Cybernetic Explanation, Purpose and AI:

In today’s post, I am following the theme of cybernetic explanation that I talked about in my last post – The Monkey’s Prose – Cybernetic Explanation. I recently listened to the talks given as part of the Tenth International Conference on Complex Systems. I really enjoyed the keynote speech by the Herb. A. Simon award winner, Melanie Mitchell. She told the story of a project that her student did where the AI was able to recognize whether there was an animal in a picture or not with good accuracy. Her student dug deep into the AI’s model. The AI is taught to identify a characteristic by showing a large number of datasets (in this case pictures with and without animals). The AI is shown which picture has an animal and which picture does not. The AI comes up with an algorithm based on the large dataset.  The correct answers reinforce the algorithm, and the wrong answers tweaks the algorithm as needed with the assigned weights to the “incorrectness”. This is very much like how we learn. What Mitchell’s student found was that the AI is assigning probabilities based on whether the background is blurry or not. When the background is blurry, it is more likely that there is an animal in the picture. In other words, it is not looking for an animal, it is just looking to see whether the background is blurry or not. Depending upon the statistical probability, the AI will answer that there is or there is not an animal in the picture.

We, humans, assign the meaning to the AI’s output, and believe that the AI is able to differentiate whether there is an animal in the picture or not. In actuality, the AI is merely using statistical probabilities of whether the background is blurry or not. We cannot help but assign meanings to things. We say that nature has a purpose, or that evolution has a purpose. We assign causality to phenomenon. It is interesting to think about whether it truly matters that the AI is not really identifying the animal in the picture. The outcome still has the appearance that the AI is able to tell whether there is an animal or not in the picture. We are able to bring in more concepts that the AI cannot. Mitchell discusses the difference between concepts and perceptual categories. What the AI is doing is constructing perceptual categories that are limited in nature, whereas what we construct are concepts that may be linked to other concepts. The example that Mitchell provided was that of a bridge. For us, a bridge can mean many things based on the linguistic application. We can say that a person is able to “bridge the gap” or that our nose has a bridge. The capacity for AI, at this time at least, is to stick to the bridge being a perceptual category based on the context of the data it has. We can talk in metaphors that the AI cannot understand. A bridge can be a concept or an actual physical thing for us. For a simple task such as the question of an animal in the picture carries no risk. However, as we up the ante to a task such as autonomous driving, we can no longer rely on the appearances of the AI being able to carry out the task. This is demonstrated in the morality or ethics debate with regards to AI, and how it should carry out probability calculations in the event of a hazard. This involves questions such as the ones in the trolley problem.

This also leads to another idea that has the cybernetic explanation embedded in it. This is the idea of “do no harm”. The requirement is not specifically to do good deeds, but to not do things that will cause harm to others. As the English philosopher, John Stuart Mill put it:

That the only purpose for which power can be rightfully exercised over any member of a civilized community, against his will, is to prevent harm to others.

 This is also what Isaac Asimov referred to as the first of the three laws of robotics in his 1942 short story, Runaround:

  1. A robot may not injure a human being or, through inaction, allow a human being to come to harm.

The other two laws are circularly referenced to the first law:

2. A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.

3. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.

The idea of cybernetic explanation gives us another perspective to purpose and meaning. Our natural disposition is to assign meaning and purpose, as I indicated earlier. We tend to believe that Truth is out there or that there is an objective reality. As the great Cybernetician Heinz von Foerster put it – “The environment contains no information; the environment is as it is”. Truth or descriptions of reality is our creation with our vocabulary. And most importantly, there are other beings describing realities with their vocabularies as well. I will finish with some wise words from Friedrich Nietzsche.

“It is we alone who have devised cause, sequence, for-each-other, relativity, constraint, number, law, freedom, motive, and purpose; and when we project and mix this symbol world into things as if it existed ‘in itself’, we act once more as we have always acted—mythologically.”

Please maintain social distance and wear masks. Stay safe and Always keep on learning…

In case you missed it, my last post was The Monkey’s Prose – Cybernetic Explanation:

The Monkey’s Prose – Cybernetic Explanation:

Imagine that you are on your daily walk in the park. You see a monkey on a park bench, busily typing away. You become curious as to what is happening. You slowly approach him from behind, and try to see what is being typed on the paper. Strange enough, what you see typed on the paper so far is legible prose; complete with grammar and semantics. What could be an explanation for this phenomenon?

This example was given by the great anthropologist cybernetician, Gregory Bateson. He used the example to explain “cybernetic explanation”, as he termed it. He said:

Causal explanation is usually positive. We say that billiard ball B moved in such and such a direction because billiard ball A hit it at such and such an angle. In contrast to this, cybernetic explanation is always negative… In cybernetic language, the course of events is said to be subject to restraints, and it is assumed that, apart from such restraints, the pathways of change would be governed only by equality of probability. In fact, the “restraints” upon which cybernetic explanation depends can in all cases be regarded as factors which determine inequality of probability If we find a monkey striking a typewriter apparently at random but in fact writing a meaningful prose, we shall look for restraints, either inside the monkey or inside the typewriter… Somewhere there must have been a circuit which could identify error and eliminate it.

Bateson’s use of the word “restraints” is comparable to “constraints”. Larry Richards notes that Bateson used the term “restraint” referring to the approach of Cybernetics as “negative explanation”, focusing on what is not desirable, rather than what is. When there are no constraints, we can say that all events are equally likely. If we have enough chances, we will see at least one event, where a monkey can type out a work of Shakespeare (sometimes referred to as Infinite Monkey theorem). But here, we are looking at cybernetic phenomenon where constraints are present, and they guide the outcome. In the case of the monkey’s prose, one possibility could be that the typewriter is programmed in such a fashion that no matter what key is pressed, a preprogrammed prose is generated. This would be an example of a circuit that Bateson referred to.

Let’s consider another example. Let’s say that every hour you take two measurements, measurement A and measurement B. What you find is that measurement A goes up and down, while measurement B remains fairly steady. From this dataset, what correlation can you determine between A and B? Without any additional knowledge, the general consensus would that there is no correlation between the two measurements. What if we consider the mechanism of a thermostat? The thermostat does not turn ON until the temperature goes outside a tight range. Only when the temperature goes outside the range does the thermostat turn ON. It maintains the internal temperature of the house based on how the external temperature impacts the internal temperature. In the example above, the external temperature was A and the internal temperature was B. Without a knowledge of thermostat, if we were given just the two datasets, we would not be able to see any connection between the two datasets. This idea is sometimes referred to Friedman’s Thermostat after the American economist, Milton Friedman.

The thermostat is a very basic example of cybernetic explanation. Even though, we may perceive that the thermostat’s goal is to maintain the room temperature at a constant value, the thermostat does not have a goal per se. It does not stay ON to ensure that the temperature is maintained at a constant value. Instead, it turns ON when the temperature goes outside a limit. The thermostat negatively “moves away” from the outside range value of the temperature and stays ON until it is inside a determined range. The thermostat acts only when it hits a constraint or it is guided by the restraint, to use Bateson’s language. It is not a movement towards a goal temperature of say 70 degrees F, but rather a movement away from a current temperature of say 68 degrees F. Larry Richards explained this wonderfully:

Any system with constraints appears to have a purpose as there are outcomes precluded from the set of possibilities. 

Another example we can consider is that of driving a car. When you drive a car, you apply gas or brake only when needed. You don’t steer the car to try to keep it running in a straight line. You engage when the car is moving towards the edges of your lane. To continuously work towards a goal requires high energy, and a person driving is not suitable for this.

This idea of cybernetic explanation brings forth valuable insights when we look at social systems such as an organization. Richards proposes that assigning or designing a purpose for a social system can lead to problems.

I suggest avoiding or suspending… the idea of purpose. The idea of teleological systems – that systems have a purpose first, with structure following – implies that systems are created or evolve to achieve a goal or objective.

The problem in Second Order Cybernetics arises when the observers/designers specify the purpose of their designs, giving conscious intent to their actions. Gregory Bateson (1972a, 1972b) warned of the dysfunctions of conscious purpose when the actions taken do not and cannot account for all the ecological circularities of the situation and the unanticipated consequences inherent in taking such actions. Yet, humans have needs, desires, preferences and values; we are self-aware of our actions and alternatives; and, we can act with intent to satisfy our needs and desires. To act without self-awareness of our desires and the possible consequences of our actions would be irresponsible. 

 Richards advises to look for present constraints that guide actions.

Specifying a set of constraints treats desires as a spatial concept, focusing attention on the states we wish to exclude from happening, leaving open a space of possible outcomes deemed currently acceptable. This approach is present-oriented, merging ends and means: the set of constraints that represent our desires and the actions we take to avoid what we do not want are here and now, and our evaluation of possible consequences is based on current best available knowledge. Our desires, actions and evaluations can change as we experiment, learn and change, making it important to be careful about excluding outcomes that could become useful as circumstances change. Treating desires as constraints and intention as an awareness of desires as constraints opens the door for an alternative to the consciousness of purpose about which Bateson was concerned.

The idea of cybernetic explanation and constrains raise the importance of dialogue amongst the coparticipants of the social realm. Rather than going after a narrow purpose, we may be better served if we can explore the space of constraints to identify conditions that promote outcomes that we desire. When we utilize a constancy of purpose, we are utilizing a narrow view that is not able to accommodate the various interpretations and desires of the many coparticipants of our social realm. Bateson viewed the pursuit of conscious purpose as being damaging to the very ecology that supports being human. (Klaus Krippendorff). Krippendorff came out with an Empirical Imperative to support this idea:

Empirical Imperative: Invent as many alternative constructions as you can and actively explore the constraints on their affordances.

I will finish with more wise words from Richards that provides further insights about cybernetic explanation:

If I know what I want and I know it is possible to achieve it, I do not need cybernetics—I just go and do what I need to do to achieve the outcome. However, when I only have a vague idea about what I want or do not want and I do not know how to pursue or avoid it in the current society, the vocabulary of cybernetics can be useful. Cybernetics is not about success and the achievement of goals; it is about the reconfiguration of constraints (resources) in order to make possible what was not previously possible, including the avoidance of what was previously inevitable. 

Please maintain social distance and wear masks. Stay safe and Always keep on learning…

In case you missed it, my last post was Complexity – Only When You Realize You Are Blind, Can You See:

Complexity – Only When You Realize You Are Blind, Can You See:

In today’s post, I am looking at the idea of complexity from a second order Cybernetics standpoint. The phrase “only when you realize you are blind, can you see”, is a paraphrase of a statement from the great Heinz von Foerster. I have talked about von Foerster in many of my posts, and he is one of my heroes in Cybernetics. There is no one universally accepted definition for complexity. Haridimos Tsoukas and Mary Jo Hatch wrote a very insightful paper called “Complex Thinking, Complex Practice”. In the paper, they try to address how to explain complexity. They refer to the works of John Casti and C. H. Waddington to further their ideas:

Waddington notes that complexity has something to do with the number of components of a system as well as with the number of ways in which they can be related… Casti defines complexity as being ‘directly proportional to the length of the shortest possible description of [a system]’.

Casti’s views on complexity are particularly interesting because complexity is not viewed as being intrinsic to the phenomenon. This is a common idea in Cybernetics, mainly second order cybernetics. There are two ‘classifications’ of cybernetics – first order and second order cybernetics. As von Foerster explained it, first order cybernetics is the study of observed systems, where the basic assumption is that the system is objectively knowable. The second order cybernetics is the study of observing systems, where the basic assumption is that the observer is included in the act of observing, and thus the observer is part of the observed system. This leads to second order thinking such as understanding understanding or observing observing. It is interesting because, as I am typing, Microsoft Word is telling me that “understanding understanding” is syntactically incorrect. This obviously would be a first order viewpoint. The second order cybernetics is a meta discipline and one that generates wisdom.

When we take the observer into consideration, we realize that complexity is in the eyes of the beholder. Complexity is observer-dependent; that is, it depends upon how the system is described and interpreted. If the observer is able to make more varying distinctions in their description, we can say that the phenomenon or the system is being interpreted as complex. In their paper, Tsoukas and Jo Hatch brings up the ideas of language in describing and thus interpreting complexity. They note that:

Chaos and complexity are metaphors that posit new connections, draw our attention to new phenomena, and help us see what we could not see before (Rorty).

This is quite interesting. When we learn the language of complexity, we are able to understand complexity better, and we become better at describing it, in a reflexive manner.

What complexity science has done is to draw our attention to certain features of systems’ behaviors which were hitherto unremarked, such as non-linearity, scale-dependence, recursiveness, sensitivity to initial conditions, emergence (etc.)

From this standpoint, we can say that complexity lies in the interactions we have with the system, and depending on our perspectives (vantage point) and the interaction we can come away with a different interpretation for complexity.

Heinz von Foerster remarked that complexity is not in the world but rather in the language we use to describe the world. Paraphrasing von Foerster, cognition is computation of descriptions of reality. Managing complexity then becomes a cognitive task. How well you can interact or manage interactions depends on how effective your description is and how well it aligns with others’ descriptions. The complexity of a system lies in the description of that system, which entirely rests on the observer/sensemaker. The idea that complexity is in the eyes of beholder is to point out the importance of second order cybernetics/thinking. The world is as it is, it gets meaning only when we assign meaning to it through how we describe/interpret it. To put differently, “the logic of the world is the logic of the descriptions of the world” (Heinz von Foerster)

The idea of complexity not being intrinsic to a system is also echoed by one of the pioneers of cybernetics, Ross Ashby. He noted – “a system’s complexity is purely relative to a given observer; I reject the attempt to measure an absolute, or intrinsic, complexity; but this acceptance of complexity as something in the eye of the beholder is, in my opinion, the only workable way of measuring complexity”.

The ideas of second order cybernetics emphasize the importance of observers. The “system” is a mental construct by an observer to make sense of a phenomenon. The observer based on their needs draw boundaries to separate a “system” from its environment. This allows the observer to understand the system in the context of its environment. At the same time, the observer has to understand that there are other observers in the same social realm who may draw different boundaries and come out with different understandings based on their own needs, biases, perspectives etc.

A phenomenon can have multiple identities or meanings depending on who is describing the desired phenomenon. Let’s use the Covid 19 pandemic as an example. For some people, this has become a problem of economics rather than a healthcare problem, while for some others it has become a problem of freedom or ethics. If we are to attempt tackling the complexity of such an issue, the worst thing we can do is to attempt first order thinking- the idea that the phenomenon can be observed objectively. Issues requiring second order approach get worse by the application of first order methodologies. The danger in this is that we can fall prey to our own narrative being the whole Truth.

As the pragmatic philosopher Richard Rorty points out:

The world does not speak. Only we do. The world can, once we have programmed ourselves with a language, cause us to hold beliefs. But it cannot propose a language for us to speak. Only other human beings can do that.

If we are to understand complexity of a phenomenon, we need to start with realizing that our version of complexity is only one of the many.  Our ability to understand complexity depends on our ability to describe it. We lack the ability to completely describe a phenomenon. The different descriptions that come about from the different participants may be contradictory and can point out apparent paradoxes in our social realm.

In complexity, if we are to tackle it, we need to have coherence of multiple interpretations. As Karl Weick points out, we need to complicate ourselves. By generating and accommodating multiple inequivalent descriptions, practioners will increase the complexity of their understanding and, therefore, will be more likely to match the complexity of the situation they attempt to manage. In complexity, coherence – the idea of connecting ideas together, is important since it helps to create a clearer picture and affords avoiding blind spots. This co-construted description itself is an emergent phenomenon.

In second order Cybernetics, there are two statements that might shed more light on everything we have discussed so far:

Anything said is said BY an observer. (Maturana)

Anything said is said TO an observer. (von Foerster)

A lot can be said between these two statements. The first points out that the importance of the observer, and the second points out that there are other observers, and we coconstruct our social reality.

Our descriptions are abstractions since we are limited by our languages. All our biases, fears, misunderstandings, ignorance etc. lie hidden in the “systems” we construct. We get into trouble when we assume that these abstractions are real things. This is the first order approach, where we are not aware that we do not see due to our cognitive blind spots. When we realize that all we have are abstractions, we get to the second order approach. We include ourselves in our observation and we start looking at how we make these abstractions. We also become aware of other autonomous participants of our social reality engaging in similar constructions of narratives. As we seek their understanding, we become aware of our cognitive blind spots. We realize that everything is on a spectrum, and our thinking of “either/or” is actually a false dichotomy.

At this point, Heinz von Foerster would say that we start to see when we realize that we are blind.

Please maintain social distance and wear masks. Stay safe and Always keep on learning…

In case you missed it, my last post was Causality and Purpose in Systems:

Causality and Purpose in Systems:

In today’s post, I am pursuing the ideas from my last two posts. I am going to look at purposiveness and purposefulness in systems, and I am going to discuss ideas inspired by Aristotle and Werner Ulrich. Aristotle was Plato’s student, and a polymath. He was the first Western philosopher to provide a framework for causality. Aristotle noted that things are always changing or are in motion. He proposed that matter (things) exists as forms. Matter moves through forms, from simple to complex, similar to an evolutionary process, until it meets its final form. Thus, for Aristotle change is not meaningless. This is the teleological view where every thing is moving towards its higher purpose. He explained this in terms of potentiality and actuality. The current state of the matter represents the potentiality. Once the current state is transformed so that it is in a new form and the desired purpose is achieved, the matter has achieved actuality.

A simple example is that of a stone. The stone has potentiality, and once it becomes a statue, as the artist intended, it meets its actuality. We can imagine matter going through a series of forms. Matter represents possibility (potentiality) and form represents reality (actuality). The change continues until change itself is unnecessary. This also sheds light on purpose. The purpose of a thing is to fulfill its potential. The potentiality represents its purpose. For an organism, the purpose is the realization of its form. For example, the purpose for a seed is to grow into a tree. This also can be viewed as a constraint in the sense that the seed has no other choice but to become a tree. Here the causality is to unfold what is already enfolded.

We may take these ideas for granted, but these were the groundbreaking ideas on which we built the foundations of science on. When we look at the ideas of Aristotle, we see that he didn’t include an observer in the mix. His view is that of an empiricist, one who believes that knowledge is possible from experiencing the real world. For him, knowledge is derived from an objective reality. Let’s take the example of a stone and a sculptor. The purpose of the stone was provided by the sculptor, since it was him who provided “information” to form the statue. The change happened because of the sculptor. We can state that the change was the actualization of potential through the information provided by the sculptor. This is an important example to bear in mind as we proceed further into this post.

We cannot help but draw similarities between the sculptor and a designer of a human system such as an organization. The sculptor provided the information for the stone to change into a beautiful statue. The designer can be viewed as providing a blue print for the organization to form into an adaptive and agile organization. This viewpoint would be true if we were talking about purposive systems. Purposiveness, as explained by Ulrich, refers to the effectiveness and efficiency of means or tools: in other words, cogs in the machines. This is the mechanistic framework, where the designer is the expert who assigns purposes for each part of the system. However, when we look at social systems or human systems, we need to consider purposefulness. Ulrich viewed purposefulness as the critical awareness of self-reflective humans with regard to ends or purposes and their normative implications for all of those who might be affected by their consequences. Ulrich succinctly summarized the idea of purposive human systems in the statement – all design of tools represents somebody’s solution to somebody’s problems. Purposefulness aligns with intrinsic motivation compared to an extrinsic motivation provided by the designer. Humans are purposeful, and although we are able to follow orders, we will not be able to actualize our self-potential. At some point, we may decide to not follow orders anymore. We should be able to provide purpose for ourselves and actualize the potential the way we deem fit. When we consider using a mechanistic framework on human systems, it is good to remind ourselves of Geert Hofstede’s quote: “as soon as people are part of the process, the effects of interventions are not known.”

Another point to consider with treating organizations as purposive systems is that the designer lacks the variety to deal with all of the variety the environment might impose on the organization. Thus, if the designer had a form in mind to tackle a particular need to be met, the structure has to follow the form that it is constrained to. For example, if the designer planned for the organization to produce only black cars, and suddenly there is a need for ventilators, theoretically the purposive system will not be able to meet that need. The structure of the organization is constrained to produce only black cars. The designer has to then intervene to change the form again, to use Aristotle’s idea, so that the organization can now produce ventilators. This approach gets messy and murky fast when the number of demands increase and the designer is not able to match the variety needed. Ideally, the recursions should have enough autonomy at their levels to meet the requisite variety needed.

We cannot help but fall into the trap of anthropomorphism when it comes to talking about organizations. We may talk about the organizations having goals or that organization can self-organize or be agile. We are forgetting that organizations do not have goals; some people in the organizations do. There is no one mind or self-conscious entity having a purpose of its own or moving towards the goal of self-actualization. It is actually a fairly delicate balance. The idea of causality does not apply to human systems. We should stop thinking in terms of causality, rules etc., and instead think in terms of constraints, dispositions etc.

Geoffrey Vickers, an eminent British Systems Thinker, talks about resisting our urge to view organizations/social systems in terms of “systems”. One underlying theme in Systems Thinking is that the whole is more important than the parts. This brings into question – who is defining the whole? Systems are theoretical constructs rather than real entities in the world. This is the idea that the systematicity is not in the real world, but in how we view the world. Vickers realized that the very word “systems” had become dehumanized. As Peter Checkland notes (with some paraphrasing):

He (Vickers) rejected the goal-seeking model of human life (the core of management science) and then the cybernetic model because in it, the course to which the steersman steers is a given from the outside the system; whereas in human affairs the course being followed is continuously generated and regenerated from inside the system. This led him to his notion of appreciation in which, both individually and in groups, we all do the following: selectively perceive our world; make judgements about it, judgments of both fact (what is the case?) and value (is this good or bad, acceptable or unacceptable?); envisage acceptable forms of the many relationships we have to maintain over time; and to act to balance those relationships in line with our judgments.

Another good quote to further this idea comes from Espejo and Harnden:

A model is a convention – a way of talking about something in a manner that is understandable and useful in a community of observers. It is not a description of reality, but a tool in terms of which a group of observers in a society handle the reality they find themselves interacting with.

The idea that humans are purposeful and yet they belong to a purposeful system posits the importance of continuous self-reflection and self-correction from the part of a manager. This also needs a second-order approach to improve our understanding. We have to evaluate how we are part of the system we observe. In the wise words of Heinz von Foerster, we have to decide if we are apart from the system we are looking at or if we are a part of the system we are looking at. We must be aware of the blind spots we have, or as Ulrich refers to them – all conceivable sources of possible deception.

Another important point to keep in mind with social systems is the interconnectedness (which also points toward the complexity of social systems). As West Churchman, Ulrich’s mentor and teacher, points out – “in any specific problem one finds the connectedness to all the other problems”. Ulrich points out that the overwhelming connectedness of problems forces systems designers, no less than any other planners, to content themselves with partial solutions that consider only a limited number of whole systems implications – usually those of interest to the involved decision-makers. That’s always the rub. Everything is connected with everything else and yet we try to make sense by cutting off the majority of the connections and we don’t see ourselves being a part of the phenomenon we are looking at. This is also why objective reality is not a good viewpoint to hold. It forces mechanistic frameworks and reductionist ideas that are not suitable for social systems. The crucial issue, then, is no longer “What do we know?” but rather “How do we deal with the fact that we don’t know enough?” In particular, uncertainty about the whole systems implications of our actions does not dispense us from moral responsibility; hence, “the problem of systems improvement is the problem of the ‘ethics of the whole system’.”

When we are talking about social systems, we need to realize that we cannot simply view the humans as “parts”. This forces us to immediately consider the ethical viewpoints. As Ulrich points out, any systems concept that does not include the intrinsic purposefulness ultimately falls back upon a machine model of social systems. Tools have purposes and are purposive. People are not tools, and nor are they purposive. The purposiveness of tools depends on the purposefulness of people using the tools. Purpose only makes sense when you talk about yourself.

I will finish with Ralph Stacey’s wise words:

When one moves away from thinking that one has to manage the whole system, one pays attention to one’s own participation in one’s own local situation in the living present. Perhaps this humbler kind of “management” is what the “knowledge society” requires.

Please maintain social distance and wear masks. Stay safe and Always keep on learning…

In case you missed it, my last post was The Conundrum of Autonomy in Systems:

The Conundrum of Autonomy in Systems:

In my previous post, I talked about the idea of the Copernican revolution in philosophy by Immanuel Kant. In today’s post, I am expanding upon the ideas originated by Kant, especially autonomy and how it poses challenges in how we view human systems. I am also heavily relying on the ideas of Ralph Stacey. Kant had a lot to say about human autonomy. Autonomy stands for the ability to set laws for oneself or the ability to perform actions that are not directed by someone else. Kant viewed humanity as an end in itself and not a means to an end. Humans should not be used simply as a means to get something done. Humans, Kant noted, have the power to act according to their own conception of laws.

Kant was one of the pioneers of systems thinking. He understood the idea of circular causality and self-organization. Kant proposed that all living beings can be viewed as self-organizing systems rather than mechanisms such as a clock. The idea of a self-organizing system meant that the idea of feedback is important. However, Kant made an important distinction when it came to human beings. He proposed that humans cannot be understood as merely being a part of the “system” of nature. For this he used some ideas from Aristotle. Kant noted that all other living beings follow a formative causality, where the structure determines the unfolding of the living being itself. For example, a tree follows the unfolding of their lifecycle from a seed. The same formative causality is applicable to the human body; however, this is not applicable to the human being as a whole who has autonomy. This is beautifully explained by Ralph Stacey:

Humans are part of nature but if nature is governed by fixed mechanistic and systemic laws, then they cannot have any freedom to make their own choices… the body is subject to the fixed laws of nature but the mind is governed by the laws of reason, rationalist causality, and it is reason that makes us free. Kant was here formulating the theory of autonomous, rational individual who chooses goals and actions required to achieve them on the basis of reason. Kant then stressed that autonomous individuals could not be understood as parts of a whole because then they would be subject to the whole and so lose their autonomy. The notion of a system could, therefore, not be applied to reasoning individuals and it would not be valid to regard society as a system whose parts were individuals.

The idea of structure determining the outcome is a prevalent theme in many schools of Organizational Management. However, the idea of humans as being rule-following parts of the “system” should be challenged. In the light of the understanding that we are autonomous individuals with many self-imposed purposes and needs, the mechanistic view of an organization system based on structure falls apart. The “human body” may be viewed as a system, however a human being cannot be viewed as a system or being a part of a system.

The notion of Systems Thinking as being a study of real systems that can be observed objectively is still prevalent. This view suggests ideas such as learning organization or complex adaptive systems. Stacey again provides wisdom in this aspect:

For me, the claim that organizations learn amounts to both reification and anthropomorphism. I argue that organizations are not things because no one can point to where an organization is –all one can point to is the artefacts used by members of organizations in their work together. In our experience, the organization qua organization arises as the patterning of our interactions with each other… Since an organization is neither inanimate thing nor living body, in anything other than rather fanciful metaphorical terms, it follows that an organization can neither think nor learn.

The conundrum of autonomy also brings the important point that objective reality is not possible. The idea that a manager can objectively view the organization by being outside the organization must be reevaluated. This notion implies that the manager can use scientific thinking and identify rules to implement to optimize the organization. But this again utilize the idea that humans can be viewed as mere parts of a system. Stacey cautions us against this:

Management science equates the manager with the scientist and the organization with the mechanistic phenomenon that the scientist is concerned with. The manager’s main concern is with getting the right “if-then” causal rules. There is a quite explicit assumption that there is some set of rules that are optimal, that is, that produce the most efficient global outcome of the actions of the parts, or members, of the organization. There is an important difference between the scientist concerned with nature and the analogous manager concerned with an organization. The scientist discovers the laws of nature while the manager, in the theory of management science, chooses rules driving the behavior of organization’s members. In this way, there is rationalist causality, but it applies only to the manager who exercises the freedom of autonomous choice in the act of choosing the goals and designing the rules that the members of the organization are to follow in order to achieve the goals. Those members are assumed to be rule-following entities. The organizational reality, of course, is that members of an organization are not rule-following entities and they all do choose their own goals and actions to some extent.

Final Words:

Edgar Morin wonderfully noted that the autonomy of a system is less than the sum of autonomies of all the individual parts of a system. The idea that humans should not be viewed as being parts of a system should challenge your current view points on systems thinking. Kant proposed that we are using an as-if metaphor to construct reality since we do not have access to the external reality. From this standpoint, we can notate that systems are not real entities in the real world. Humans are autonomous and this means that we cannot stipulate purposes for other people. The freedom of the employee puts a constraint on the organization, and the freedom of the organization puts a constraint on the employee. This requires an ongoing reinterpretation and adjustment of intentions and values at all levels of recursions in an organization. This is not a conundrum to be solved. It is a creative tension that should be reinterpreted as often as possible.

I will finish with a Zen story:

A man is riding on top of a horse that is galloping by frantically, as if he has to be somewhere important, as soon as possible. A bystander sees this and asks the man, “Where are you going?

“I don’t know,” the rider replies, “ask the horse!

Wear a mask, stay safe and Always keep on learning…

In case you missed it, my last post was Copernican Revolution – Systems Thinking:

Copernican Revolution – Systems Thinking:

In today’s post, I am looking at “Copernican Revolution”, a phrase used by the great German philosopher, Immanuel Kant. Immanuel Kant is one of the greatest names in philosophy. I am an Engineer by profession, and I started learning philosophy after I left school. As an Engineer, I am trained to think about causality in nature – if I do this, then that happens. This is often viewed as the mechanistic view of nature and it is reliant on empiricism. Empiricism is the idea that knowledge comes from experience. In contrast, at the other end of knowledge spectrum lies rationalism. Rationalism is the idea that knowledge comes from reason (internal). An empiricist can quickly fall into the trap of induction, where you believe that there is uniformity in nature. For example, if I clapped my hand twenty times, and the light flickered each time, I can then (falsely) conclude that the next time I clap my hand the light will flicker. My mind created a causal connection to my hand clapping and the light flickering.

David Hume, another great philosopher, challenged this and identified this approach as the problem of induction. He suggested that we, humans, are creatures of habit that we assign causality to things based on repeat experience. His view was that causality is assigned by us simply by habit. His famous example of challenging whether the sun will rise tomorrow exemplifies this:

That the sun will not rise tomorrow is no less intelligible a proposition, and implies no more contradiction, than the affirmation, that it will rise.

Hume came up with two main categories for human reason, often called Hume’s fork:

  1. Matters of fact – this represents knowledge that we gain from experience (synthetic), and this happens after the fact of experience (denoted by posteriori). An example is – the ball is heavy. Thinking cannot provide the knowledge that the ball is heavy. One has to interact with the ball to learn that the ball is heavy.
  2. Relation of ideas – this represents knowledge that does not rely on experience. This knowledge can be obtained simply through reason (analytic). This was identified as a priori or from before. For example – all bachelors are unmarried. No experience is needed for this knowledge. The meaning of unmarried is predicated in the term “bachelor”.

All the objects of human reason or enquiry may naturally be divided into two kinds, to wit, relations of ideas, and matters of fact. Of the first kind are the sciences of Geometry, Algebra, and Arithmetic … [which are] discoverable by the mere operation of thought … Matters of fact, which are the second object of human reason, are not ascertained in the same manner; nor is our evidence of their truth, however great, of a like nature with the foregoing.

Hume’s fork stipulates that all necessary truths are analytical, the meaning is predicated in the statement. Similarly, knowledge regarding matters of fact indicate that the knowledge is contingent on the experience gotten from the interaction. This leads to further ideas such as – there is a separation between the external world and the knowledge about the world. The knowledge about the world would come only from the world through empiricism. One can view this as the human mind revolving around the world.

Immanuel Kant challenged the idea of Hume’s fork and came up with the idea of a priori synthetic knowledge. Kant proposed that we, humans, are bestowed with a framework for reasoning that is a priori and yet synthetic. Kant synthesized ideas from rationalism and empiricism, and added a third tine to Hume’s fork. Kant famously stated – That all our knowledge begins with experience there can be no doubt. Kant clarified that it does not follow that knowledge arises out of experience. What we come to know is based on our mental faculty. The mind plays an important role in our knowledge of the world. The synthetic a priori propositions say something about the world, and yet at the same time they say something about our mind.

How the world is to us depends on how we experience it, and thus the knowledge of the external world is dependent on the structure of our mind. This idea is often described as a pair of spectacles that we are born with. We see the world through this pair of spectacles that we cannot take off. What we see forms our knowledge of the world, but it is dependent on the pair of spectacles that is a part of us. Kant’s great idea is that our knowledge of the world does not conform to the world. Our knowledge of the world conforms not to the nature of the world, but to the nature of our internal faculties. To paraphrase Heinz von Foerster, we do not see the world as is, it is as we see it.

Nicholas Copernicus, the Polish astronomer, came up with a heliocentric view of the world. The prevalent idea at the time was that the celestial bodies, including the sun, revolved around the earth. Copernicus challenged this, and showed that the earth actually revolves around the sun. Kant, in a similar fashion, suggested that the human minds do not revolve around the world with the meanings coming into our minds. Instead, the world revolves around our minds, and we assign meanings to the objects in the world. This is explained wonderfully by Julie. E. Maybee:

Naïve science assumes that our knowledge revolves around what the world is like, but, Hume’s criticism argued, this view entails that we cannot then have knowledge of scientific causes through reason. We can reestablish a connection between reason and knowledge, however, Kant suggested, if we say—not that knowledge revolves around what the world is like—but that knowledge revolves around what we are like. For the purposes of our knowledge, Kant said, we do not revolve around the world—the world revolves around us. Because we are rational creatures, we share a cognitive structure with one another that regularizes our experiences of the world. This intersubjectively shared structure of rationality—and not the world itself—grounds our knowledge.


We have assumed that the knowledge of the world, our cognition, conforms to the world. Kant proposes that all we have access to is the phenomena, and not the actual world. What we are learning is dependent on us. We use an as-if model to generate meaning based on our interaction with the external world. In this viewpoint, the systems are not real things in the world. The systems are concepts that we construct, and they are as-if models that we use to make sense of the phenomena. What we view as systems are the constructions we make and depends on our need for understanding.  

Alan Stewart uses a similar idea to explain his views on constructivism:

The fundamental premise of constructivism is that we humans are self-regulating organisms who live from the inside out. As a philosophical counterpoint to naive realism, constructivism suggests that we are proactive co-creators of the reality to which we respond. Underlying this concept is that perception is an active process in which we ‘bring forth distinctions’. It is our idiosyncratic distinctions which form the structure of the world(s) which each of us inhabits.”

I will finish with a great lesson from Alan Watts:

“Everything in the world is gloriously meaningless.”

To further elaborate, I will add that all meaning comes from us. From a Hume-ian sense, we are creatures of habit in that we cannot stop assigning meaning. From a Kant-ian sense we are law-makers, not law-discoverers.

From a Systems Thinking perspective, we have unique perspectives and we assign meanings based on this. We construct “systems” “as-if” the different parts work together in a way to have a purpose and a meaning, both of which are assigned by us. The meaning comes inside out, not the other way around. To further this idea, as a human collective, we cocreate an emergent phenomenal world. In this aspect, “reality” is multidimensional, and each one of us has a version that is unique to us.  

Stay safe and Always keep on learning…

In case you missed it, my last post was Hegel, Dialectics and POSIWID:

Hegel, Dialectics and POSIWID:

In today’s post, I am looking at Hegel’s dialectical approach and using it to gain a better understanding of the purpose of an organization. Georg Wilhelm Friedrich Hegel (1770 – 1831) was a German philosopher who furthered the ideas of German Idealism in Philosophy after Immanuel Kant. Hegel’s writing is quite dense and he is often considered to be one of the hardest philosophers to understand. With this introduction, I should note here that my post is “inspired” by his dialectical approach.

When we look at a phenomenon say “A”, we are speaking about our understanding of “A”. This understanding automatically brings in the opposite or “notA” to the realm of the understanding. We can denote “notA” as “!A”. Our understanding of “A” lies somewhere between “A” and “!A”. To improve our understanding of “A”, we should also look at “!A”. This is a very simple view of Hegel’s dialectic. The idea of dialectics implies that all abstract concepts are partial and contain innate contradictions. As we further our understanding of the concept, we go through a dialectic process by looking at the innate contradiction (A and !A). The new understanding can be notated as A’, which again is partial and sets off another dialectical process. Hegel’s idea of dialectical process is a holistic approach. Generally, when we speak about contradictions, we either view it as an absurdity that negates any further thought or as a pro-con discussion which leads to choosing one over the other.

Hegel’s view of dialectics has a background based in history. Hegel’s view is that the world is in a movement from one phase to the next. It goes through transformation continuously. Hegel uses this idea of movement from one end to the other for reasoning. This maybe made easier to understand by using the example of a flower bud. Hegel wrote:

The bud disappears when the blossom breaks through, and we might say that the former is refuted by the latter; in the same way when the fruit comes, the blossom may be explained to be a false form of the plant’s existence, for the fruit appears as its true nature in place of the blossom. The ceaseless activity of their own inherent nature makes these stages moments of an organic unity, where they not merely do not contradict one another, but where one is as necessary as the other; and constitutes thereby the life of the whole.

 We can look at this example with the starting point of the seed. The seed grows into a plant. The plant produces the bud, and the bud blooms into a flower, which produces the seed. Each stage brings the past stages with it. To have a good understanding we should also look at the previous stages. Any one stage cannot be viewed in isolation. Any previous stages we bring forth for our understanding is not cancelled, but kept for improving our understanding. The meaning is holistic. Hegel would state that only the truth is whole.

“The truth is the whole. The whole, however, is merely the essential nature reaching its completeness through the process of its own development. Of the Absolute it must be said that it is essentially a result, that only at the end is it what it is in very truth; and just in that consists its nature, which is to be actual, subject, or self-becoming, self-development.”

As Lloyd Spencer and Andrzej Krauze write:

For Hegel, only the whole is true. Every stage or phase or moment is partial, and therefore partially untrue. Hegel’s grand idea is ‘Totality’ – which preserves within it each of the ideas or stages it has overcome or subsumed. Overcoming or subsuming is a developmental process made up of ‘moments’. The Totality is the product of that process which preserves all of its ‘moments’ as elements in a structure, rather than as stages or phases.

The absolute state is where the dialectic movement goes towards. The absolute state has essences of all the past moments we considered. Hegel would call this as “Aufheben.” Aufheben, itself, requires a dialectical approach to understand its meaning since it contains contradictory reflections. The term is translated to English as “sublation”, and it means “to lift up” and also “to cancel”. Hegel is indicating that as we make a dialectical movement, we are preserving some aspects of the moments we are considering while at the same times negating some aspects of the moments. The dialectical movement is generally viewed to be consisting of three moments (as Julie Maybee notes):

  1. The first moment—the moment of the understanding—is the moment of fixity, in which concepts or forms have a seemingly stable definition or determination.
  2. The second moment—the “dialectical” or “negatively rational” moment—is the moment of instability.
  3. The third moment—the “speculative” or “positively rational” moment—grasps the unity of the opposition between the first two determinations, or is the positive result of the dissolution or transition of those determinations.

The common example used to explain this is that of “being <-> not-being <-> becoming”. When we think of “being” we are thinking of a total presence of a being. But to understand this idea, we should also consider the absence of that being or “not-being” or “nothingness”. A being becomes nothing at the end. Or a being comes into existence from “not-being”. This is the act of “becoming”. The idea of “becoming” has the ideas of “being” and “not-being”. As noted earlier, to better understand “A”, we also need to understand “!A”. The higher understanding seems to be an emergent property. The better understanding of “A” lies between “A” and “!A” and requires the movement from “A” to “!A” to get to the higher understanding of “A”.

Another example we can use is that of “beauty”. As Maybee notes:

The highest definition of the concept of beauty, for instance, would not take beauty to be fixed and static, but would include within it the dialectical nature or finiteness of beauty, the idea that beauty becomes, on its own account, not-beauty. This dialectical understanding of the concept of beauty can then overgrasp the dialectical and finite nature of beauty in the world, and hence the truth that, in the world, beautiful things themselves become not-beautiful, or might be beautiful in one respect and not another.

The Purpose of an Organization:

We are taught that organizations have a designed purpose, and we are taught about the constancy of purpose to be a successful organization. Let’s use the idea of a dialectical approach to look at purpose of a system. Our first moment is that Organizations have a purpose and that it is dictated by the “designer” of the Organization. The second moment comes when we realize that the organization is not a single entity but a collective. Organizations are made of humans who themselves are purposeful. The top down designed purpose may not have a meaning as it flows down the organizational chain. Thus, we come to realize that organizations do not have a purpose. Then we come to the third moment with the idea of POSIWID – the purpose of a system is what it does. As the great management cybernetician, Stafford Beer said:

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.

From the third moment, we realize that purpose is emergent and is always dynamic. Most importantly, depending upon who is the observer, the purpose will change. Stafford Beer’s Viable System Model is an excellent framework to look at this further. Beer’s model is recursive with viable systems within viable systems. The purpose is different depending upon the level of recursion and depending upon who is observing, and also when the observation is done. The schematic below was Beer’s vision of recursions from the Project Cybersyn in Chile.

An interesting example to further this understanding is the notion that the purpose is always determined by the user. The purpose is the need of the user that needs to be met at any given time. For example, the user may have multiple purposes for a screwdriver depending on the need – as a hammer, as a can opener, as a tool for tightening screws etc. The purpose is dynamic for sure. The environment always has more variety than the organization’s management. I highly encourage the readers to check out Stafford Beer’s Viable System Model.

Final Words:

Every idea is in the process of transformation, and carries with it traces of the ideas they were built on. The same can be said about us humans, individually and collectively. Hegel seems to suggest that all ideas progress towards “Geist” or “Spirit” (the Absolute Knowledge), a state of total and truthful knowledge. No further knowledge is useful or possible. This sounds like a state of maximum entropy. One could view this as – everything is in a progression towards a state of maximum Entropy similar to the ultimate universal heat death!

We need to be open and rational to pursue better understanding. The dialectic movement is possible only when we consider innate contradictions. We can also choose not to pursue the dialectic movement and assume that our current position is stable by ignoring the innate contradictions. Full or Absolute understanding is not possible since we think in abstractions, and all abstractions are partial by definition. We fail to improve our understanding when we assume that we have the “whole” knowledge.

As a note, I should state that I purposefully chose note to use the formulaic thesis-antithesis-synthesis idea since Hegel never used that to explain his ideas.

Hegel reportedly admitted to the difficulty of his ideas. He is sometimes attributed to have said, “When I Wrote It, Only God and I Knew the Meaning; Now God Alone Knows.”

On his deathbed he noted, “There was only one man who ever understood me, and even he didn’t understand me.”

To keep up with the theme, I can also offer the great British philosopher Bertrand Russel’s criticism of Hegel as the second moment:

Hegel’s philosophy is so odd that one would not have expected him to be able to get some men to accept it, but he did. He set out with so much obscurity that people thought it must be profound. It can quite easily be expounded lucidly in words of one syllable, but then its absurdity becomes obvious.

Stay safe and Always keep on learning…

In case you missed it, my last post was Shingo’s Whys:

Shingo’s Whys:

Shigeo Shingo is one of my heroes in Industrial Engineering. He had a great mind that thrived on curiosity. In today’s post, I am looking at Shingo’s whys. This is in contrast to Taiichi Ohno’s 5Why method. Ohno’s 5Why method is one of the tools in Toyota Production System to get to the root cause. When you see a problem, you ask “why did that problem happen?” When you get an answer to that question, you then ask “Why did that problem#2 happen?” and so on until you get to the root cause. When you eliminate the root cause, the problem is solved. This approach assumes a direct and linear cause and effect relationship. And depending upon the user’s expertise and experience, you can get different results. A tool like 5Why is user-dependent and one-dimensional. It is appropriate for necessary causes; it may not be appropriate for sufficient causes. Its usefulness certainly diminishes as complexity increases.

Shingo’s Whys are not in relation to Ohno’s 5Whys, but another set of questions, 5W1H. The 5W1H questions are:

  1. Who
  2. What
  3. Where
  4. When
  5. Why
  6. How

These questions are the levers you can push to further our search for answers. It is said that the origin of these questions goes back to the great Aristotle (Source: Aristotle’s Nicomachean Ethics as the Original Locus for the Septem CircumstantiaeMichael. C. Sloan). Another source where the idea of the 5W1H was stated clearly is from Thomas Aquinas:

For in acts we must take note of who did it, by what aids or instruments he did it (with), what he did, where he did it, why he did it, how and when he did it.

 The idea of 5W1H was also made famous by Rudyard Kipling:

I keep six honest serving-men

(They taught me all I knew);

Their names are What and Why and When

And How and Where and Who.

The usefulness of this simple framework is also illustrated in the Job Methods program from the Training Within Industry initiative:

Shingo viewed these as the five elements of production. He noted them as:

  1. What? (object of production)
  2. Who? (subject of production)
  3. How? (method of operation)
  4. Where? (space of production)
  5. When? (time of production)
  6. Why? (applies to all the five elements noted above)

In a simple example of producing a medical swab, perhaps the five elements of production are:

  1. What is to be produced? – the medical swab
  2. Who is producing it? – machines or workers
  3. How are we producing it? – the different operations the process goes through from raw materials to the end sterile product
  4. Where are we producing it? – space utilization; this includes the storage area at incoming, the QC lab for inspection, the storage area for inventory, the clean room for actual production, and again the storage area at the end.
  5. When? – this includes the duration and timing.

Shingo teaches us to ask “Why” to each of the five elements of production (Shingo’s whys):

  1. Why do we need this object?
  2. Why do we require this subject?
  3. Why use this kind of method?
  4. Why this kind of space utilization?
  5. Why this kind of time?

He brilliantly explained:

The five elements of production just make up the status quo. If we want to improve the present situation, we must direct the question “why?” at each one of those elements repeatedly and relentlessly.

The obvious question this would lead to is whether we can ask a “Why?” question to the “Why?” itself. I will leave this question for the reader to ponder. The questioning with “why?” gets to the actual purpose behind the reasoning or rationale of a decision. It is an effective way to get to meta-analysis, a second-order activity.

Final Words:

Shigeo Shingo learned the ideas of making improvements from another giant, Lillian Gilbreth. Shingo learned from Ken’ichi Horigome, who learned from Jiro Kakuka. Jiro Kakuda learned the concepts and techniques of improvement at Gilbreth’s institute in the United States. Shingo wonderfully summarized the Gilbreth approach as (the emphasis is mine):

  1. Analyze the facts in detail
  2. Pursue work goals by asking the question “why?” at least three times
  3. Bear in mind that there are several means to any one goal
  4. Identify the “one best way” to perform the task in the present circumstances

A keen student of Toyota Production System can identify the inspirations of continuous improvement in the steps detailed above. I will finish with wonderful words of wisdom from Shingo.

Time is merely a shadow of motion. Supervisors frequently put pressure on plant workers to speed up their work, to get jobs done more quickly. Yet simply working faster – without improving the motions that take up the time – will not speed things up in the final analysis. Time is merely a shadow of motion, and no matter how much we may complaint about shadows, nothing will happen unless we deal with the substance – motion – that throws the shadow.

Stay safe and Always keep on learning…

In case you missed it, my last post was Lillian Gilbreth’s Synthesist:

Lillian Gilbreth’s Synthesist:

Lillian Gilbreth is one of my heroes in Industrial Engineering. I have written about her here and here. In today’s post, I am looking at Gilbreth’s idea of an analyst and synthesist. The term “analyst” is in common vocabulary, whereas the term “synthesist” is not. Even Microsoft Word is identifying that the term “synthesist” is incorrect.

In any introduction class to systems thinking, we get introduced to the idea of analysis and synthesis. As Russell Ackoff, the giant in Systems Thinking, teaches us:

A system is a whole which consists of a set of two or more parts. Each part affects the behavior of the whole, depending on how it interacts with the other parts of the system. To understand a system, analysis says to take it apart. But when you take a system apart, it loses all of its essential properties. The discovery that you cannot understand the nature of a system by analysis forced us to realize that another type of thinking was required. Not surprisingly, it came to be called synthesis.

Analysis… reveals structure— how a system works. If you want to repair an automobile, you have to analyze it to find what part isn’t working. Synthesis reveals understanding—why it works the way it does. The automobile, for example, was originally developed for six passengers. But no amount of analysis will help you to find out why. The answer lies in the fact that cars were designed for the average American family, which happened to be 5.6 at the time.

Lillian Gilbreth also talked about analysis and synthesis, back in 1914, in her book, The Psychology of Management. Gilbreth discussed ideas from the British psychologist, James Sully.

Analysis is defined by Sully as follows: “Analysis” is “taking apart more complex processes in order to single out for special inspection their several constituent processes.” He divides elements of thought activity into two:

(a) analysis: abstraction, (b) synthesis: comparison.”

Gilbreth further clarified what an analyst does:

ANALYST’S WORK IS DIVISION. – It is the duty of the analyst to divide the work that he is set to study into the minutest divisions possible.

She went on to describe the qualifications of an analyst.

QUALIFICATIONS OF AN ANALYST. – To be most successful, an analyst should have ingenuity, patience, and that love of dividing a process into its component parts and studying each separate part that characterizes the analytic mind. The analyst must be capable of doing accurate work, and orderly work.

To get the most pleasure and profit from his work he should realize that his great, underlying purpose is to relieve the worker of unnecessary fatigue, to shorten his work period per day, and to increase the number of his days and years of higher earning power. With this realization will come an added interest in his subject.

Gilbreth defined the role of a synthesist as follows:

THE SYNTHESIST’S WORK IS SELECTION AND ADDITION. – The synthesist studies the individual results of the analyst’s work, and their inter-relation, and determines which of these should be combined, and in what manner, for the most economic result. His duty is to construct that combination of the elements which will be most efficient.

The qualifications of a synthesist was explained as:

QUALIFICATIONS OF THE SYNTHESIST. – The synthesist must have a constructive mind, for he determines the sequence of events as well as the method of attack. He must have the ability to see the completed whole which he is trying to make, and to regard the elements with which he works not only as units, but in relation to each other. He must feel that any combination is influenced not only by the elements that go into it, but by the inter-relation between these elements. This differs for different combinations as in a kaleidoscope.

The relationship between the analyst and synthesist was best explained by Gilbreth as:

If synthesis in Scientific Management were nothing more than combining all the elements that result from analysis into a whole, it would be valuable. Any process studied analytically will be performed more intelligently, even if there is no change in the method. But the most important part of the synthesist’s work is the actual elimination of elements which are useless, and the combination of the remaining elements in such a way, or sequence, or schedule, that a far better method than the one analyzed will result.

Final Words:

Lillian Gilbreth’s ideas, as the cliché goes, were truly ahead of her times. We have all benefited from her brilliance. Gilbreth viewed a synthesist as a conserver of a valuable elements as well as an inventor involved in invention of better methods of doing work, such as tools or equipment. She also said that a synthesist is a discoverer of laws because they have the ability to understand why the parts are working the way they are, in relation to one another. A systems thinker fuses analysis and synthesis. Moreover, a systems thinker should be able to find differences among apparently similar things and similarities among apparently different things.

I will finish with further ideas from the 18th century French Philosopher Victor Cousin:

The legitimacy of every synthesis is directly owing to the exactness of analysis; every system which is merely [sic] an hypothesis is a vain system; every synthesis which has not been preceded by analysis is a pure imagination: but at the same time every analysis which does not aspire to a synthesis which maybe equal to it, is an analysis which halts on the way.

On the one hand, synthesis without analysis gives a false science; on the other hand, analysis without synthesis gives an incomplete science.

Stay safe and Always keep on learning…

In case you missed it, my last post was The Truths of Complexity:

The Truths of Complexity:

The Covid 19 pandemic has given me an opportunity to observe, meditate and learn about complexity in action. In today’s post, I am looking at “truths” in complexity. Humans, more than any other species, have the ability to change their environment at a faster pace. They are also able to maintain belief systems over time and act on them autonomously. These are good reasons to call all “human systems” complex systems.

The Theories of Truth:

Generally, there are three theories of truth in philosophy. They are as follows:

  1. Correspondence theory of truth – very simply put, this means that what you have internally in your mind corresponds one-to-one with the external world. The statement you might make such as – “the cat is on the mat” is true, if there are truly a cat and a mat, and if that cat is on that mat. The main objection to this theory is that we don’t have access to have an objective reality. What we have is a sensemaking organ, our brain, that is trying to make sense based on the data provided by the various sensory organs. The brain over time generates stable correlations which allows it to abstract meanings from the filtered information from the sensory data. The correspondence theory is viewed as a “static” picture of truth, and fails to explain the dynamic and complex nature of reality.
  2. Coherence theory of truth – In this approach, a statement is true if it is coherent with the different specified set of beliefs and propositions. Here the idea is more about a fit and harmony with existing beliefs. The coherence theory is about consistency. An objection to this theory is that the subjective nature of a statement can “bend” to match the existing strong belief systems. Perhaps, a good example of this is the recent poll that found that the majority of democrats fear that the worst is yet to come for the Covid 19 pandemic, while the majority of republicans believe that the worst is over. Another criticism against this is that we can be inconsistent in our beliefs as indicated by cognitive dissonance.
  3. Pragmatic Theory of truth – The pragmatic theory of truth was put forth as an alternative to the static correspondence theory of truth. In this theory, the value of truth is dependent on the utility it brings. Pragmatic theories of truth have the effect of shifting attention away from what makes a statement true and toward what people mean or do in describing a statement as true. As one of the proponents of Pragmatic theory, William James, put it – True beliefs are useful and dependable in ways that false beliefs are not:‘You can say of it then either that “it is useful because it is true” or that “it is true because it is useful”. Both these phrases mean exactly the same thing.’ One of my favorite explanations of pragmatic theory comes from Richard Rorty, who viewed it as coping with reality, rather than copying reality. One of the criticisms against the pragmatic theory of truth is how it explains truth in terms of utility. As John Capps notes, utility, long-term durability, and assertibility (etc.) should be viewed not as definitions but rather as criteria of truth, as yardsticks for distinguishing true beliefs from false ones.

Sensemaking Complexity:

From the discussion of truth, we can see that seeking truth is not an easy task, especially when we deal with complexity of human systems. Our natural tendency is to find order as pleasing and reassuring. We try to find order in all we can, and we try our best to maintain order as long as we can. In this attempt, we often neglect the actual complexity we are dealing with. A common way to distinguish complexity of a phenomenon is – ordered, complicated or complex. We can say a square peg in a square hole is an ordered phenomenon. The correspondence theory of truth is quite apt here because we have a one to one relationship. We have a very good working knowledge of cause and effect. As complexity increases, we get to complicated phenomenon where there is still somewhat a good cause and effect relationship. A car can be viewed as a complicated phenomenon. The correspondence theory is still apt here. Once we add a human to the mix, we get to complexity. Imagine the driver of a car. Now imagine thousands of drivers all at once. The correspondence theory of truth falls apart fast here.

The main source of complexity in the example discussed above comes from humans. We are autonomous, and we are able to justify our own actions. We may go faster than the speed limit because we are already late for the appointment. We may overtake on the wrong side because the other driver is driving slowly. We assign meanings and we also assign purposes for others. We do not always realize that other humans also have the same power.

We have seen varying responses and behavior in this pandemic. We have seen the different justifications and hypotheses. We agree with some of them and strongly disagree with others depending on how they cohere with our own belief systems. The actual transmission of the virus is fairly constrained. It transmits mainly from person to person. The transmission occurs mainly through respiratory droplets. Every human interaction carries some risk of becoming infected if the other person is a carrier of the virus. However, the actual course of the pandemic has been complex.

Philosophical Insights to Sensemaking Complexity:

I will use the ideas of Friedrich Nietzsche and William. V.O. Quine to further look at truth and how we come to know about truth. Nietzsche had a multidimensional view of truth. He viewed truth as:

A mobile army of metaphors, metonyms, and anthropomorphisms—in short, a sum of human relations which have been enhanced, transposed, and embellished poetically and rhetorically, and which after long use seem firm, canonical, and obligatory to a people: truths are illusions about which one has forgotten that this is what they are; metaphors which are worn out and without sensuous power; coins which have lost their pictures and now matter only as metal, no longer as coins.

He emphasized on the abstract nature of truth. One comes to view the abstractions/metaphors as stand in for reality, and eventually falsely equate them to reality.

Every word immediately becomes a concept, in as much as it is not intended to serve as a reminder of the unique and wholly individualized original experience to which it owes its birth, but must at the same time fit innumerable, more or less similar cases—which means, strictly speaking, never equal—in other words, a lot of unequal cases. Every concept originates through our equating what is unequal.

Nietzsche advised us against using a cause-effect, correspondence type viewpoint in sensemaking complexity:

It is we alone who have devised cause, sequence, for-each-other, relativity, constraint, number, law, freedom, motive, and purpose; and when we project and mix this symbol world into things as if it existed ‘in itself’, we act once more as we have always acted—mythologically. 

As Maureen Finnigan notes in her wonderful essay, Nietzsche’s Perspective: Beyond Truth as an Ideal:

As truth is not objective, in like manner, it is not subjective. Since thinking is not wholly rational, disconnected from the body, or independent of the world, the subjective perception, or conception, of truth through the intellect alone is impossible. “The ‘pure spirit’ is pure stupidity: if we subtract the nervous system and the senses—the ‘mortal shroud’—then we miscalculate—that is all!” Inasmuch as the individual is not independent from the world, one can neither subjectively nor objectively explain the world as if detached, but must interpret the world from within. Subjective and objective, like True and apparent, soul and body, thinking thing and material thing, intellect and sense, noumena and phenomena, are dualities that Nietzsche aspires to overcome. Thus, although Nietzsche is not a rationalist, this does not mean he falls into the irrationalist camp. He does not abolish reason but instead situates it within life, as an instrument, not as an absolute.

With complexity, we should not look for correspondence but coherence. Correspondence forces categorization while coherence forces connections. This follows nicely into Quine’s Web of Belief idea. Quine’s idea is a holistic approach. We make meanings in a holistic fashion. When we observe a phenomenon, our sensory experience and the belief it generates do not standalone in our entire belief system. Instead, Quine postulates that we make sense holistically with a web of belief. Every belief is connected to other beliefs like a web.

For example, we can say Experience1(E1) led to Belief1(B1), and Experience2(E2) led to Belief2(B2) etc. This has the correspondence nature we discussed earlier. This view prefers the ordered static approach to sensemaking. However, in Quine’s view, it is more dynamic, interconnected and complex. This has the coherence nature we discussed earlier. The schematic below, inspired by a lecture note from Bryan. Van. W. Norden, shows this in detail.

The idea of Web of Belief is clearly explained by Thomas Kelly:

Quine famously suggests that we can picture everything that we take to be true as constituting a single, seamless “web of belief.” The nodes of the web represent individual beliefs, and the connections between nodes represent the logical relations between beliefs. Although there are important epistemic differences among the beliefs in the web, these differences are matters of degree as opposed to kind. From the perspective of the epistemologist, the most important dimension along which beliefs can vary is their centrality within the web: the centrality of a belief corresponds to how fundamental it is to our overall view of the world, or how deeply implicated it is with the rest of what we think. The metaphor of the web of belief thus represents the relevant kind of fundamentality in spatial terms: the more a particular belief is implicated in our overall view of the world, the nearer it is to the center, while less fundamental beliefs are located nearer the periphery of the web. Experience first impinges upon the web at the periphery, but no belief within the web is wholly cut off from experience, inasmuch as even those beliefs at the very center stand in logical relations to beliefs nearer the periphery.

The idea of degrees rather than a concrete distinction between beliefs is very important to note here. Additionally, Quine proposes that when we counter an experience contradicting our belief, we seek to restore consistency/coherence in the web by giving up beliefs that are located near the periphery rather than the ones near the center.

Final Words:

The dynamic nature of complexity is not just applicable to a pandemic but also to scientific paradigms. This is beautifully explained in the quote from Jacob Bronowski below:

“There is no permanence to scientific concepts because they are only our interpretations of natural phenomena … We merely make a temporary invention which covers that part of the world accessible to us at the moment”

Our beliefs shape our experience as much as our experiences shape our beliefs in a recursive manner. The web gets more complex as time goes on, where some of the nodes become more distinct and some others get hazier. We are prone to getting perpetually frustrated if we try to apply a static framework to the dynamic everchanging domain of complexity. It gets more frustrating because patterns emerge on a continuous basis providing an illusion of order. The static and rigid frameworks break because of their rigidity and inflexibility to tackle the variety thrown upon them.

With this mind, we should come to realize that we do not have a means to know the external world as-is. All we can know is how it appears to us based on our web of belief. The pragmatic tradition of truth advises us to keep going on our search for truth, and that this search is self-corrective. The correspondence theory fails us because the meaning we create is not independent of us, but very much a product of our web of belief. At the same time, if we don’t seek to understand others, coherence theory will fail us because we would lack the requisite variety needed to make sense of a complex phenomenon. I will finish with an excellent quote from Maureen Finnigan:

Human beings impose their own truth on life instead of seeking truth within life.

Stay safe and Always keep on learning… In case you missed it, my last post was Korzybski at the Gemba: