The Whole is ________ than the sum of its parts:

Rubin2

One of the common expressions depicting holistic thinking is – “the whole is larger/greater than the sum of its parts.” In today’s post I would like to look at this expression from a few different perspectives.

Kurt Koffka:

Kurt Koffka (1886 – 1941), the brilliant Gestalt psychologist said, “the whole is other than the sum of its parts.” Koffka was adamant to not misstate him as the whole being larger than the sum of its parts. He was pointing out that the whole is not merely an addition of parts, and that the whole has a separate existence. We humans tend to organize our percepts into wholes. Our mental shortcuts first make us see the whole, rather than the parts. The term “gestalt” itself refers to form or pattern. We are prone to identifying larger patterns from partial data.

Andras Angyal:

Andras Angyal (1902 – 1960) was an American psychiatrist and a Systems Theorist. He emphasized the importance of positional values of parts within a system. He did not view the whole being more than the sum of its parts.

Summation, however, is not organization, but it is of little help simply to say that a system is more than the sum of its parts…“A system is a distribution of constituents with positional values in a dimensional domain.” Functional relationship is the key concept of the reductive approach. For a systems approach a different concept, such as that of positional value, is required which expresses arrangement and compels reference of the parts back to the whole. The value of parts is what they do for the whole. Their function is its maintenance. Only a whole maintained in this way can relate to an environment. To make possible relations with an environment is the function of the whole.

An easy example is to put together three sticks of different lengths. The order of the sticks does not matter for the total length of the three sticks put together. For contrast, let’s look at a car. For a car, the positional value or the order of the parts are of utmost importance. They have to go together in a specific manner for the car to be a car.

Edgar Morin:

Edgar Morin, the brilliant French philosopher says that “the whole is less than the sum of its parts.” This is a powerful statement. The parts lose its freedom when it is constrained to be in a specific form of organization. The whole is more constrained, or has less freedom than the sum of freedoms of the parts put together. The parts give up some of its properties when it organizes to be a whole. At the same time, the whole is also more than the sum of its parts. Morin says:

In order to understand the apparent contradiction of a whole that is simultaneously more and less than the sum of its parts, I claim the heritage of the Greek philosopher Heraclitus, from the 6th century BC: when you reach a contradiction, it doesn’t necessarily mean an error, but rather that you have touched on a fundamental problem. Therefore, I believe that these contradictions should be recognized and upheld, rather than circumvented.

Additionally, Morin stated:

The whole is greater than the sum of the parts (a principle which is widely acknowledged and intuitively recognized at all macroscopic levels), since a macro-unity arises at the level of the whole, along with emergent phenomena, i.e., new qualities or properties.

The whole is less than the sum of the parts, since some of the qualities or properties of the parts are inhibited or suppressed altogether under the influence of the constraints resulting from the organization of the whole.

The whole is greater than the whole, since the whole as a whole affects the parts retroactively, while the parts in turn retroactively affect the whole (in other words, the whole is more than a global entity-it has a dynamic organization).

Morin had strong words about just using holism:

Holism is a partial, one-dimensional, and simplifying vision of the whole. It reduces all other system-related ideas to the idea of totality, whereas it should be a question of confluence. Holism thus arises from the paradigm of simplification (or reduction of the complex to a master-concept or master-category).

Final Words:

The idea that the whole is different or other than the sum of its parts is a different way of thinking. Holism can be as limiting as reductionism. One might say that thinking in terms of wholes is very much thinking in terms of parts since the whole can be construed to be a part of a larger system. The emphasis is on the observer and the purpose that the observer has with the specific perspective that he or she is taking. All humans are purposeful creatures. What one observes, depends upon the properties of the observer. This also means that the other observers, the cocreators or the participants in the system, have their own purposes. We cannot stipulate the purpose(s) for a fellow being. To paraphrase West Churchman, systems thinking begins when one sees through the eyes of another.

The idea that the whole is more important than the part should be challenged, especially when it comes to human systems. All human systems are in a delicate balance with each other, which can tip one way or the other based on emerging attractors. The individual strives for autonomy, while the larger human systems the individual is part of, strive for homonomy. One should not ignore the other.

I will finish with another lesson from Morin:

The parts are at once less and greater than the parts. The most remarkable emergent phenomena within a highly complex system, such as human society, occur not only at the level of the whole (society), but also at the level of the individuals (even especially at that level-witness the fact that self-consciousness only emerges in individuals). In this sense: The parts are sometimes greater than the whole. As Stafford Beer has noted: “[T]he most profitable control system for the parts does not exclude the bankruptcy of the whole.” “Progress” does not necessarily consist in the construction of larger and larger wholes; on the contrary, it may lie in the freedom and independence of small components. The richness of the universe is not found in its dissipative totality, but in the small reflexive entities-the deviant and peripheral units-which have self-assembled within it…

Always keep on learning…

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

Constructivism at the Gemba:

forester

Gemba is one of the most emphasized words in Toyota Production System and Lean. Gemba is where the real action takes place, where one should go to gather the facts. As I ventured into Systems Thinking and Cybernetics, especially the teachings of Heinz von Foerster, it gave me a chance to reflect upon ‘gemba’. Often, we talk about gemba being an objective reality existing independent of us, and one which we can understand if we spend enough time in it. What I have come to realize is that the question of whether an objective reality exists is not the right one to ask. For me, the important question is not whether there is a reality (ontology), but how do you come to know that which we refer to as reality (epistemology).

I will start off with the famous aphorism of West Churchman, a key Systems Thinker:

“A systems approach begins when first you see the world through the eyes of another.”

We all have different worldviews. Your “reality” is different than mine, because you and I are different. We have our own unique experiences that shape our worldviews. One could say that we have constructed a stable reality based on our experiences. We learn in school that we should separate the observed from the observer to make valid observations. The idea of constructivism challenges this. Constructivism teaches that any observation made cannot be independent of the observer. Think about this – what we are reacting to, is actually a model of the world we have built in our heads. This world is constructed based on repeat experiences. The repeat experiences have trained our brain to identify correlations that we can experience when we come across a similar experience again. This is detailed in the excellent book on Heinz von Foerster by Lynn Segal (The Dream of Reality: Heinz Von Foerster’s Constructivism):

The constructivists challenge the idea that we match experience to reality. They argue instead that we “re-cognize” a reality through the intercorrelation of the activities of the various sense organs. It is through these computed correlations that we recognize a reality. No findings exist independently of observers. Observing systems can only correlate their sense experiences with themselves and each other. “All we have are correlations,” says von Foerster. “I see the pencil and I hold the pencil; I can correlate my experience of the pencil and use it… There is indeed a deep epistemological divide that separates the two notions of reality, the one characterized by use of the definite article (“the reality”), the other by the indefinite article (“a reality”). The first depends on the assumption that independent observations confirm the existence of the real world, the second, on the assumption the correlation of independent observations leads to the construction of a real world. To wit, the school says my sensation of touch is confirmation for my visual sensation that ‘here is a table.’ A school says my sensation of touch, in correlation with my visual sensation, generates an experience that I may describe as ‘here is a table.’ “

Von Foerster takes this idea further with an excellent gem:

Properties associated with things are indeed properties that belong to the observer. Obscenity- what’s obscene resides in the observer. If Mr. X says this picture is obscene, then we know something about Mr. X and nothing about the picture.

Ludwig von Bertalanffy, one of the founding fathers of Systems Theory, also had similar ideas. He noted in his 1955 essay, “An Essay on the Relativity of Categories”:

It seems to be the most serious shortcoming of classic occidental philosophy, from Plato to Descartes and Kant, to consider man primarily as a spectator, as ens cogitans, while, for biological reasons, he has essentially to be a performer, an ens agens in the world he is thrown in… the conception of the forms of experience as an adaptive apparatus proved in millions of years of struggle for existence, guarantees that there is a sufficient correspondence between “appearance” and “reality”. Any stimulus is experienced not as it is but as the organism reacts to it, and thus the world-picture is determined by psychophysical organization… perception and experienced categories need not mirror the “real” world; they must, however, be isomorphic to it to such degree as to allow orientation and thus survival. What traits of reality we grasp in our theoretical system is arbitrary in the epistemological sense, and determined by biological, cultural and probably linguistic factors?

An important outcome of accepting the idea of constructivism is the realization that I, as the constructor, am responsible for the reality that I create. I cannot revoke my responsibility for my reality nor my actions. I will further this again by using a von Foerster quote:

“Ontology, and objectivity as well, are used as emergency exits for those who wish to obscure their freedom of choice, and by this to escape the responsibility of their decisions.”

With this, we come to realize that our reality is not the only valid reality. As a constructivist, we realize that others have their own versions of reality.

“The only thing you can do as a constructivist is to give others the opportunity to construct their own world.”

Heinz von Foerster captured this with his two imperatives:

Von Foerster’s Ethical Imperative: “Always act in ways that create new possibilities.”

Von Foerster’s aesthetic imperative: “if you want to SEE, learn how to act.”

The ethical imperative is an invitation to realize that there are other participants in your reality, who themselves create their own versions of realities. The aesthetic imperative similarly is an invitation to reflect that objective reality is not possible. One has to interact and experience to construct a stable reality. Additionally, there are certain things that cannot be made explicit. These have to be implicit in action. My own humble take on the aesthetic imperative is – “if you want to SHOW, learn how to act.” The two imperatives flow into each other nicely. Von forester teaches that we should ensure autonomy for the other participants. For if we do not stipulate autonomy, then the observation does not result in interaction and thus minimize the experience. The concept of observation itself disappears. We should give the responsibility for others to construct their own reality as autonomous agents. In order to see, there has to be interaction between sensorium and motorium.

The idea of autonomous agents is important in constructivism. As Ernst von Glasersfeld puts it: “From the constructivist perspective, ‘input’ is of course not what an external agent or world puts in, but what the system experiences.” This means that we cannot simply command and expect the participants to follow through the orders. This is the idea of viewing the worker as a machine, not as a thinking agent.We should not stipulate the purpose of another. The participants at the gemba must be given the freedom to construct their own stable reality. This includes stipulating their own purposes. Voiding this takes away their freedom of choice and responsibility from the participants.

This brings us back to the original point about gemba. When you go to gemba, you are trying to gather facts from the real place. But as we have been reflecting, reality is not something objective. We need to seek understanding from others’ viewpoints. If we do not seek understanding from others, our reality will not include their versions. Our models will remain our own, one full of our own biases and weaknesses. There is no one Gemba out there. Gemba is a socially constructed reality, one that is a combination of everybody’s constructed reality. As noted earlier, to improve our experience, we should go to gemba often. Our experience helps with our construction of stable reality, which in turn improves our experience. This idea of closure is important in cybernetics and constructivism. We will use another von Foerster gem to improve this understanding – “Experience is the cause. The world is the consequence.”

The very act of knowing that our knowledge is incomplete or imperfect is a second order act. This allows us to perform other second order acts such as thinking about thinking. The idea of constructivism and the rejection of an objective reality might challenge your current mental paradigm of the world. But this is an important idea to at least consider.

I will finish this post with yet another wonderful von Foerster gem, where he talks about Alfred Korzybski’s famous quote, “The map is not the territory.”:

“Ladies and Gentlemen, I am glad that you are all seated, for now comes the Heinz von Foerster theorem: ‘The map is the territory’ because we don’t have anything else but maps. We only have depictions or presentations – I wouldn’t even say re-presentations – that we can braid together within language with the other.”

Always keep on learning…

In case you missed it, my last post was If the Teacher Hasn’t Learned, the Teacher Hasn’t Taught:

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

sonic screwdriver

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 asUpon 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

A Study of “Organizational Closure” and Autopoiesis:

autopoiesis

In today’s post, I am looking at the phrase “Organizational Closure” and the concept of autopoiesis. But before that, I would like to start with another phrase “Information Tight”. Both of these phrases are of great importance in the field of Cybernetics. I first came across the phrase “Information Tight” in Ross Ashby’s book, “An Introduction to Cybernetics”. Ross Ashby was one of the pioneers of Cybernetics. Ashby said: [1]

Cybernetics might, in fact, be defined as the study of systems that are open to energy but closed to information and control— systems that are “information‐tight”.

This statement can be confusing at first, when you look at it from the perspective of Thermodynamics. Ashby is defining “information tight” as being closed to information and control. The Cybernetician, Bernard Scott views this as: [2]

…an organism does not receive “information” as something transmitted to it, rather, as a circularly organized system it interprets perturbations as being informative.

Here the “tightness” refers to the circular causality of the internal structure of a system. This concept was later developed as “Organization Closure” by the Chilean biologists, Humberto Maturana and Francisco Varela. [3] They were trying to answer two questions:

  • What is the organization of the living?
  • What takes place in the phenomenon of perception?

In answering these two questions, they came up with the concept of Autopoiesis. Auto – referring to self, and poiesis – referring to creation or generation. Autopoiesis means self-generation. Escher’s “Drawing Hands” is a good visualization of this concept. We exist in the continuous production of ourselves.

Escher

As British organizational theorist, John Mingers put it: [4]

Maturana and Varela developed the concept of autopoiesis in order to explain the essential characteristics of living as opposed to nonliving systems. In brief, a living system such as a cell has an autopoietic organization, that is, it is ”self-producing. ” It consists of processes of production which generate its components. These components themselves participate in the processes of production in a continual recursive re-creation of self. Autopoietic systems produce themselves and only themselves.

John H Little provides further explanation: [5]

Autopoietic systems, are self-organizing in that they produce and change their own structures but they also produce their own components… The system’s production of components is entirely internal and does not depend on an input-output relation with the system environment.

Two important principles underlying autopoiesis are “structural determinism” and “organizational closure.” To understand these principles, it is first necessary to understand the difference between “structure” and “organization” as Maturana uses these terms. “Organization” refers to the relations between components which give a system its identity. If the organization of a system changes, its identity changes. “Structure” refers to the actual components and relations between components that make up a particular example of a type of system.

Conceptually, we may understand the distinction between organization and structure by considering a simple mechanical device, such as a pencil. We generally have little difficulty recognizing a machine which is organized as a “pencil” despite the fact that pencil may be structurally built in a variety of ways and of a variety of materials. One organizational type, therefore may be manifested by any number of different structural arrangements.

Marjatta Maula provides additional information on the “organization” and “structure”, two important concepts in autopoiesis.

In autopoiesis theory, the concepts ‘organization’ and ‘structure’ of a system have a specific meaning. ‘Organization’ refers to an idea (such as an idea of airplane or a company in general). ‘Structure’ refers to the actual embodiment of the idea (such as a specific airplane or a specific company). Thus, ‘organization’ is abstract but ‘structure’ is concrete (Mingers, 1997). Over time an autopoietic system may change its components and structure but maintain its ‘organization.’ In this case, the system sustains its identity. If a system’s ‘organization’ changes, it loses its current identity (von Krogh & Roos, 1995). [6]

The most important idea that Maturana and Varela put forward was that an autopoietic system does not take in information from its environment and an external agent cannot control an autopoietic system. Autopoietic systems are organizationally (or operationally) closed. That is to say, the behavior of the system is not specified or controlled by its environment but entirely by its own structure, which specifies how the system will behave under all circumstances. It is as a consequence of this closure that living systems cannot have “inputs” or “outputs”-nor can they receive or produce information-in any sense in which these would have independent, objective reality outside the system. Put in another way, since the system determines its own behavior, there can be no “instructive interactions” by means of which something outside the system determines its behavior. A system’s responses are always determined by its structure, although they may be triggered by an environmental event.[7]

Although organizationally closed, a system is not disconnected from its environment, but in fact in constant interaction with it. Maturana and Varela (1987) call this ongoing process “structural coupling” (p. 75). System and environment (which will include other systems) act as mutual sources of perturbation for one another, triggering changes of state in one another. Over time, provided there are no destructive interactions between the system and the medium in which it realizes itself (i.e., its environment), the system will appear to an observer to adapt to its environment. What is in fact happening, though, is a process of structural “drift” occurring as the system responds to successive perturbations in the environment according to its structure at each moment. [7]

In other words, the idea of an organism as an information processing agent is a misunderstanding. When you look at it further, although it might appear as strange, little by little, it might make sense. Think about a classroom, a teacher is giving a lecture and the same “information” reaches the students. However, what type and amount of “information” is taken in depends on each individual student. Maturana explains it as the teacher makes the selection (in the form of the lecture), however, the teacher cannot make the student accept the “information” in its entirety. A loose analogy is a person pushing a button on a vending machine. The internal structure of the machine determines how to react. If the machine does not have a closed structure inside, it cannot react. The pressing of the button is viewed as a perturbation, and the vending machine reacts based on its internal structure at that point in time. If the vending machine was out of order or if there was something blocking the item, the machine will not dispense even if the external agent “desired” the machine to reach in a specific way.

According to Maturana, all systems consisting of components are structure-determined, which is to say that the actual changes within the system depend on the structure itself at that particular instant. Any change in such a system must be a structural change. If this is the case, then an environmental action cannot determine its own effect on a system. Changes, or perturbations in the environment can only trigger structural change or compensation. “It is the structure that determines both what the compensation will be and even what in the environment can or cannot act as a trigger” (Mingers, 1995, p. 30).

It is the internal structure of the system at any point in time that determines:

  1. all possible structural changes within the system that maintain the current organization, as well as those that do not, and
  2. all possible states of the environment that could trigger changes of state and whether such changes would maintain or destroy the current organization (Mingers, 1995, p. 30).[5]

As we understand the idea of autopoiesis, we start to realize that it has serious implications. Our abstract concept of a process is shown below:[5]

INPUT -> PROCESS -> OUTPUT

In light of autopoiesis, we can see that this abstraction does not make sense. An autopoietic system cannot accept inputs. We treat information and knowledge as a commodity that can be easily coded, stored and transferred. Again, in the light of autopoietic systems, we require a new paradigm. As Little continues:[5]

An organizationally closed system is one in which all possible states of activity always lead to or generate further activity within itself… Organizationally closed systems do not have external inputs that change their organization, nor do they outputs in terms of their organization. Autopoietic systems are organizationally closed and do not have inputs and outputs in terms of their organization. They may appear to have them, but that description only pertains to an observer who can see both the system and its environment, and is a mischaracterization of the system. The idea of organizational closure, however, does not imply that such systems have no interactions with their environment. Although their organization is closed, they still interact with their environment through their structure, which is open.

John Mingers provides further insight: [4]

Consider the idea that the environment does not determine, but only triggers neuronal activity. Another way of saying this is that the structure of the nervous system at a particular time determines both what can trigger it and what the outcome will be. At most, the environment can select between alter­natives that the structure allows. This is really an obvious situation of which we tend to lose sight. By analogy, consider the humming computer on my desk. Many interactions, e.g., tapping the monitor and drawing on the unit, have no effect. Even pressing keys depends on the program recognizing them, and press­ing the same key will have quite different effects depending on the computer’s current state. We say, “I’ll just save this file,” and do so with the appropriate keys as though these actions in themselves bring it about. In reality the success (or lack of it) depends entirely on our hard-earned structural coupling with the machine and its software in a wider domain, as learning a new system reminds us only too well.

Another counterintuitive idea was put forth by the German sociologist Niklas Luhmann, that further elaborates the autopoietic system’s autonomous nature and the “independence” from the external agent:

The memory function never relates to facts of the outer world . . . but only to the states of the system itself. In other words, a system can only remember itself.

An obvious question at this point is – If a system is so independent of its environment, how does it come to be so well adjusted, and how do systems come to develop such similar structures?[4]

The answer lies in Maturana’s concept of structural coupling. An autopoietic organization is realized in a particular structure. In general, this structure will be plastic, i.e., changeable, but the changes that it undergoes all maintain auto poiesis so long as the entity persists. (If it suffers an interaction which does not maintain autopoiesis, then it dies.) While such a system exists in an environ­ment which supplies it with necessities for survival, then it will have a structure suitable for that environment or autopoiesis will not continue. The system will be structurally coupled to its medium. This, however, is always a contingent matter and the particular structure that develops is determined by the system. More generally, such a system may become structurally coupled with other systems-the behavior of one becomes a trigger for the other, and vice versa.

Maturana and Varela did not extend the concept of autopoiesis to a larger level such as a society or an organization. Several others took this idea and went further. [8]

Using the tenets of autopoietic theory (Zeleny: 2005), he interprets organizations as networks of interactions, reactions and processes identified by their organization (network of rules of coordination) and differentiated by their structure (specific spatio-temporal manifestations of applying the rules of coordination under specific conditions or contexts). Following these definitions, Zeleny argues that the only way to make organizational change effective is to change the rules of behavior (the organization) first and then change processes, routines, and procedures (the structure). He explains that it is the system of the rules of coordination, rather than the processes themselves, that defines the nature of recurrent execution of coordinated action (recurrence being the necessary condition for learning to occur). He states: ‘Organization drives the structure, structure follows organization, and the observer imputes function’.

 Espejo, Schumann, Schwaninger, and Bilello (1996)adopt similar terminology, but instead of organization they refer to an organization’s identity as the element that defines any organization, explaining that it is the relationships between the participants that create the distinct identity for the network or the group. Organization is then defined as ‘a closed network of relationships with an identity of its own’. While organizations may share the same kind of identity, they are distinguished by their structures. People’s relationships form routines, involving roles, procedures, and uses of resources that constitute stable forms of interaction. These allow the integrated use and operation of the organization’s resources. The emergent routines and mechanisms of interaction then constitute the organization’s structure. Hence, just like any autopoietic entity, organizations as social phenomena are characterized by both an organization (or identity) and a structure. The rules of interaction established by the organization and the execution of the rules exhibited by the structure form a recursive bond.

Final Words:

I highly encourage the readers to pursue understanding of autopoiesis. It is an important concept that requires a shift in your thinking.

I will finish off with an example of autopoietic system that is not living. I am talking about von Neumann probes. Von Neumann probes are named after John von Neumann, one of the most prolific polymaths of last century. A von Neumann probe is an ingenious solution for fast space exploration. A von Neumann probe is a spacecraft that is loaded with an algorithm for self-replication. When it reaches a suitable celestial body, it will mine the required raw materials and build a copy of itself, complete with the algorithm for self-replication. The new spacecraft will then proceed to explore the space in a different direction. The self-replication process continues with every copy in an exponential manner. You may like this post about John von Neumann.

Always keep on learning…

In case you missed it, my last post was The Illegitimate Sensei:

[1] An Introduction to Cybernetics – Ross Ashby

[2] Second-order cybernetics: an historical introduction – Bernard Scott

[3] Autopoiesis and Cognition: The Realization of the Living – Francisco Varela and Humberto Maturana

[4] The Cognitive Theories of Maturana and Varela – John Mingers

[5] Maturana, Luhmann, and Self-Referential Government – John H Little

[6] Organizations as Learning Systems – Marjatta Maula

[7] Implications of The Theory Of Autopoiesis For The Discipline And Practice Of Information Systems – Ian Beeson

Exploring The Ashby Space:

Ashby4

Today’s post is a follow-up to an earlier post, Solving a Lean Problem versus a Six Sigma Problem:

In today’s post, I am looking at “The Ashby Space.” The post is based on the works of Ross Ashby, Max Boisot, Bill McKelvey and Karl Weick. Ross Ashby was a prominent cybernetician who is famous for his “Law of Requisite Variety.” The Law of Requisite Variety can be stated as “Only variety can destroy/absorb variety.” Ashby defined variety as the number of distinguishable states of a system. Stafford Beer used variety as a measure of complexity. The more variety a system has the more complex it is. 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, variety of a system may be viewed as dependent on the observer.

Max Boisot and Bill McKelvey expanded upon the Law of Requisite Variety and stated that only complexity can destroy complexity. In other words, only internal complexity can destroy external complexity. If the regulatory agency of a system does not have the requisite variety to match the variety of its environment, it will not be able to adapt and survive. Ashby explained this using the example of a fencer:

If a fencer faces an opponent who has various modes of attack available, the fencer must be provided with at least an equal number of modes of defense if the outcome is to have the single value: attacked parried.

Boisot and McKelvey restated Ashby’s law as – the range of responses that a living system must be able to marshal in its attempt to adapt to the world must match the range of situations—threats and opportunities—that it confronts. They explained this further using the graphical depiction they termed as “the Ashby Space.” The Ashby Space has two axes, the horizontal axis represents the Variety of Responses, and the vertical axis represents the Variety of Stimuli. Ashby’s law can be represented by the 45˚ diagonal line. The diagonal line represents the requisite variety where the stimuli variety matches the response variety. To adapt and survive we should be in on the diagonal line or below. If we are above the diagonal line, the external variety surpasses the internal variety needed and we perish. Using Ashby’s fencer example, the fencer is able to defend against the opponent only if his defense variety matches or exceeds that of the opponent’s offense variety. This is shown below.

Ashby1

Boisot and McKelvey also depicted the Ordered, Complex and Chaotic regimes in the Ashby space. In the ordered regime, the cause-effect relationships are distinguishable and generally has low variety. The complex regime has a higher variety of stimuli present and requires a higher variety of responses. The cause-effect relationships are non-linear and may make sense only in hindsight. The chaotic regime has the most variety of stimuli. This is depicted in the schematic below. Although the three regimes may appear equally sized in the schematic, this is just for representational purposes.

Ashby2

The next idea that we will explore on the Ashby Space is the idea of the Adaptive Frontier. Ashby proposed a strong need for reducing the amount of variety from the external environment. He viewed this as the role of regulation. Ashby pointed out that the amount of regulation that can be achieved is limited by the amount of information that can be transmitted and processed by the system. This idea is depicted by the Adaptive Frontier curve. Any variety that lies outside this curve is outside the “adaptation budget” of the system. The system does not have the resources nor capacity to process all the variety that is coming in, and does not have the capacity to allocate resources to choose appropriate responses. The adaptive frontier is shown in the schematic below as the red dotted curve.

Ashby3

Combining all the ideas above, the Ashby Space can be depicted as below.

Ashby Space

Boisot and McKelvey detail three types of responses that a living system might follow in the presence of external stimuli. Consider the schematic below, where the agent is located at “Q” in the Ashby Space, which refers to the stimuli variety, X.

  1. The Behaviorist – This is also referred to as the “headless chicken response”. When presented with the stimuli variety, X, the agent will pursue the headless chicken response of trying to match the high variety in a haphazard fashion and soon finds himself outside the adaptive frontier and perishes. The agent fails to filter out any unwanted stimuli and fails to process meaningful information out of the incoming data.
  2. The Routinizer – The routinizer interprets the incoming stimuli as “seen it all before.” They will filter out too much of the incoming data and fail to recognize patterns or mis-categorize them. The routinizer is using the schema which they already have, and their success lies in how well their schema matches the real-world variety-reducing regularities confronting the agent.
  3. The Strategist – An intelligent agent has to correctly interpret the data first, and extract valid information about relevant regularities from the incoming stimuli. The agent then has to use existing schema and match against existing patterns. If the patterns do not match, the agent will have to develop new patterns. As you go up in the Ashby space, the complexity increases, and as you go down, the complexity decreases. The schemas should have the required complexity to match the incoming stimuli. The agent should also be aware of the adaptive frontier and stay within the resource budget constraints. The strategist will try to filter out noise, use/develop appropriate schemas and generate effectively complex responses.

Ashby4

Final Words:

The Ashby Space is a great representation to keep in mind while coping with complexity. The ability of a system to discern what is meaningful and what is noise depends on the system’s past experiences, world views, biases and what its construes as morals and values. Boisot and McKelvey note that:

Not everything in a living system’s environment is relevant or meaningful for it, however. If it is not to waste its energy responding to every will-o-the wisp, a system must distinguish schema based on meaningful information (signals about real-world regularities judged important) from noise (meaningless signals). Note that what constitutes information or noise for a system is partly a function of the organism’s own expectations, judgments, and sensory abilities about what is important —as well as of its motivations— and hence, of its models of the world. Valid and timely representations (schema) economize on the organism’s scarce energy resources.

This also points to the role of sensemaking. As Karl Weick notes, “an increase in complexity can increase perceived uncertainty… Complexity affects what people notice and ignore… The variety in a firm’s repertory of beliefs should affect the amount of time it spends consciously struggling to make sense. The greater the variety of beliefs in a repertoire, the more fully should any situation be seen, the more solutions identified, and the more likely it should be that someone knows a great deal about what is happening.”

The models or representations we construct to represent a phenomenon do not have to be as complex as the phenomenon itself, just like the usefulness of a map is in its abstraction. If the map was as complex as the city it represented, it would become identical to city, with the roads, buildings etc., an exact replica. The system however should have the requisite variety. The system should be able to filter out unwanted variety and amplify its meaningful variety to achieve this. The agent must wait for “meaningful” patterns to emerge, and keep learning.

The agent must also be aware to not claim victory or “Mission Accomplished” when dealing with complexity. Some portion of the stimuli variety may be met with the existing schema as part of routinizing. However, this does not mean that the requisite variety has been achieved. A broken clock is able to tell time correctly twice a day, but it does not mean that you should assume that the clock is functional.

I will finish off with a great insight from Max Boisot:

Note that we do not necessarily require an exact match between the complexity of the environment and the complexity of the system. Afterall, the complexity of the environment might turn out to be either irrelevant to the survival of the system or amenable to important simplifications. Here, the distinction between complexity as subjectively experienced and complexity as objectively given is useful. For it is only where complexity is in fact refractory to cognitive efforts at interpretation and structuring that it will resist simplification and have to be dealt with on its own terms. In short, only where complexity and variety cannot be meaningfully reduced do they have to be absorbed. So an interesting way of reformulating the issue that we shall be dealing with in this article is to ask whether the increase in complexity that confronts firms today has not, in effect, become irreducible or “algorithmically incompressible”? And if it has, what are the implications for the way that firms strategize?

Always keep on learning…

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

I welcome the reader to read further upon the ideas of Ross Ashby. Some of the references I used are:

  1. An Introduction to Cybernetics, Ross Ashby (1957)
  2. Requisite variety and its implications for the control of complex systems, Cybernetica 1:2, p. 83-99, Ross Ashby (1958)
  3. Complexity and Organization–Environment Relations: Revisiting Ashby’s Law of Requisite Variety, Max Boisot and Bill McKelvey (2011)
  4. Knowledge, Organization, and Management. Building on the Work of Max Boisot, Edited by John Child and Martin Ihrig (2013)
  5. Connectivity, Extremes, and Adaptation: A Power-Law Perspective of Organizational Effectiveness, Max Boisot and Bill McKelvey (2011)
  6. Counter-Terrorism as Neighborhood Watch: A Socio/Computational Approach for Getting Patterns from Dots, Max Boisot and Bill McKelvey (2004)
  7. Sensemaking in Organizations (Foundations for Organizational Science), Karl Weick (1995)

Nietzsche’s Overman at the Gemba:

Overman

In today’s post, I am looking at Nietzsche’s philosophy of Übermensch. Friedrich Wilhelm Nietzsche is probably one of the most misunderstood and misquoted philosophers. The idea of Übermensch is sometimes mistranslated as Superman. A better translation is “Overman”. The German term “mensch” means “human being” and is gender neutral. Nietzsche spoke about overman first in his book, “Thus Spoke Zarathustra.” In the prologue of this book, Nietzsche through Zarathustra asks:

I teach you the overman. Man is something that shall be overcome. What have you done to overcome him?

Nietzsche provides further clarification that, “Man is a rope, fastened between animal and Übermensch – a rope over an abyss.Übermensch is an idea that represents a being who has overcome himself and his human nature – one who can break away from the bondage of ideals and create new ones in place of the old stale ones.

Nietzsche came to the conclusion that humanity was getting stale by maintaining status quo through adhering to ideals based in the past. He also realized that the developments in science and technology, and the increase in collective intelligence was disrupting the “old” dogmatic ideals and the end result was going to be nihilism – a post-modern view that life is without meaning or purpose. Nietzsche famously exclaimed that; God is dead! He was not rejoicing in that epiphany. Nietzsche proposed the idea of Übermensch as a solution to this nihilistic crisis. Übermensch is not based on a divine realm. Instead Übermensch is a higher form on Earth. Overcoming the status quo and internal struggles with the ideals is how we can live our full potential in this earth and be Übermensch.

Nietzsche contrasted Übermensch with “Last Man”. The last man embraces status quo and lives in his/her comfort zone. The last man stays away from any struggle, internal or external. The last man goes with the flow as part of a herd. The last man never progresses, but stays where he is, clutching to the past.

Nietzsche used the metaphors of the camel, the lion and the child to detail the progress towards becoming an Übermensch. As the camel, we should seek out struggle, to gain knowledge and wisdom through experience. We should practice self-discipline and accept more duties to improve ourselves. As the lion, we should seek our independence from the ideals and dogmas. Nietzsche spoke of tackling the “Thou Shalt” dragon as the lion. The dragon has a thousand scales with the notation, “thou shalt”. Each scale represents a command, telling us to do something or not do something. As the lion, we should strongly say, “No.” Finally, as the child, we are free. Free to create a new reality and new values.

At the Gemba:

Several thoughts related to Übermensch  and Lean came to my mind. Toyota teaches us that we should always strive toward True North, our ideal state. We are never there, but we should always continue to improve and move towards True North. Complacency/the push to maintain status quo is the opposite of kaizen, as I noted in an earlier post.

I am reminded of a press article about Fujio Cho. In 2002, when Fujio Cho was the President of Toyota Motor Corporation, Toyota became the third largest automaker in the world and had 10.2% of share of world market. Cho unveiled a plan to be world’s largest automaker with 15% global market share. Akio Matsubara, Toyota’s managing director in charge of the corporate planning division, stated:

“The figure of 15 percent is a vision, not a target,” he said. “Now that we’ve achieved 10 percent, we want to bring 15 percent into view as our next dream. We don’t see any significance in becoming No. 1.”

The point of the 15 percent figure, he said, is to motivate Toyota employees to embrace changes to improve so they would not become complacent with the company’s success.

My favorite part of the article was Morgan Stanley Japan Ltd. auto analyst Noriaki Hirakata’s remarks about Fujio Cho. Toyota’s executives, he said, believe Toyota is “the best in the world, but they don’t want to be satisfied.”

It’s as if Cho’s motto has become “Beat Toyota,” Hirakata said.

I am also reminded of a story that the famous American Systems Thinker, Russel Ackoff shared. In 1951, he went to Bell Labs in Murray Hill, New Jersey, as a consultant. While he was there, all the managers were summoned to an impromptu urgent meeting by the Vice President of Bell Labs. Nobody was sure what was going on. Everyone gathered in a room anxious to hear what the meeting was about. The Vice President walked in about 10 minutes late and looked very upset. He walked up to the podium and everyone became silent. The Vice President announced:

“Gentlemen, the telephone system of the United States was destroyed last night.”

He waited as everyone started talking and whispering that it was not true. The Vice President continued:

“The telephone system was destroyed last night and you had better believe it. If you don’t by noon, you are fired.”

The room was silent again. The Vice President then started out laughing, and everyone relaxed.

“What was that all about? Well, in the last issue of the Scientific American,” he said, “there was an article that said that these laboratories are the best industrially based scientific laboratories in the world. I agreed, but it got me thinking.”

The Vice President went to on to state that all of the notable inventions that Bell Lab had were invented prior to 1900. This included the dial, multiplexing, and coaxial cable. All these inventions were made prior to when any of the attendees were born. The Vice President pointed out that they were being complacent. They were treating the parts separately and not improving the system as a whole. His solution to the complacency? He challenged the team to assume that the telephone system was destroyed last night, and that they were going to reinvent and rebuilt it from scratch! One of the results of this was the push button style phones that reduced the time needed to dial a number by 12 seconds. This story reminds me of breaking down the existing ideals and challenging the currently held assumptions.

Nietzsche challenges us to overcome the routine monotonous ideas and beliefs. Instead of simply existing, going from one day to the next, we should challenge ourselves to be courageous and overcome our current selves. This includes destruction and construction of ideals and beliefs. We should be courageous to accept the internal struggle, when we go outside our comfort zone. The path to our better selves is not inside the comfort zone.

Similar to what Toyota did by challenging the prevalent mass production system and inventing a new style of production system, we should also challenge the currently held belief system. We should continue evolving toward our better selves. As Nietzsche said:

What is great in man is that he is a bridge and not an end.

I say unto you: One must still have chaos in oneself to be able to give birth to a dancing star.

Always keep on learning…

In case you missed it, my last post was Solving a Lean Problem versus a Six Sigma Problem:

Solving a Lean Problem versus a Six Sigma Problem:

Model

I must confess upfront that the title of this post is misleading. Similar to the Spoon Boy in the movie, The Matrix, I will say – There is no Lean problem nor a Six Sigma problem. All these problems are our mental constructs of a perceived phenomenon. A problem statement is a model of the actual phenomenon that we believe is the problem. The problem statement is never the problem! It is a representation of the problem. We form the problem statement based on our vantage point, our mental models and biases. Such a constructed problem statement is thus incomplete and sometimes incorrect. We do not always ask for the problem statement to be reframed from the stakeholder’s viewpoint. A problem statement is an abstraction based on our understanding. Its usefulness lies in the abstraction. A good abstraction ignores and omits unwanted details, while a poor abstraction retains them or worse omits valid details. Our own cognitive background hinders our ability to frame the true nature of the problem. To give a good analogy, a problem statement is like choosing a cake slice. The cake slice represents the cake, however, you picked the slice you wanted, and you still left a large portion of the cake on the table, and nobody wants your slice once you have taken a bite out of it.

When we have to solve a problem, it puts tremendous cognitive stress on us. Our first instinct is to use what we know and what we feel comfortable with. Both Lean and Six Sigma use a structured framework that we feel might suit the purpose. However, depending upon what type of “problem” we are trying to solve, these frameworks may lack the variety they need to “solve” the problem. I have the used the quotation marks on purpose. For example, Six sigma relies on a strong cause-effect relationship, and are quite useful to address a simple or complicated problem. A simple problem is a problem where the cause-effect relationship is obvious, whereas a complicated problem may require an expert’s perspective and experience to analyze and understand the cause-effect relationship. However, when you are dealing with a complex problem, which is non-linear, the cause-effect relationship is not entirely evident, and the use of a hard-structured framework like Six sigma can actually cause more harm than benefit. All human-centered “systems” are complex systems. In fact, some might say that such systems do not even exist. To quote Peter Checkland, In a certain sense, human activity systems do not exist, only perceptions of them exist, perceptions which are associated with specific worldviews.

We all want and ask for simple solutions. However, simple solutions do not work for complex problems. The solutions must match the variety of the problem that is being resolved. This can sometimes be confusing since the complex problems may have some aspects that are ordered which give the illusion of simplicity. Complex problems do not stay static. They evolve with time, and thus we should not assume that the problem we are trying to address still has the same characteristics when they were identified.

How should one go from here to tackle complex problems?

  • Take time to understand the context. In the complex domain, context is the key. We need to take our time and have due diligence to understand the context. We should slow down to feel our way through the landscape in the complex domain. We should break our existing frameworks and create new ones.
  • Embrace diversity. Complex problems require multidisciplinary solutions. We need multiple perspectives and worldviews to improve our general comprehension of the problem. This also calls to challenge our assumptions. We should make our assumptions and agendas as explicit as possible. The different perspective allows for synthesizing a better understanding.
  • Similar to the second suggestion, learn from fields of study different from yours. Learn philosophy. Other fields give you additional variety that might come in handy.
  • Understand that our version of the problem statement is lacking, but still could be useful. It helps us to understand the problem better.
  • There is no one right answer to complex problems. Most solutions are good-enough for now. What worked yesterday may not work today since complex problems are dynamic.
  • Gain consensus and use scaffolding while working on the problem structure. Scaffolding are temporary structures that are removed once the actual construction is complete. Gaining consensus early on helps in aligning everybody.
  • Go to the source to gain a truer understanding. Genchi Genbutsu.
  • Have the stakeholders reframe the problem statement in their own words, and look for contradictions. Allow for further synthesis to resolve contradictions. The tension arising from the contradictions sometimes lead us to improving and refining our mental models.
  • Aim for common good and don’t pursue personal gains while tackling complex problems.
  • Establish communication lines and pay attention to feedback. Allow for local context while interpreting any new information.

Final Words:

I have written similar posts before. I invite the reader to check them out:

Lean, Six Sigma, Theory of Constraints and the Mountain

Herd Structures in ‘The Walking Dead’ – CAS Lessons

A successful framework relies on a mechanism of feedback-induced iteration and keenness to learn. The iteration function is imperative because the problem structure itself is often incomplete and inadequate. We should resist the urge to solve a Six Sigma or a Lean problem. I will finish with a great paraphrased quote from the Systems Thinker, Michael Jackson (not the famous singer):

To deal with a significant problem, you have to analyze and structure it. This means, analyzing and structuring the problem itself, not the system that will solve it. Too often we push the problem into the background because we are in a hurry to proceed to a solution. If you read most texts thoughtfully, you will see that almost everything is about the solution; almost nothing is about the problem.

Always keep on learning…

In case you missed it, my last post was Maurice Merleau-Ponty’s Lean Lessons:

Purpose of a System in Light of VSM:

Varieties 2

In today’s post, I am looking at the concept of POSIWID (“Purpose Of a System Is What It Does”) Please note that VSM stands for “Viable System Model” and not “Value Stream Mapping”.

The idea of POSIWID was put forth by the father of Management Cybernetics, Stafford Beer. As Beer puts it: [1]

A good observer will impute the purpose of a system from its actions… There is, after all, no point in claiming that the purpose of a system is to do what it consistently fails to do.

An organization is a sociotechnical and complex system. This means that it cannot be controlled by simple edicts that are put top down from the management. We should not go by what the “designer” of the system says it does, we should impute the purpose from what the system actually does.

A good explanation comes from Dan Lockton: [2]

The implication of a posiwid approach is that it doesn’t matter why a system was designed, or whether the intention was to influence behavior or not. All that matters are the effects: if a design leads to people behaving in a different way, then that is the ‘purpose’ of the design. Intentionality is irrelevant: to understand the behavior of systems, we need to look at their effects… Essentially, a posiwid approach means that both `positive’ and `negative’ effects of a system must be dealt with. We might try to dismiss unintended effects, but they are still effects, and we need to recognize them, and deal with them. Undesirable phenomena are not simply blemishes they are [the system’s] outputs (Beer, 1974, p.7).

A general interpretation of an organization’s purpose is – to make money. This is the idea proposed by Eliyahu Goldratt in his famous book – The Goal. However, Beer’s view of the goal of an organization is to stay viable. Beer defines “viable” as “able to maintain a separate existence”. He identifies all organizations as viable systems. He was inspired by the human anatomy. He realized that the viable systems are recursive. In other words, Every viable system contains viable systems and is contained in a viable system. For example, a human being is a viable system, who is part of an assembly line which is also a viable system. This assembly line in turn is a part of a Value stream, which is again a viable system. This goes on and on. Beer developed Viable System Model (VSM) by exploring the necessary and sufficient conditions for viability in any complex system whether an organism, an organization or a country. 

There are three elements to a viable system:

  • The Operation – This is similar to the muscles and organs. This is what does the actual value adding functions. There can be several operation units in the system in focus.
  • The Meta system (Management) – This is similar to the brain and the nervous system. This is the glue that holds all the operational units and provides coherence to the structure.
  • The Environment – This is the relevant part of the external environment in which the system is in.

In an overall sense, management’s function is to manage complexity. Beer uses variety as a measure of complexity. Variety is the number of possible states of a system. The environment obviously has the maximum variety of the three elements. The operation has more variety than the management. Thus, we can denote this as. [3]

Varieties

Here the amoeba shape represents the environment, the circle represents the operational unit and the square represents the management. “V” represents the variety possessed by each element. Management has to attenuate or filter out the extra variety while amplifying its variety in order to accommodate the variety that surrounds it. The same goes for the operational unit. Please note that we are dealing with continuous loops rather than simple connectivities. Beer postulates his Law of Inter-Recursive Cohesion based on this: Managerial, Operational and Environmental varieties, diffusing through an institutional system, TEND TO EQUATE; they should be designed to do so with minimum damage to people and to cost.

This idea is based on Ross Ashby’s Law of Requisite Variety – Only variety can absorb variety. In order to maintain viability, attenuators and amplifiers must be in place so that the three varieties are equivalent. There are homeostatic loops in place that amplify the lower varieties to absorb the higher varieties, and attenuate the higher varieties towards the lower varieties. This is depicted in the schematic below. Please note the adjustment to the scale of “V” to denote equivalence of variety achieved through attenuation and amplification.

Varieties 2

For a simple example, let’s look at a football game. There are 11 players for each team. There is one-to-one compensation of variety possible between the two teams playing. The officials, “managing” the game are able to match the variety from the players with the use of attenuators (rules, policies etc. of the game) and amplifiers (whistles, flags etc.).

Every viable system has five sub-systems, identified as Systems 1 through 5:

System 1 – Interacting operational units.

System 2 – Responsible for coordination between the interacting operational units, and provides stability via anti-oscillatory and conflict resolution strategies. An example is production control in a manufacturing plant.

System 3 – Responsible for control and optimization, and synergy between the organizational units. Often referred to as an “internal eye” focusing on “here and now”, internal and immediate functions. There is also a “Three*” subsystem that is responsible for monitoring/audit.

System 4 – Responsible for Planning and “Intelligence”. Often referred to as “external eye” focusing on “there and then”. System Four’s role is to observe the anticipated future environment and its own states of adaptiveness and act to bring them into harmony. [4]

System 5 – Responsible for developing “identity” and policy. Maintaining a good balance between System Three’s concern with the day to day running of affairs and System Four’s concentration on the anticipated future is a challenge for every organization. [4] System five is responsible for monitoring the balance between System three and System four.

As noted earlier, the strength of the VSM is in recursion. Every viable system at every recursion level must have the five subsystems working coherently in order to be viable.

Taken it all together, the Viable System Model looks like: [5]

621px-VSM_Default_Version_English_with_two_operational_systems

The diagram can appear confusing due to the recursive nature of the viable systems within the viable system. There is lot more to the VSM than discussed here.

POSIWID:

Due to the presence of viable systems within a viable system, the policies set by each System five may not be in alignment with the policy set by the System five in the larger viable system. Beer postulates that the observed and imputed “purpose” of the system and the “designed” purpose of the system are not in agreement. Beer states that the purpose is generally formulated within a higher recursion, thus, it is imperative that the purpose is restated at each low recursion in a language that the system understands. Based on this purpose, the system in focus will act by its proper inputs and reacts to its environment resulting in a new state of the system. The system should have a “comparator” that continuously compares the declared purpose and the purpose imputed from the results that the system delivers. This results in a feedback that leads to a modification of the original purpose. Beer states that:

This system will converge on a compromise purpose – it is neither what the higher recursion would like to see done, nor what the viable system itself would most like to see done, nor what the viable system itself would like to indulge in doing.

The purposes of the corporate system and those of System One are different, because System One consists of viable systems whose conditions of survival are formulated at a different level of recursion. The compromise convergence must continually act and this generally leads to lowest variety compromise possible. Please note that “what the system does” is done by System One. Beer postulated that autonomy is a computable function of the purpose of a viable system based on this. Autonomy is the maximum discretionary action available for the subsystem, short of threatening the integrity of the system as a whole.

Final Words:

Stafford Beer was man beyond his times for sure. I strongly encourage the readers to read as much of his works as you can. The VSM allows us to diagnose or even design an organization by making sure that the required homeostats, subsystems and channels are present to ensure viability. For those who wish to implement Lean or Six Sigma or Agile or any of other paradigms out there, I will finish with words of wisdom from Beer:

We manage through a model that we hold in our heads about how things work ‘out there’. If our model does not have Requisite Variety, then we ought to incorporate learning circuits that will enrich it. But if we are ideologically attached to our model, so that it is not negotiable, then it becomes a dysfunctional paradigm.

Always keep on learning…

In case you missed it, my last post was Cultural Transmission at Toyota:

[1] Diagnosing the System, Stafford Beer

[2] POSIWID and determinism in design for behaviour change, Dan Lockton

[3] World in Torment, Stafford Beer

[4] The Viable System Model and its Application to Complex Organizations, Allenna Leonard, Ph.D.

[5] VSM By Mark Lambertz – Own work, CC BY-SA 4.0,

https://commons.wikimedia.org/w/index.php?curid=59912637

 

Herd Structures in ‘The Walking Dead’ – CAS Lessons:

zombie_PNG64

The Walking Dead is one of the top-rated TV shows currently. The show is about survival in a post-apocalyptic zombie world. The zombies are referred to as “walkers” in the show. I have written previously about The Walking Dead here. In today’s post, I want to briefly look at Complex Adaptive Systems (CAS) in the show’s backdrop. A Complex Adaptive System is an open non-linear system with heterogenous and autonomous agents that have the ability to adapt to their environment through interactions between themselves and with their environment.

The simplest example to get a grasp of CAS is to look at an ant colony. Ants are simple creatures without a leader telling what each ant should do. Each ant’s behavior is constrained by a set of behavioral rules which determine how they will interact with each other and with their environment. The ant colony taken as a whole is a complex and intelligent system. Each ant works with local information, and interacts with other ants and the environment based on this information. The different tasks that the ants do are patrol, forage, maintain nest and perform midden work. The local information available to each ant is the pheromone scent from another ant. As a whole, their interactions result in a collective intelligence that sustains their colony. In presence of perturbations in their environment, the ants are able to switch to specific tasks to maintain their system. The ants decide the task based on the local information in the form of perturbation to their environment and their rate of interaction with other ants performing the specific tasks. The ants go up in the ranks eventually becoming a forager in the presence of need. A forager ant always stays a forager. The ant colony carries a large amount of “reserve ants” who do not perform any function. This reserve allows for specific task allocation as needed based on perturbations to their environment.

To further illustrate the “self-organizing” or pattern forming behavior of ants, let’s take for example, their foraging activity. The ants will set out from the colony in a random fashion looking for food. Once an ant finds food, it will bring it back to the nest leaving a pheromone trail on its way back. The other ants engaged in the foraging activity will follow the pheromone trail and bring back food while leaving their pheromone scent on the path. The pheromone scent will evaporate over a short amount of time. The ants that followed the shortest path would go back for more food and their pheromone trail will stay “fresh” while a longer path will not remain as “fresh” since the pheromone has more time to evaporate. This means that the path with the strongest pheromone trail is the shortest path to the food. The shortest path was a result of positive feedback loops from more and more ants leaving pheromone at a faster rate. Here the local information available to each ant is the rate of pheromone release from the other ants. The faster the rate, the stronger the trail. This generally corresponds to the shortest trail to the food source. Once the food source is consumed, another food source is identified and a new short path is established. This “algorithm” called as Ant Colony Optimization Algorithm is utilized by several transportation companies to find the shortest routes.

Foraging

In the show, The Walking Dead, a similar collective behavior is shown by the zombies. The zombies exhibit a herding behavior where a large number of zombies will move together as a herd in search for “food”. The zombies in The Walking Dead world are devoid of any intelligence and there is no one in charge similar to the ants. The zombies however do not have a nest. They just wander around. The zombies in the show are attracted by sound, movement and possibly absence of “zombie smell”. The zombies do not attack each other possibly due to the presence of “zombie smell”. In fact, in the show several characters were able to survive zombie attack by lathering themselves in the “zombie goo”.

The possible explanation for the formation of herd structures is the hardwired attribute that we all have – copying others. We tend to follow what others are doing when we are not sure what is happening. We go with the flow. A good example is the wave we do in a sports stadium. We could develop a model where a few zombies are attracted by a stimulus and they walk toward the stimulus. The other zombies simply follow them, and soon a large crowd forms due to the reinforced loops with more and more followers. This is similar to the positive reinforcing feedback of pheromone trail in the example of ants.

The show recently introduced an antagonist group called the “Whisperers”. The Whisperers worship the dead and adorn the zombie skins and walk amongst the zombies. They learned to control the herd and make them go where they want. The Whisperers themselves a CAS, adapted to survive by being with the walkers. Possibly, they are able to guide the walkers by first forming a small crowd themselves and then getting more walkers to join them as they move as a group. Since they have the “zombie smell” on them, the walkers do not attack them.

How Does Understanding CAS Help Us?

We are not ants and certainly not zombies (at least not yet). But there are several lessons we can get from understanding CAS. We all belong to a CAS at work, and in our community. The underlying principle of CAS is that we live in a complex world where we can understand the world only in the context of our environment and our local interactions with our neighbors and with the environment. Every project we are involved in is new and not identical to any previous project. This could be the nature of the project itself, or the team members or the deadlines or the client. Every part of the project can introduce a new variation that we did not know of. Given below are some lessons from CAS.

  1. Observe and understand patterns:

Complex Adaptive Systems present patterns due to the agents’ interactions. You have to observe and understand the different patterns around you. How do others interact with each other? Can you identify new patterns forming in the presence of new information or perturbations in your environment? Improve your observation skills to understand how patterns form around you. Look and see who the “influencers” are in your team.

  1. Understand the positive and negative feedback loops:

Observe and understand the positive and negative feedback loops that exist around. A pattern forms based on these loops. The awareness of the positive and negative loops will help us nurture the required loops.

  1. Be humble:

Complexity is all around us and this means that we lack understanding. We cannot foresee or predict how things will turn out every time. Complex systems are dispositional, to quote Dave Snowden. They may exhibit tendencies but we cannot completely understand how things work in a complex system. Edicts and rules do not always work and they can have unintended consequences. Every event is possibly a new event and this means that although you can have insights from your past experiences, you cannot control the outcomes. You cannot simply copy and paste because the context in the current system is different.

  1. Get multiple perspectives always (reality is multidimensional and constructed):

Get multiple perspectives. To quote the great American organizational theorist, Russell Ackoff, “Reality is multidimensional.” To add to this, it is also constructed. The multiple perspectives help us to understand things a little better and provide a new perspective that we were lacking. Systems are also constructed and can change how it appears depending on your perspective.

  1. Go inside and outside the system:

We cannot try to understand a system by staying outside it all of the time. Similarly, we cannot understand a system by staying inside it all of the time. Go to the Gemba (the actual workplace) to grasp the situation to better understand what is going on. Come away from it to reflect. We can understand a system only in the context of the environment and the interactions going on.

  1. Have variety:

Similar to #4, variety is your friend in a complex system. Variety leads to better interactions that will help us with developing new patterns. If everybody was the same then we would be reinforcing the same idea that would lack the requisite variety to counter the variety present in our environment. Our environment is not homogenous.

  1. Aim for Effectiveness and not Efficiency:

In complex systems, we should aim for effectiveness. Here, the famous Toyota heuristic, “Go slow to go fast” is applicable. Since each event is novel, we cannot aim for efficiency always.

  1. Use Heuristics and not Rules:

Heuristics are flexible and while rules are rigid. Rules are based on past experiences and lack the variety needed in the current context. Heuristics allow flexing allowing for the agents to change tactics as needed.

  1. Experiment frequently with safe to fail small experiments:

As part of prodding the environment, we should engage in frequent and small safe to fail experiments.  This helps us improve our understanding.

  1. Understand that complexity is always nonlinear, thus keep an eye out for emerging patterns:

Complexity is nonlinear and this means that a small change can have an unforeseen and large outcome. Thus, we should observe for any emerging patterns and determine our next steps. Move towards what we have identified as “good” and move away from what we have deemed as “bad”. Patterns always emerge bottom-up. We may not be able to design the patterns, but we may be able to recognize the patterns being developed and potentially influence them.

Final Words:

My post has been a very simple look at CAS. There are lot more attributes to CAS that are worth pursuing and learning. Complexity Explorer from Santa Fe institute is a great place to start. I will finish with a great quote from the retired United States Army four-star general Stanley McChrystal, from his book, Team of Teams:

“The temptation to lead as a chess master, controlling each move of the organization, must give way to an approach as a gardener, enabling rather than directing. A gardening approach to leadership is anything but passive. The leader acts as an “Eyes-On, Hands-Off” enabler who creates and maintains an ecosystem in which the organization operates.”

Always keep on learning…

In case you missed it, my last post was Conceptual Metaphors in Lean:

Know Your Edges:

jigsaw

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 certainty. 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: