroy (absorb) variety. This means that the regulator should have enough variety to absorb any perturbations in order to truly manage a system. Unfortunately, the external variety is always larger than the internal variety. In other words, the regulator has to have the means to filter out unwanted external variety and it should amplify the internal variety to stay viable. An important concept to grasp with this idea is that the number of distinguishable states (and thus variety) depends upon the ability of the observer. In this regard, the variety of a system may be dependent on the observer.

With these concepts in mind, I will introduce two ideas (hypotheses) that I have been playing with:

1) Purpose hypothesis: The user determines the purpose/use of a device.

2) Counteraction hypothesis: When presented with a complex situation, the user generally seeks simplicity. When presented with a simple situation, the user generally seeks complexity.

Harish’s Purpose Hypothesis: The user determines the purpose/use of a device.

The user is external to the design of a device. The user at any given point has more variety than the simple device. Thus, the user ultimately determines the purpose of a device. How many times have you used a simple screwdriver for other purposes than screwing/unscrewing a screw?

Harish’s Counteraction hypothesis: When presented with a complex situation, the user generally seeks simplicity. When presented with a simple situation, the user generally seeks complexity.

The user has a tendency to move away from the perceived complexity of a device. If it is viewed as simple, the user will come up with complex ways to use it. If it is viewed as complex, the user will try to come up with simple ways to use the device. Complexity is in the eyes of the beholder. This can be also explained as – Upon realizing that something is not working, a rational being, instead of continuing on the same path, will try to do the opposite. A good example is a spreadsheet – in the hands of an expert, the spreadsheet can be used for highly complicated mathematical simulations with numerous macros, and alternately, in the hands of a novice, the spreadsheet is just a table with some data points. In a similar way, if something is perceived as complex, the user will find a way to simplify the work to get the bare minimum output.

The Cybernetic Dance between the Designer and the User:

There is a dance between the designer and the user, and the medium of the dance is the interface of the device. The designer has to anticipate the different ways the user can interface with the device, and make the positive mannerisms attractive and the negative mannerisms unattractive. In the cybernetics terms, the designer has to amplify the desirable variety of the device so that the user is more likely to choose the correct way the device should be used. The designer also has to attenuate the undesirable variety so that the user will not choose the incorrect ways of use. If the design interface is providing a consistent message each time, then the entropy of the message is said to be zero. There is no change in the “message” conveyed by the design. One of the concepts in Lean is poka yoke or error proofing a device. From what we have seen so far, we can say that a successful poka yoke device has the requisite variety. The message conveyed by the device is consistent and the user always chooses the correct sequence of operation.

Krippendorff explains this nicely in terms of affordances of a device: ^[1]

When an interface works as expected, one can say with James Gibson (1979) that the artifact in question affords the construction that a user has of it; and when it does not work as expected, one can say that the artifact objects to being treated the way it is, without revealing why this is so.

Krippendorff also explains that the interface does not carry a message from the designer to the user. This is an interesting concept. Krippendorff further explains that the user assigns the meaning from how the user interacts with the device. The challenge then to the designer is to understand the problem, and determine the easiest way to solve it.

Different people may interface rather differently with the same artifact. What is a screwdriver for one person, may be an ice pick, a lever to pry a can of paint open, and a way to bolt a door for another. Human-centered designers must realize that they interface with their artifacts in anticipation that the result of their interactions affords others to meaningfully interface with their design—without being able to tell them how.

An interface consists of sequences of ideally meaningful interactions—actions followed by reactions followed by responses to these reactions and so on—leading to a desirable state. This circularity evidently is the same circularity that cybernetics theorizes, including what it converges to, what it brings forth. In human terms, the key to such interactions, such circularities, is their meaningfulness, the understanding of what one does in it, and towards which ends. Probably most important to human-centeredness is the axiom:

Humans do not respond to the physical qualities of things but act on what they mean to them (Krippendorff, 2006a).

Variety Costs Money:

Another concept from the cybernetics viewpoint is that adding variety costs money. In theory, a perfect device could be designed, but this would not be practical from a cost standpoint. Afterall, a low price is one of the ways the designer can amplify variety. A good story to reflect this is the design of the simple USB. A USB cord is often cited as an example for poka yoke. There is only way to insert it into the port. When you think about it, a USB pin has two states for insertion, of which only one is correct. There is no immediate standard way that the user can tell how it can be inserted. Thus, the USB lacks the requisite variety and it can lead to dissatisfaction of the user. Now the obvious question is why this is not an issue on a different connector such as Apple’s lightning cord, which can be inserted either way. It turns out that the lack of variety for the USB was on purpose. It was an effort to save money.^[2]

A USB that could plug in correctly both ways would have required double the wires and circuits, which would have then doubled the cost. The Intel team led by Bhatt anticipated the user frustration and opted for a rectangular design and a 50-50 chance to plug it in correctly, versus a round connector with less room for error.

Feedback must be Instantaneous:

Paul Pangaro defines Cybernetics as:

Cybernetics is about having a goal and taking action to achieve that goal. Knowing whether you have reached your goal (or at least are getting closer to it) requires “feedback”, a concept that was made rigorous by cybernetics.

Thus, we can see that the device should be designed so that any error must be made visible to the user immediately and the user can correct the error to proceed. Any delay in this can only further add to the confusion of the user. The designer has to take extreme care to reduce the user’s cognitive load, when the user is interfacing with the device. Paraphrasing Michael Jackson (not the singer), from the cybernetics standpoint, the organization of the device should have the best possible model of the environment relevant to its purposes. The organization’s structure and information flows should reflect the nature of that environment so that the organization is responsive to it.

Final Words:

I will finish with wise words from Krippendorff regarding how the user perceives meaning by interfacing with a device.

Unlike what semiotics conceptualizes, from a cybernetic perspective, artifacts do not “carry” meanings from designers to their users. They do not “contain” messages or “represent” meanings…

For example, the meaning of a button is what pressing it sets in motion: ringing an alarm, saving a file or starting a car. The meaning of a soccer ball is the role it plays in a game of soccer and especially what its players can do with it. The meaning of an architectural space is what it encourages its inhabitants to do in it, including how comfortable they feel. The meaning of a chair is the perceived ability to sit on it for a while, stand on it to reach something high up, keep books on it handy, for children to play house by covering it with a blanket, and staple several of them for storage. For its manufacturer, a chair is a product; for its distributor, a problem of getting it to a retailer; for a merchant it means profit; for its user, it may also be a conversation piece, an investment, a way to complete a furniture arrangement, an identity marker, and more.

Typically, artifacts afford many meanings for different people, in different situations, at different times, and in the context of other artifacts. Although someone may consider one meaning more important than another, even by settling on a definition—like a chair in terms of affording sitting on it—it would be odd if an artifact could not afford its associated uses. One can define the meaning of any artifact as the set of anticipated uses as recognized by a particular individual or community of users. One can list these uses and empirically study whether this set is afforded by particular artifacts and how well. Taking the premise of second-order cybernetics seriously and applying the axioms of human-centeredness to designers and users alike calls on designers to conceive of their job not as designing particular products, but to design affordances for users to engage in the interfaces that are meaningful to them, the very interfaces that constitute these users’ conceptions of an artifact, for example, of a chair, a building or a place of work.

Always keep on learning…

In case you missed it, my last post was A Study of “Organizational Closure” and Autopoiesis:

[1] The Cybernetics of Design and the Design of Cybernetics – Klaus Krippendorff

[2] Ever Plugged A USB In Wrong? Of Course You Have. Here’s Why

Conceptual Metaphors in Lean:

In today’s post, I am looking at conceptual metaphors in Lean. A Conceptual metaphor is a concept in conceptual linguistics, first introduced by George Lakoff and Mark Johnson in their 1980 book, Metaphors We Live By. They noted that:

Human beings structure their understanding of their experiences in the world via “conceptual metaphors” derived from basic sensorimotor and spatial concepts (spatial primitives and image schemata) learned during infancy and early childhood.

Metaphors are normally thought of as a way to explain something further. Aristotle noted that metaphors made learning pleasant. “To learn easily is naturally pleasant to all people, and words signify something, so whatever words create knowledge in us are most pleasant.” However, the conceptual metaphor theory goes beyond the metaphor being just a linguistic/artistic phenomenon. The conceptual metaphor theory notes that metaphors are primarily used to understand abstract concepts, and that these are used subconsciously on an everyday basis. The conceptual metaphors are treated as an inevitable part of our thinking and reasoning. Lakoff and Johnson note that:

The essence of metaphor is understanding and experiencing one kind of thing in terms of another… Metaphors are fundamentally conceptual in nature; metaphorical language is secondary. Conceptual metaphors are grounded in everyday experience. Abstract thought is largely, though not entirely, metaphorical. Metaphorical thought is unavoidable, ubiquitous, and mostly unconscious. Abstract concepts have a literal core but are extended by metaphors, often by many mutually inconsistent metaphors. Abstract concepts are not complete without metaphors. For example, love is not love without metaphors of magic, attraction, madness, union, nurturance, and so on.

One form of conceptual metaphor is an “Ontological Metaphor” – a metaphor in which an abstraction, such as an activity, emotion, or idea, is represented as something concrete, such as an object, substance, container, or person. A good example of an ontological metaphor in lean is waste. We are taught that we should seek total elimination of waste in lean. We are giving a physical representation to the abstract concept of “waste”. Waste is an adversary that can hurt us, steal from us, and destroy us. To paraphrase Lakoff: (I have inserted Waste in his example)

The ontological metaphor of waste allows us to make sense of phenomena in the world in human terms—terms that we can understand on the basis of our own motivations, goals, actions, and characteristics. Viewing something as abstract as waste in human terms has an explanatory power of the only sort that makes sense to most people. When we are suffering substantial economic losses, WASTE IS AN ADVERSARY metaphor at least gives us a coherent account of why we’re suffering these losses.

It is also interesting to see how the concept of waste got translated as it was transplanted from Toyota to the West. Taiichi Ohno, the father of TPS, saw waste in terms of man-hours and labor density. Outside Toyota, elimination of waste was seen as a means to increase capacity, a pursuit of efficiency alone.

Labor density is the ratio of work and motion.

Work/Motion = Labor Density

In the equation, work indicates the action carried out to forward a process or enhance the added value. Ohno realized that the correct way to improve labor density is to keep the numerator (work) the same, while decreasing the non-value added portion of motion. The denominator is an impersonal motion and the numerator is work with a human touch. The act of intensifying labor density or of raising the labor utility factor means to make the denominator smaller (by eliminating waste) without making the numerator larger.

Kiichiro Toyoda, Toyota’s president in 1949, issued the challenge to catch up with the United States within three years. America’s productivity was thought to be eight or nine times better than Japan’s. Ohno realized that this was not because the Americans were physically exerting ten times more than the Japanese. “It was probably that the Japanese are wasteful in their production system”, Ohno thought. Ohno’s view was that the total elimination of waste should result in man-hour reduction. Toyota’s man-hour reduction movement is aimed at reducing the overall number of man-hours by eliminating wasted motions and transforming them into work. Toyota succeeded because they realized that elimination of waste was an expression of their respect for humanity. The respect of humanity portion may have gotten lost in translation when the ontological metaphor of “waste” was spread outside Toyota.

Employees give their valuable energy and time to the company. If they are not given the opportunity to serve the company by working effectively, there can be no joy. For the company to deny that opportunity is against the principle of respect for humanity. People’s sense of value cannot be satisfied unless they know they are doing something worthwhile.

Ohno’s first go-to training tool was to ask the supervisor to try doing the same work with less operators. The elimination of waste becomes easier when the operators have a visual control system for seeing waste as either time on hand or stock on hand, and when they avoid overproduction via Kanban. Ohno’s view of elimination of waste was to be effective and efficient by producing only what is needed. The idea of elimination of waste in the West may have become pursuing just efficiency and dropping effectiveness. The waste elimination can be viewed as a means to increase capacity, and this leads to the question – why should we stop at the daily required quantity of 100 units now that the improvement activities have yielded us more capacity to produce up to 125 units a day? Lean has become “doing more with less”, while Ohno’s goal was “doing just what is needed with less.” Ohno’s goal was being efficient and effective, even if it meant machines remained idle.

Final Words:

The term “Lean” itself is a conceptual metaphor. “Lean” refers to being fit, as opposed to being obese. In “Lean”, elimination of waste is about “trimming the fat”. The metaphor of “lean” represents the aesthetics of being beautiful and healthy – perhaps a notion of being in charge and knowing what needs to be done. This could be viewed as the Western philosophy of outwardly focus on external beauty, whereas the Eastern philosophy is more inwardly focused. In Japanese culture, the concept of harmony is imperative. This is part of the ‘respect for humanity’ side of the Toyota Production System.

I welcome the reader to explore the concept of conceptual metaphor. You may also like one of my older posts – Would Ohno Change the Term “Lean”?

Always keep on learning…

In case you missed it, my last post was Chekhov’s Gun at the Gemba:

The Confirmation Paradox:

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:

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:

Know Your Edges:

In today’s post I will start with a question, “Do you know your edges?”

Edges are boundaries where a system or a process (depending upon your construction) breaks down or changes structure. Our preference, as the manager or the owner, is to stay in our comfort zone, a place where we know how things work; a place where we can predict how things go; a place we have the most certainty. Let’s take for a simple example your daily commute to work – chances are high that you always take the same route to work. This is what you know and you have a high certainty about how long it will take you to get to your work. Counterintuitively, the more certainty you have of something, the less information you have to gain from it. Our natural tendency is to have more certainty about things, and we hate uncertainty. We think of uncertainty as a bad thing. If I can use a metaphor, uncertainty is like medicine – you need it to stay healthy!

To discuss this further, I will look at the concept of variety from Cybernetics. Variety is a concept that was put forth by William Ross Ashby, a giant in the world of Cybernetics. Simply speaking, variety is the number of states. If you look at a stop light, generally it has three states (Red, Yellow and Green). In other words, the stop light’s variety is three (ignoring flashing red and no light). With this, it is able to control traffic. When the stop light is able to match the ongoing traffic, everything is smooth. But when the volume of traffic increases, the stop light is not able to keep up. The system reacts by slowing down the traffic. This shows that the variety in the environment is always greater than the variety available internally. The external variety also equates with uncertainty. Scaling back, let’s look at a manufacturing plant. The uncertainty comes in the form of 6M (Man, Machine, Method, Material, Measurement and Mother Nature). The manager’s job is to reduce the uncertainty. This is done by filtering the variety imposed from the outside, magnifying the variety that is available internally or looking at ways to improve the requisite variety. Ashby’s Law of Requisite Variety can be stated as – “only variety can absorb variety.”

All organizations are sociotechnical systems. This also means that in order to sustain, they need to be complex adaptive systems. In order to improve the adaptability, the system needs to keep learning. It may be counterintuitive, but uncertainty is required for a complex adaptive system to keep learning, and to maintain the requisite variety to sustain itself. Thus, the push to stay away from uncertainty or staying in the comfort zone could actually be detrimental. Metaphorically, staying the comfort zone is staying away from the edges, where there is more uncertainty. After a basic level of stability is achieved, there is not much information available in the center (away from the edges). Since the environment is always changing, the organization has to keep updating the information to adapt and survive. This means that the organization should engage in safe to fail experiments and move away from their comfort zone to keep updating their information. The organization has to know where the edges are, and where the structures break down. Safe to fail experiments increases the solution space of the organization making it better suited for challenges. These experiments are fast, small and reversible, and are meant to increase the experience of the organization without risks. The organization cannot remain static and has to change with time. The experimentation away from the comfort zone provides direction for growth. It also shows where things can get catastrophic, so that the organization can be better prepared and move away from that direction.

This leads me to the concept of “fundamental regulator paradox”. This was developed by Gerald Weinberg, an American Computer scientist. As a system gets really good at what it does, and nothing ever goes wrong, then it is impossible to tell how well it is working. When strict rules and regulations are put in place to maintain “perfect order”, they can actually result in the opposite of what they are originally meant for. The paradox is stated as:

The task of a regulator is to eliminate variation, but this variation is the ultimate source of information about the quality of its work. Therefore, the better job a regulator does, the less information it gets about how to improve.

This concept also tells us that trying to stay in the comfort zone is never good and that we should not shy away from uncertainty. Exploring away from the comfort zone is how we can develop the adaptability and experience needed to survive.

Final Words:

This post is a further expansion from my recent tweet. https://twitter.com/harish_josev/status/1055977583261769728?s=11

Information is most rich at the edges. Information is at its lowest in the center. Equilibrium also lies away from the edges.

The two questions, “How good are you at something?” and “How bad are you at something?” may be logically equivalent. However, there is more opportunity to gain information from the second question since it leads us away from the comfort zone.

I will finish with a lesson from one of my favorite TV Detectives, D.I Richard Poole from Death in Paradise.

Poole noted that solving murders were like solving jigsaw puzzles. One has to work from the corners, and then the edges and then move towards the middle. Then, everything will fall in line and start to make sense.

Always keep on learning…

In case you missed it, my last post was Bootstrap Kaizen:

The Purpose of Visualization:

“Many men go fishing all of their lives without knowing that it is not the fish they are after.” – a quote misattributed to Henry David Thoreau.

What is the purpose of visualization? Before answering this, let’s look at what is visualization. Visualization is making information visible at the gemba. The information could be in the form of daily production boards or it could be non-conforming components or other artifacts placed on a table on the production floor. Another phrase that is used in place of visualization is “visibilization”. I had talked about this in the post – Visibilization: Crime Fighting, Magic and Mieruka. The purpose of visualization or visibilization is to make waste visible so that appropriate action can be pursued. Or is it?

I recently came across the paper “Defining Insight for Visual Analytics” by Chang, Ziemkiewicz et al. I enjoyed the several insights I was able to gain from this paper. The purpose of visualization is to enable and discover insight. This may seem fairly logical and straightforward. Chang et al. details that there are two types of insights – knowledge building insight and spontaneous insight. The knowledge building insight is a linear continuous process where the operator can use established problem solving methods and heuristics to solve a problem and gain insight into the process. The spontaneous insight does not come from gradual learning heuristics or problem solving methods. The spontaneous insight results in “aha!” moments and usually new knowledge. The spontaneous insight often occurs when the operator has tried the normal problem solving routines without success. The spontaneous insight happens in frustration after several attempts when the mind breaks off from normal routines. Researchers are able to study the two insights by using electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) on the participants’ brains.

Chang et al. notes that – In normal problem solving, the activity in the temporal lobe is continuous and mostly localized in the left hemisphere, which is thought to encode more detailed information in tightly related semantic networks. This indicates that normal problem solving involves a narrow but continuous focus on information that is highly relevant to the problem at hand. In contrast, when participants solve a problem with spontaneous insight, the right temporal lobe shows a sharp burst of activity, specifically in an area known as the superior temporal gyrus. Unlike the left temporal lobe, the right temporal lobe is thought to encode information in coarse, loosely associated semantic networks. This suggests that spontaneous insight occurs through sudden activation of less clearly relevant information through weak semantic networks, which corresponds to a participant’s paradigm shift following an impasse.

The findings indicate that the spontaneous insight is qualitatively different from the knowledge building insight. The knowledge building insight is using the normal routines and increasing the existing knowledge, while the spontaneous insight is breaking away from the normal routines and creating new knowledge. Spontaneous insight is a form of problem solving that is used to find solutions to difficult and seemingly incomprehensible problems. Knowledge-building insight, on the other hand, is a form learning that builds a relationally semantic knowledge base through a variety of problem-solving and reasoning heuristics.

In the light of the two insights, which one is better? The point is not to identify what is better, but to understand that both types of insights are important and are both related to one another. Chang et al. theorizes that one can only gain spontaneous insights only from routine knowledge building insights. In their words – Einstein didn’t come up with the Theory of Relativity out of thin air but rather based it on experiments inconsistent with existing theories and previous mathematical work. The existence of deep, complex knowledge about a subject increases the likelihood that a novel connection can be made within that knowledge. Likewise, each major spontaneous insight opens up the possibility of new directions for knowledge-building. Together, the two types of insights support each other in a loop that allows human learning to be both flexible and scalable.

Chang et al. hypothesizes that there is a positive non-linear relationship between gaining insights and the knowledge that the operator already possesses. The more knowledge the operator has, the more likelihood that the operator will gain further insights with visualization. In this light, the purpose of visualization is to develop your employees, and in some regards demonstrates respect for people. Making the problems/waste visible allows them to engage in daily/ frequent problem solving routines that builds knowledge building insights, which then leads to spontaneous insights to improving their processes. In other words, it is about building the continuous improvement muscle! The problems on the floor can vary in their complexities. There can be routine problems with known linear relationships (simple to complicated problems), and there can be problems where there are no known solutions and are intricately woven with non-linear relationships (complicated to complex problems). Solving the routine problems can help with gaining valuable spontaneous insights to tackle the complex problems.

I will finish off with a quote from the great Carl Sagan when he went on The Tonight Show with Johnny Carson:

For most of history of life on this planet, almost all the information they had to deal with was in their (organisms’) genes. Then about 100 million years ago or a little longer than that, there came to be a reptile that for the first time in the history of life had more information in its brains than in its genes. That was a major step symbolically in the evolution of life on this planet. Well, now we have an organism – us, which can store more information outside the body altogether than inside the body – that is in books and computers and television and video cassettes. And that extraordinarily expands our abilities to understand what is happening and to manipulate and control our environment, if we do it wisely, for human benefit.

Always keep on learning…

In case you missed it, my last post was Looking at Kaizen and Kaikaku:

Hammurabi, Hawaii and Icarus:

In today’s post, I will be looking at Human Error. In November 2017, The US state of Hawaii reinstated the Cold War era nuclear warning signs due to the growing fears of a nuclear attack from North Korea. On January 13, 2018, an employee from the Hawaii Emergency Management Agency sent out an alert through the communication system – “BALLISTIC MISSILE THREAT INBOUND TO HAWAII. SEEK IMMEDIATE SHELTER. THIS IS NOT A DRILL.” The employee was supposed to take part in a drill where the emergency missile warning system is tested. The alert message was not supposed to go to the general public. The cause for the mishap was soon determined to be human error. The employee in the spotlight and few others left the agency soon afterwards. Even the Hawaiian governor, David Ige, came under scrutiny because he had forgotten his Twitter password and could not update his Twitter feed about the false alarm. I do not have all of the facts for this event, and it would not be right of me to determine what went wrong. Instead, I will focus on the topic of human error.

One of the first proponents of the concept of human error in the modern times is the American Industry Safety pioneer, Herbert William Heinrich. In his seminal 1931 book, Industrial Accident Prevention, he proposed the concept of Domino theory to explain industry accidents. Heinrich reviewed several industrial accidents of his time, and came up with the following percentages for proximate causes:

88% are from unsafe acts of persons (human error),
10% are from unsafe mechanical or physical conditions, and
2% are “acts of God” and unpreventable.

The reader may find it interesting to learn that Heinrich was working as the Assistant Superintendent of the Engineering and Inspection Division of Travelers Insurance Company, when we wrote the book in 1931. The data that Heinrich collected was somehow lost after the book was published. Heinrich’s domino theory explains an injury from an accident as a linear sequence of events associated with five factors – Ancestry and social environment, Fault of person, Unsafe act and/or mechanical or Unsafe performance of persons, Accident and Injury.

He hypothesized that taking away one domino from the chain can prevent the industrial injury from happening. He wrote – If one single factor of the entire sequence is to be selected as the most important, it would undoubtedly be the one indicated by the unsafe act of the person or the existing mechanical hazard. I was taken aback by the example he gave to illustrate his point. As an example, he talked about an operator fracturing his skull as the result of a fall from a ladder. The investigation revealed that the operator descended the ladder with his back to it and caught his heel on one of the upper rungs. Heinrich noted that the effort to train and instruct him and to supervise his work was not effective enough to prevent this unsafe practice. “Further inquiry also indicated that his social environment was conducive to the forming of unsafe habits and that his family record was such as to justify the belief that reckless tendencies had been inherited.”

One of the main criticisms to Heinrich’s Domino model is its simplistic nature to explain a complex phenomenon. The Domino model is reflective of the mechanistic view prevalent at that time. The modern view of “human error” is based on cognitive psychology and systems thinking. In this view, accidents are seen as a by-product of the normal functioning of the sociotechnical system. Human error is seen as a symptom and not a cause. This new view uses the approach of “no-view” when it comes to human error. This means that the human error should not be its own category for a root cause. The process is not perfectly built, and the human variability that might result in a failure is the same that results in the ongoing success of the process. The operator has to adapt to meet the unexpected challenges, pressures and demands that arise on a day-to-day basis. The use of human error as a root cause is a fundamental attribution error – focusing on the human trait of the operator as being reckless or careless; rather than focusing on the situation that the operator was in.

One concept that may help in explaining this further is Local Rationality. Local Rationality starts with the basic assumption that everybody wants to do a good job, and we try to do the best (be rational) with the information that is available to us at a given time. If this decision led to an error, instead of looking at where the operator went wrong, we need to look at why he made the decisions that made sense to him at that point in time. The operator is in the “sharp end” of the system. James Reason, Professor Emeritus of Psychology at the University of Manchester in England, came up with the concept of Sharp End and Blunt End. Sharp end is similar to the concept of Gemba in Lean, where the actual action is taking place. This is mainly where the accident happens and is thus in the spotlight during an investigation. Blunt end, on the other hand, is removed and away in space and time. The blunt end is responsible for the policies and constraints that shape the situation for the sharp end. The blunt end consists of top management, regulators, administrators etc. Professor Reason noted that the blunt end of the system controls the resources and constraints that confront the practitioner at the sharp end, shaping and presenting sometimes conflicting incentives and demands. The operators in the sharp end of the sociotechnical system inherits the defects in the system due to the actions and policies set by blunt end and can be the last line of defense instead of being the main proponents or instigators of the accidents. Professor Reason also noted that – rather than being the main instigators of an accident, operators tend to be the inheritors of system defects. Their part is that of adding the final garnish to a lethal brew whose ingredients have already been long in the cooking. I encourage the reader to research the works of Jens Rasmussen, James Reason, Erik Hollnagel and Sydney Dekker since I have tried to only scratch the surface.

Final Words:

Perhaps the oldest source of human error causation is the Code of Hammurabi, the code of ancient Mesopotamian laws dating back to 1754 BC. The Code of Hammurabi consisted of 282 laws. Some examples of human error are given below.

If a builder builds a house for someone, and does not construct it properly, and the house which he built falls in and kill its owner, then that builder shall be put to death.
If a man rents his boat to a sailor, and the sailor is careless, and the boat is wrecked or goes aground, the sailor shall give the owner of the boat another boat as compensation.
If a man lets in water and the water overflows the plantation of his neighbor, he shall pay ten gur of corn for every ten gan of land.

I will finish off with the story of Icarus. In Greek mythology, Icarus was the creator of the labyrinth in the island of Minos. Icarus’ father was the master craftsman Daedalus. King Minos of Crete imprisoned Daedalus and Icarus in Crete. The ingenious Daedalus observed the birds flying and invented a set of wings made from bird feathers and candle wax. He tested the wings out and made a pair for his son Icarus. Daedalus and Icarus planned their escape. Daedalus was a good Engineer since he studied the failure modes of his design and identified the limits. Daedalus instructed Icarus to follow him closely and asked him to not fly too close to the sea since the moisture can dampen the wings, and not fly too close to the sun since the heat from sun can melt the wings. As the story goes, Icarus was excited with his ability to fly and got carried away (maybe reckless). He flew too close to the sun, and the wax melted from his wings causing him to fall down to his untimely death.

Perhaps, the death of Icarus could be viewed as a human error since he was reckless and did not follow directions. However, Stephen Barlay in his 1969 book, Aircrash Detective: International Report on the Quest for Air Safety, looked at this story closely. At the high altitude that Icarus was flying, the temperature will actually be cold rather than warm. Thus, the failure would actually be from the cold temperature that would make the wax brittle and break instead of wax melting as indicated in the story. If this was true, during cold weathers the wings would have broken down and Icarus would have died at another time even if he had followed his father’s advice.

Always keep on learning…

In case you missed it, my last post was A Fuzzy 2018 Wish

The Socratic Method:

In today’s post, I am looking at the Socratic Method. Socrates was one of the early founders of Western Philosophy. Marcus Cicero (106–43 BCE), a Roman politician, wrote that it was Socrates who brought philosophy down from heaven to earth.

“Socrates however (was the) first (who) called philosophy down from heaven, and placed it in cities, and introduced it even in homes, and drove (it) to inquire about life and customs and things good and evil.”

I have always been curious about the Socratic Method. I have heard it mentioned in many books as the method to teach by asking. In my mind, I drew the analogy of guiding a horse to the pond so that it can drink water. The “guiding” is done through the questions so that the teacher does not provide the answer to the student directly. Instead, the student comes up with the answer. This is not the same as the normal teaching in schools (“lecturing”), where the teacher will give the answers, while the students remain passive. Socrates used the analogy of a midwife who helps others to deliver their thoughts in a clear and meaningful manner.

There are three terms commonly seen to describe the Socrates Method.

Elenchos
Dialectic
Aporia

Elenchos is a Greek term, which can be translated as “cross-examination”. There is a negative connotation to this term. Socrates’ method has been described as an Elenctic method. The negative connotation comes from pointing out to the interlocutor that he does not have the knowledge that he thought he did, puncturing the conceit of wisdom. Socrates would start out by saying that he does not know about something, for example, the concept of virtue. Then he would ask for help from the person of interest to define what virtue is. From that point onwards, once the person of interest commits to a definition, Socrates will continue to ask questions, and each question will point out a weakness that refutes the definition. After a round of questions, the person of interest gets very confused and recognizes that he did not understand the subject as he thought he did and feels that he embarrassed himself.

Dialectic is another Greek term that can be translated as “discussion”. Dialectic does not have the negative connotation that Elenchos has. Any complex idea contains contradictions, inconsistencies and even portions of ignorance. The Dialectic method is a way to reveal the contradictions or inconsistencies, to go back and forth between contrasting ideas to refine the topic on hand.

What Socrates is trying to achieve from his questions is “Aporia”. Aporia is another Greek term that can be translated as “perplexity” or “impasse”. Once the interlocutor realizes that he does not know as much as he thought he did, he achieves aporia. He feels the discomfort cognitively because he was sure that he knew about the subject. The interlocutor is outside of his comfort zone. However, Socrates was able to find fault with his knowledge. Aporia is the starting point for the interlocutor to examine himself and reflect so that new knowledge can be gained.

Combining the three ideas above, we can loosely explain the Socratic Method as follows:

Make the person of interest (POI) at ease, and ask the question in the form of “what is X?”
If POI defines “X” as “Y”, find examples where “X” is not “Y”
Ask questions to further define “X” in light of the new information. Repeat (2) and (3).
Each round of questions must move the POI further away from their first definition.
POI achieves aporia.

Socrates would plead ignorance and ask for specific definitions when asking questions. The questions can also be in other forms such as “what is the purpose of X” or “How does one obtain X” etc. The first question forces the POI to define the boundary of his conception of the idea. This can be thought of as a box. However, with each refutation, the POI realizes that the boundary he first drew is not enough, and that he has to redefine the boundary – perhaps make it larger or smaller, or draw the boundary in a whole other area.

One of the best examples I have seen to explain this is that of a chair. How would one define a chair? One possible definition is that a chair is something for a person to sit upon.

However, there are many other things that people sit on, for example – a step on a stair.

With this refutation, the definition may now be changed to “a chair is something designed for a person to sit.”

The new refutation might be that a bench is something that is designed for a person to sit, and so is a stool. These are not chairs.

Perhaps, the chair can be now defined as “piece of furniture designed for only one person with a back and four legs”. This is similar to the definition in Merriam Webster dictionary.

Even with the new definition, there are still inconsistencies. There are chairs such as decorative chairs that are not supposed to be sat on. There are chairs like a bean bag chair that do not have a back or legs.

Compared to defining a chair, it is harder to define ideas that are not tangible. There are many phrases in Lean like “Respect for People” and “flow” that are thrown around. How would you define “Respect for People”? Would you define it as being nice to your workers? How would you define “flow”? Would you define it as production with one-piece at a time?

On a side note, you can use the Socratic Method on yourself. This can be compared to Hansei in Toyota Production System. What are your beliefs and worldviews? Can you identify any contradictions or inconsistencies that might refute this? Actively seeking out to disprove your belief system helps you in your pursuit for wisdom. Seek out aporia!

Final words:

Socrates did not write any books. Plato, his disciple, wrote about Socrates a lot in his books. Most of what we know about Socrates came from Plato’s books. Socrates never defined or explained his method, nor did Plato write it down as a method. What we have come to know as the Socratic Method is from reading Plato’s books and noting the patterns of dialogues that Socrates engaged in. In Plato’s book, “Apology”, Socrates talks about the reason for going around and asking questions. Socrates’ friend, Chaerephon went to Delphi and asked the Pythian priestess “Is there anybody wiser than Socrates?” The Pythian priestess responded that there was no one wiser. This really confused Socrates, and he took this to mean that the Gods are commanding him to examine himself as well as others. He came to the realization that while others were pretending to possess knowledge, he knows nothing, and this knowledge is what sets him apart from others. Socrates said that the unexamined life is not worth living. The pursuit of knowledge starts with questions.

I will finish with a story of Diogenes and Plato. Diogenes was one of the founders of Cynic Philosophy. Diogenes asked Plato for a definition of man. Knowing Diogenes’ cynical nature, Plato gave the tongue-in-cheek definition from Socrates – “Man is a featherless biped.” Diogenes went outside, and bought a chicken. He then plucked all of its feathers, brought it to Plato, and said, “Behold. Here is a man.”

Plato then ordered his academy to add “with broad flat nails” to the definition.

Always keep on learning…

In case you missed it, my last post was Which Way You Should Go Depends on Where You Are:

Clause for Santa – A Look at Bounded Rationality:

It is Christmas time in 2016. My kids, ages 6 and 9, believe in Santa Claus. It bothers me that they believe in Santa Claus; mainly because it is not logical to believe in a magical being bringing materialistic presents and also because we, their parents, do not get credit for the presents they receive.

From my children’s perspective though, Santa does make sense. Think of it as a black box; they write what they want in a list, believe in Santa, and on Christmas day they find their toys under the Christmas tree. The output matches the input, repeatedly over the years. This passes the scientific evidence based sniff tests’ criteria. They also find additional evidence in the form of stories, movies, songs etc. of Santa Clause and his magical flying reindeers. From their standpoint, they have empirical evidence for making a decision to believe in Santa.

This line of thinking led me to reflect on “Bounded Rationality”. Bounded Rationality is a concept that was created by the great American thinker Herbert Simon. Herbert Simon won the Nobel Prize for Economics in 1978 for his contributions.

According to the famous German Psychologist Gerd Gigerenzer, in the 1950s and 60s, the enlightenment notion of reasonableness reemerged in the form of the concept of “rationality”. Rationality refers to the optimization of some function. The optimization can be maximization or minimization. Simon determined that there is a limit to the “rationality” of humans, and his views were against the ideas of a fully rational man in neoclassical economics. Simon believed that we cannot be fully rational while making decisions, and that our rationality is bounded by our mental capabilities and mental models. In his words;

Bounded rationality refers to the individual collective rational choice that takes into account “the limits of human capability to calculate, the severe deficiencies of human knowledge about the consequences of choice, and the limits of human ability to adjudicate among multiple goals”.

Bounded rationality does not, therefore, argue that decisions and the people taking them are inherently irrational, but that there are realistic limits on the ability of people to weigh complex options in a fully logical and objective way. Bounded rationality therefore concerns itself with the interaction between the human mind (with its prior knowledge, competing value systems and finite cognitive resources) and the social environment – the processes by which decisions are made and how these processes are shaped by the individual and their wider circumstances.

Thus, we do not make the best choices because; we do not have all the information, we do not understand the consequences of all the options or because we do not take time to evaluate all the alternatives. Furthermore we do not always understand that our decision was based on an imperfect model. This leads to the next idea that Herb Simon created – “satisficing”. Satisficing is a word created from two words – satisfy and suffice. In other words, satisficing is the tendency for us to latch on to “good enough for now” solutions. Simon introduced a “stop rule” as part of satisficing criterion: “Stop searching as soon as you have found an alternative that meets your aspiration level.” He later modified it to be a dynamical rule such that the aspiration level or the current criterion is raised or lowered based on previous failures or successes. Gerd Gigerenzer strongly reminds us that Bounded Rationality does not mean optimizing under constraints (finding the best option under the constraints set by the situation) or irrationality (total absence of reasonableness).

In the 2001 book, “Bounded Rationality – The Adaptive Toolbox; edited by Gerd Gigerenzer and Reinhard Selten, there is a chapter dedicated to the role of culture in bounded rationality. This chapter discusses how sociocultural processes produce bounded rational algorithms. Both ethnographic data and computer modeling suggest that innate, individually adaptive processes, such as prestige-biased transmission and conformist transmission, will accumulate and stabilize cultural-evolutionary products that act as effective decision-making algorithms, without the individual participants understanding how or why the particular system works. Systems of divination provide interesting examples of how culture provides adaptive solutions.

One of the examples they cite is the complex system of bird omens amongst the Kantu of Kalimantan (Indonesian Borneo) swidden farmers. Swidden agriculture is a technique of rotational farming. Each Kantu farmer relies on the type of bird and the type of call that the bird makes to choose the agricultural site. This creates a random distribution of the agricultural sites and ultimately helps the Kantu farmers, thus keeping their tradition alive. As a quick and thrifty heuristic, this cultural system suppresses errors that farmers make in judging the chances of a flood, and substitutes an operationally simple means for individuals to randomize their garden site selection. In addition, by randomizing each farmer’s decision independently, this belief system also reduces the chance of catastrophic failures across the entire group — it decreases the probability that many farmers will fail at the same time. All this only works because the Kantu believe that birds actually supply supernatural information that foretells the future and that they would be punished for not listening to it. How many of these cultural traditions do we still carry on in our work lives?

I found this quite interesting and maybe because it is Christmas time I could not help but draw comparisons to how we try to keep the idea of Santa alive for our kids. I thought I would dig into this deeper with my kids. I wanted to push my kids to go beyond their biases and heuristics and try to give them an opportunity to look for more information with regard to their belief in Santa. I started asking them questions in the hope that it would make them reevaluate their current decision to believe in Santa. With enough probing questions, surely they should be able to reevaluate their thinking.

I first asked them “Why do you believe in Santa?”

My youngest responded, “Believing in Santa makes him real”.

My middle child responded, “We saw him at the shopping mall parking lot loading presents in his car.”

My oldest responded with the following facts, “We get presents every year from him. We put out cookies and milk, and they are gone by Christmas day.”

Not giving up, I pushed, “If Santa gives presents to all the kids in the world, I never got any presents when I was a kid in India. Why is that?”

“You were a naughty child”, my youngest responded giggling.

“It takes a long time to get to India”, my middle child also gave her reasoning.

I thought I would give some stats with my questions, “There are about 1.9 billion kids in the world. How can Santa have toys for all of them?”

“That’s easy. Santa is super rich and can buy all the toys he wants” was the response.

“OK. How can he go around world giving toys to all the kids?”, I asked.

“He has magical reindeers” was the response.

Finally, I gave up. My attempts to crack their belief in Santa were failing. I then realized that perhaps it is not bad after all, and that my kids being kids is the most important thing of all. And it makes Christmas more magical for them.

There is always next year to try again!

Always keep on learning…

In case you missed it, my last post was What is the Sound of One Hand Clapping in Systems?

Harish's Notebook – My notes… Lean, Cybernetics, Quality & Data Science.

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Category Archives: Cognition

Weber’s Law at the Gemba:

OODA Loop at the Gemba:

Cybernetics and Design – Poka Yoke, Two Hypotheses and More:

Conceptual Metaphors in Lean:

The Confirmation Paradox:

Know Your Edges:

The Purpose of Visualization:

Hammurabi, Hawaii and Icarus:

The Socratic Method:

Clause for Santa – A Look at Bounded Rationality: