Cohesion Forces and Tools

This article is part of the series on Autonomy and Cohesion. It is the second part of the basic overview of the balance. If you haven’t read the previous part, I’d recommend doing so before reading further.

Cohesion forces

Liquids and solids are in those states because there are cohesion forces bonding the molecules together. The main cohesion forces currently studied in physics are the van der Waals forces, dipole-dipole interactions, hydrogen bonding, and ionic bonding. In socio-technical systems, there are cohesion forces too. Those forces are way more complex and less studied. Cohesion in socio-technical systems is not only due to natural forces. Tools, technologies, and artifacts can significantly contribute too. They bring direct and also systemic effects. We’ll go to the cohesion tools and technologies after we briefly review some cohesion forces and factors. Cohesion forces and factors are difficult or impossible to influence, which is what makes them different from cohesion tools and technologies.

There are personal cohesion factors like the need for safety, the need to belong to a social group, to reduce uncertainty, and the need to increase self-esteem. Such needs make us form clubs, tribes, communities, organizations, and networks.

Shared values and beliefs are strong cohesion forces, and those can include the shared value of autonomy.

There are also social identity cohesion forces. We tend to identify, sometimes strongly, with sports clubs, ethnic groups, communities, professions, organizations, or religions. In some situations, compassion, loyalty, and empathy play a bigger role. In others, completely different forces. For example, typical personal cohesion forces in social networks are the need for self-expression, validation, and recognition, as well as the fear of missing out.

In every socio-technical system, there are internal and external cohesion factors and forces. The personal cohesion forces work both within organizations and networks, although they have different subsets and strengths. Typical internal organizational cohesion forces are organizational identity, internal operational dependencies, shared resources, synergy, and efficiency. In networks, cohesion forces and factors are proximity, transitivity, and preferential attachment, and in social networks, there are many additional ones, such as shared interest and shared aversion.

Which brings us to the external cohesion factors.
Continue reading

Exploration and Exploitation

I love going to jazz festivals. Listening to good jazz at home is a pleasure, but what’s missing are the vibes during a live performance. And it’s not the same when you listen to a recording of a concert. Everything changes when you are actually there, immersed, experiencing directly with all your senses. I guess it’s similar with other types of music. But what makes the difference between listening to a recording and being at concert even bigger for jazz, is that it is all about improvisation. And then the experience of single concerts versus festivals is also different. With concerts, you immerse yourself for a couple of hours into a world of magic and then go back to the normal world. But with jazz festivals, you relocate to live in a music village for a couple of days. This doesn’t only make it a different experience, but also calls for different kinds of decisions.

Previously, when I learned of a new jazz festival or read the line-up of a familiar one, the way I decided whether to go was simple. I just checked who would be performing. If there were musicians that I liked, but hadn’t watched live, or some that I had, but wanted to see again, then I went. If not, I usually wouldn’t risk it.

Once I chose to go, this brought more things to consider. Jazz festivals usually have many stages, with parallel performances during the day and into the night. Last time I went to the North Sea Jazz Festival, there were over 80 performances in only a few days. So, there is a good chance that some of those you want to watch will clash, and you are forced to choose. And I kept applying the same low-risk strategy for choosing what to watch as I did for deciding if I should go at all.

Then one day, I arrived late to a festival just before two clashing sets were about to begin. I dashed into the closest hall with no clue as to what I would find. And there I experienced what turned out to be the best concert of the whole festival. I hadn’t heard of the group, and if I had read the description beforehand, I would have avoided their performance.

I realized then, that by only choosing concerts with familiar musicians, I was over-exploiting and under-exploring. My strategy was depriving me of learning opportunities and reducing the overall value I got from the festivals.

What happened at that festival changed the way I decide whether to go and which performances to see. Now I not only attend many more concerts of musicians previously unknown to me, but not having a familiar name in the line-up does not determine the decision to buy tickets.

However, when the whole line-up of the festival is completely unknown, then going is all exploration. That’s highly risky. When there are no familiar musicians, I listen to recordings of previous concerts of some of the groups. If I like at least two of them, then I usually go to the festival, and once there, I will still check out a few acts I don’t know. That’s another way to balance exploitation and exploration.

When I am in control, “I restrict the world to what I can imagine or permit”, writes Ranulph Glanville. He gives the example of going to a restaurant with friends. If it’s always him who chooses the restaurant, the group will only go to the restaurants that he knows. They are limited to his taste and knowledge, or rather – as he admits – by his ignorance. Letting go of control by letting others choose, not only expands his knowledge but would often give a better experience for everyone.

Having the wrong strategy when it comes to jazz festivals and restaurants reduces the pleasure, but in these examples, the decisions make such a small impact that they may not show how important this balance is. Yet, we make similar choices all the time. For example, you might decide to invest your time in getting better at what you currently do well while not allocating time to trying out new things. This may put you in a very unpleasant situation in times when there is no more demand for what you are skilled at, or when you need a change but have difficulty choosing because you haven’t tested many alternatives.

Throughout our lives, many of us realize that when making choices, we should have a balance of exploration and exploitation. We should let go of some control and not limit ourselves to what we already know. And that’s an important first step, but it’s not enough. It takes a greater effort to keep this awareness awake. And somehow, it’s also easier at a personal level. How so?

We live our lives and are experiencing every minute of every day. We absorb sounds, tastes, smells, and light and feel the air on our skin. Through evolution we are well equipped to receive a signal when there is even a small problem. We get a scratch and react right away. That’s not the case with organizations. They might be missing a whole limb or – and here the metaphor will fail to produce a feeling of exaggeration – a head, without noticing for years. And even if we have learned how to balance exploitation and exploration in our lives, the chances are we are working in organizations that haven’t. It’s not easy even to imagine what maintaining this balance means for an organization. We can’t really step into the shoes of one. What we can do instead is study this phenomenon a little closer, try to understand it better, and then, armed with a new pair of glasses, make the best of that knowledge while we keep learning from what happens. To understand how the balance between exploration and exploitation works in organizations, we’ll start with the problem of resource allocation, and then move to more complex situations. Continue reading

Productive Organisational Paradoxes

It is often said that organisations are full of paradoxes. But this refers to contradictions and tensions. It is understood as something that needs to be taken care of. When organisations are looked at as social systems, however, it becomes clear that they are only possible because of paradoxes, and particularly paradoxes of self-reference. Understanding how these paradoxes create and maintain organisations is an important skill for practitioners trying to make sense of what’s going on and improve it. The basic generative organisational paradox is that of decisions. It brings new light not only on decision patterns and dependencies, but also on understanding the nature of objectives, power, and relations with clients.

Here are the recording and slides from my talk at the SCiO open day in London in January 2019.

Most of this story, but told gently, is in Chapter 5 of Essential Balances.

QUTE: Enterprise Space and Time

Here’s another pair of glasses with which to look at organisations. It can be used either together with the Essential Balances or with the Productive Paradoxes, or on its own. For those new to my “glasses” metaphor, here’s a quick intro.

The Glasses Metaphor

As I’m sceptical about the usefulness of methodologies, frameworks and best practices when it comes to social species, my preference is to work with habits and instead of using models, to use organisations directly as the best model of themselves.

The best material model of a cat is another, or preferably the same, cat.

N. Wiener, A. Rosenblueth, Philosophy of Science (1945)

What I find important in working with organisations is to break free from some old habits, by changing them with new ones. And most of all, cultivating the habit of being conscious about the dual nature of habits: that they are both enabling and constraining; that while you create them they influence the way you create them. Along with recipes and best practices, I’m also sceptical about KPIs, evidence-based policies, and all methods claiming objectivity.

Objectivity is a subject’s delusion that observing can be done without him. Involving objectivity is abrogating responsibility – hence its popularity.

Heinz von Foerster

Instead of “this is how things are”,  my claim is that “it’s potentially useful to create certain observational habits”. Or – and here comes the metaphor – the habit of observation using different pairs of glasses. “Different” implies two things. One is that you are always wearing some pair of glasses, regardless of whether you realize it or not. And the other is that offering a new pair is less important than creating the habit of changing the glasses from time to time.

I prefer the “glasses” to the “lens” metaphor, and here’s why. Glasses indeed have lenses, and lenses are meant to improve vision or, at any rate, change it.  Quite often, the glasses I offer bring surprises. Where you trust your intuition, you might see things that are counter-intuitive, and where you’d rather use logic, they might appear illogical. It’s not intentional. It just often happens to be the case.

The first reason I prefer glasses metaphor to just lens is that glasses have frames. That should be a constant reminder that every perspective has limitations, creates a bias, and leaves a blind spot. Using the same glasses might be problematic in some situations or in all situations if you wear them for too long. And the second reason is that glasses are made to fit, they are something designed for our bodies. For example, they wouldn’t fit a mouse or even another person. This has far-reaching implications, which I’ll not go into now.

QUTE

QUTE stands for “Quantum Theory of Enterprise”. Continue reading

The Scissors of Science

Three centuries ago the average life expectancy in Europe was between 33 and 40 years. Interestingly, 33 was also the average life expectancy in the Palaeolithic era, 2.6 million years ago. What is that we’ve got in the last three centuries that we hadn’t got in all the time before? Well, science!

Science did a lot of miracles. But like all things that do miracles, it quickly turned into a religion. A God that most people in the Western world believe in today. And like all believers, when science fails, we may think it has not advanced in that area yet, but we don’t think there is anything wrong with science itself. Or when the data doesn’t match, we think it’s because those scientists are not very good at statistics. Or, if not that, then simply the problem is with failed control over scientific publications, as it was concluded three years ago when Begley and Ellis published a shocking study that they were able to reproduce only 11 per cent of the original cancer research findings.

Well, I believe that the problem with science is more fundamental than that.

The word science comes from skei, which means “to cut, to divide”. The same is the root of scissors, schizophrenia and shit. “Dividing” when applied to science, comes in handy to explain some fundamental problems with it. It has at least the following six manifestations. Continue reading

What’s Wrong with Best Practices?

That is a question I get very often from people who know that I keep away from prescriptive approaches. I’ve been giving some quick responses, but it would be better if next time I can point to a more elaborated answer. And here it is.

While I have a lot of sympathy for those that object to ‘Best Practice’ as a name — even more — to those that object it as a claim, my uneasiness is somewhat different. I’ll focus here on that.

Some best practices are very useful. In fact, most mature and well-applied best practices for carrying out some technical tasks, from taking a blood test, painting a wall or repairing an engine, to building a factory or a ship, are indeed valuable (as long as they don’t suffocate innovation).

The real problem is when best practices are applied to people and social systems. I call this a ‘problem’, but it is in fact a huge opportunity for many. Most contemporary non-fiction books, especially management and self-help texts, have been seizing this opportunity extremely well. It’s not easy to find a best seller in this category or a popular article, that doesn’t provide some sort of prescriptions and advice, often numbered, on how to achieve or avoid something. Maybe it is a best practice for best sellers. Let’s give it a try then:

 

Four Reasons Why You Should Be Cautious When Applying Best Practices:

 

1. Correlations.

How do Best Practices come about? Some individuals or organisations, become known or are later made known by the actions of the best practice discoverers and proponents, as successful according to some norms. Let’s call these individuals or organisations ‘best practice pioneer’. Then one or more observers, the ‘best practice discoverer’, studies the pioneers to find out what made them successful. The discoverer first takes certain effects, and then selects, by identifying commonalities, what she or he believes were the causes. That is followed by a generalisation of the commonalities, from which point they begin their life as prescriptions, regardless if they are called ‘best practices’, ‘methodologies’, ‘techniques’, ‘recipes’,’templates’, or something else. Later they are tested and based on feedback, reappear in more mature forms and variations, in some cases supplied with scalability criteria and conditions for a successful application. The successes and failures of those that apply them give birth to Best Practices on applying Best Practices.

The problem is, that the discoverers select common patterns among the observed successful pioneers, and infer causal relation between these commonalities and those that were the criteria to select that set of pioneers in the first place. Then such correlation leads the discoverer to select what to pay attention to, and every pattern that supports the hypothesis based on the selected commonalities would be preferred over those that don’t.

2. The risk of over-simplification.

Best practices help us deal with external stimuli, when they are too many to handle, by prompting which ones to pay attention to and how to react to them. The only way to deal with a situation is when the number of responses is higher than the number of the stimuli, within a given goal set (Ashby’s law). Or in other words, best practices are tools for reducing external variety. But not only that, they also provide means for amplifying internal variety in a special way – coupled with those stimuli that you are advised to pay attention to. So if that assumption was wrong, and it often is, neither the external variety is reduced, nor the internal is amplified.

3. Assumptions about the application context.

I used to be a practitioner of PRINCE2. What I still appreciate is a few smart techniques and in fact – the name itself. The name is the important disclaimer I’m missing in most methodologies and other types of best practices: they only work in controlled environments. They work when most of the conditions of the design-time if you allow me the IT jargon, are unchanged in run-time. This is rarely the case and is increasingly less so. This brings another interesting phenomenon: the same conditions that make the world less predictable also help quickly productise and spread best practices. They come with better marketing and with more authority in a world in which less of what has happened could prepare you for what will.

4. The habits created by Best Practices.

The worst is when people hide behind the authority of best practices or their proponents. If not that, best practices create habits of first looking for best practices, instead of thinking. And then, there is the alternative cost: the more time people spend on learning best practices, the less time they have for developing their senses for detection of weak signals and for developing their capabilities for new responses.

In summary, if you are sure that certain best practice is useful, and it’s not based on wrong inference and does not lead you to dismiss important factors, and the situation you are in is not complex, and it doesn’t weaken your resilience, then go ahead, use it.

 

Language and meta-language for Enterprise Architecture

That was the topic of a talk I gave in October 2014 at an Enterprise Architecture event in London.

Most of the slides are available as PDF slidedeck on Slideshare.

They probably don’t tell the story by themselves, and I’m not going to help them here unless this post provokes a discussion. What I’ll do instead is clarify the title. “Language” refers to the means of describing organisations. They could be different. Given the current state of maturity, I have found those based on description logic to be very useful. What I meant by the “current state of maturity” is that a method in its theoretical development, application, the technologies supporting it and the experience with their application justifies investments in utilising them and helping in their further development. Although I find such a language clearly superior to the alternatives in use, that doesn’t mean there are no issues and that no approaches are showing convincing solutions to those issues. However, the practice with the latter or with the available tools doesn’t give me enough reason to stand behind them. The situation with the “meta-language” is similar, but let’s first clarify why I call it that.

Metalanguage is commonly defined as language about language. If that was the meaning I intended, these notes here could have been referred to as a mixture of another meta- and a meta-meta-language. That’s not the case. But to clarify the intended meaning of “meta,” I need to first clarify “language.”

I have found that there is a need to describe properly the “objects” that people in organisations are concerned with and how they relate to each other. It could be some way of representing physical things such as buildings, documents and servers or abstract concepts such as services, processes and capabilities. And although it relates also to abstract things, I sometimes call it “language for the substance”.

Organisations are autonomous and adaptive systems, continuously maintained by their interaction with their niche, the latter being brought forth from the background, by that very interaction. While a language such as the one proposed can be useful to understand the components of an organisation, it doesn’t help much in understanding the dynamics and viability. The language for the substance cannot be used to talk about the form. That’s why there is a need, maybe temporarily until we find a better solution and probably a single language, to have another language and that other language I called meta-language in the presentation.

As this is a language for the form, I keep looking for ways to utilise some proposals. One nominee is George Spencer-Brown’s Laws of Form (this post includes a brief introduction). Papers like this one of Dirk Baecker give me hope that it is possible. Until then, for the purposes of Enterprise Architecture, I find the Viable System Model, with the whole body of knowledge and practice associated with it, as the most pragmatic meta-language.

 

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Redrawing the Viable System Model diagram

I’ve been arguing repeatedly that trying to get the Viable System Model from overviews, introductions and writings based on or about it, can put the curious mind in a state of confusion or simply lead to wrong interpretations. The absolute minimum is reading at least once each of the three books explaining the model. But better twice. Why? There are at least two good reasons. The obvious one is to better understand some points and pay attention to others that have been probably missed during the first run. But there is also another reason. Books are of linear nature, and when tackling non-linear subjects, a second reading gives the chance to better interpret each part of the text when having in memory other parts which relate to it.

Still, one of the things that are expected to be most helpful is in fact, what brings about either confusion, aversion or misuse: the VSM diagrams. They clearly favour expected ease of understanding over rigour, and yet often they fail in both. Here is my short list of issues, followed by a description of each:

  • Representation of the channels
  • Confusion about operations and their direct management
  • Notation and labelling of systems
  • They show something between generic and example model
  • Hierarchical implication

Representation of the channels

Stafford Beer admitted several times in his books the “diagrammatic limitations” of the VSM representations. Some of the choices had to do with the limitation of the 2D representation and others, I guess, aimed to avoid clutter. Figure 26 of “The Heart of Enterprise” is a good example of both. It shows eleven loops but implies twenty-one: 3 between environment, operations and management, multiplied by 3 for the choice of showing three operations, then another 9 = 3×3 for loops between same-type elements and finally 3 more between operation management and the meta-system.

Confusion about operations and their direct management

Depending on the context, System One refers either to operations or to their direct management. In some diagrams, S1 is the label of the circles, and in others – of the squares linked to them. Referring to one or the other in the text, depending on which channels are described, only adds to the confusion. That is related to the general problem of

Notation and labelling of systems

All diagrams representing the VSM in the original writings and all interpretations I’ve seen so far suggest that circles represent System One, and triangles pointing up and down, represent System Two and Three* respectively. Additionally, most VSM overviews state exactly that in the textual description. My assertion is almost the opposite:

What is labelled as S1 and what is shown as circles are both not representing S1.

That might come as a shock to many, and yet, now citing Beer, System One is not the “circles” but:

The collection of operating elements (that is, including their horizontal and vertical connexions)

The Heart of Enterprise, page 132

Strictly speaking, a system is a system because it shows emergent properties so it is more than the collection of its parts1that is by itself a popular but problematic statement. but even referring to it as a collection reveals the serious misinterpretation of taking only one of its parts to represent the whole system.

They show something between generic and example model

Communicating such matters to managers trained in business schools wasn’t an easy task. And it is even more challenging nowadays. There is a lot to learn and even more to unlearn. It is not surprising then that even in the generic models typically three operations are illustrated (same for System 2).  Yet, I was always missing a true generic representation, or what would many prefer to call “meta-model”.

Hierarchical implication

It can’t be repeated enough that the VSM is not a hierarchical model, and yet it is often perceived and used as such or not used especially because of that perception. It seems that recursivity is a challenging concept, while anything slightly resembling hierarchy is quickly taken to represent one. And sadly, the VSM diagram only amplifies that perception, although the orthogonality of the channels serves an entirely different purpose. Stafford Beer rarely missed an opportunity to remind us about that. Nevertheless, whatever is positioned higher implies seniority and the examples of mapping to actual roles and functions only help in confirming this misinterpretation.

 

There are other issues as well but my point was to outline the motivation for trying alternative approaches for modelling the VSM, without alternating the essence of the governing principles. Here is one humble attempt to propose a different representation (there is a less humble one which I’m working on, but it’s still too early to talk about it). The following diagram favours circular, instead of orthogonal representation which I hope achieves at least destroying the hierarchical perception. Yet, from a network point of view, the higher positioning of S3 is chosen on purpose as the network clearly shows that this node is a hub.

GenericCircularViewOfTheViableSystemModel

System One is represented by red colouring, keeping the conventional notation for the operations (S1.o) and their direct management (S1.m). As mentioned above, apart from solving this, the intention is to have it as a generic model. If that poses a problem for those used to the hybrid representation, here’s how it would look if two S1s are shown:

 

Circular Netwrok View Of The Viable System Model-2operations

I hope this proposal solves fully or partially the five issues explained earlier and brings a new perspective that can be insightful on its own. In any case, the aim is to be useful in some way. If not as it is now, then by triggering feedback that might bring it to a better state. Or, it can be useful by just provoking other, more successful attempts.

 

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    that is by itself a popular but problematic statement.

More on Requisite Inefficiency

The “slides” supporting my talk on Requisite Inefficiency a couple of months ago have been on Slideshare since then, but I haven’t had the time to share them here. Which I do now.

The various manifestations of Requisite Inefficiency in both organisms and organisations can be understood by observing the maintenance of balances between homeostasis and heterostasis (as in the adaptive immune systems), exploration and exploitation (foraging of ants or curiosity-driven vs market-driver research) as well as various types of redundancy or shift of function. The latter can be elastic, as it is in degeneracy, or plastic, as it is in exaptation.
Having an underutilised structure/function that is capable of providing the deficit of variety to the utilised structures of a system in order to match the complexity of an external stimulus, or that can be adapted in a sufficiently short time to do so, is a prerequisite for survival.

Variety, Part 2

etyCan you deal with it?

Deal originates from divide. It initially meant only to distribute. Now it also means to cope, manage and control. We manage things by dividing them. We eat an elephant piece by piece, we start a journey of a thousand miles with a single step, and we divide to conquer.

(This is the second part of a series on the concept variety used as a measure of complexity. You may want to read the previous part before this one, but even doing it after or not at all is fine.)

That proved to be a good way to manage things, or at least some things, and in some situations. But often it’s not enough. To deal with things, and here I use deal to mean manage, understand, control, we need requisite variety. When we don’t have enough variety, we could get it in three ways: by attenuating the variety of what has to be dealt with, by amplifying our variety, or by doing a bit of both when the difference is too big1There yet another way: to change our goal..

And how do we do that? Let’s start by putting some common activities in each of these groups. We attenuate external variety by grouping, categorising, splitting, standardising, setting objectives, filtering, reporting, coordinating, and consolidating. We amplify our variety by learning, trial-and-error, practising, networking, advertising, buffering, doing contingency planning, and innovating. And we can add a lot more to both lists. We use such activities but when doing these activities we need requisite variety as well. That’s why we have to apply them at different scale2Some may prefer to put it more technically as “different level of recursion”.. We learn to split and we split to learn, for example.

Attenuate and amplify variety

What about the third group? What kind of activities can both amplify ours and attenuate the variety of what we need to deal with? It could be easy to put in that third group pairs from each list but aren’t there single types? There are. Here are two suggestions: planning and pretending.

With planning, we get higher variety by being prepared for at least one scenario, especially in the parts of what we can control, in contrast to those not prepared even for that. But then, we reduce different possibilities to one and try to absorb part of the deflected variety with risk management activities.

Planning is important in both operations and projects, and yet, in a business setting, we can get away with poor planning long enough to lose the opportunity to adapt. And that is the case in systems with delayed feedback. That’s also why I like the test of quick-feedback and skin-in-the-game situations, like sailing. In sailing, You are doomed if you sail off without a plan, or if you stick to the plan in front of unforseen events. And that’s valid at every planning level, week, day or an hour.

The second example of activity that both amplifies and attenuates variety is pretending. It can be so successful as to reinforce its application to the extreme. Pretending is so important for stick insects, for example, that they apply it 24/7. That proved to be really successful for their survival and they’ve been getting better at it for the last fifty million years. It turned out to be also so satisfactory that they can live without sex for one million years. Well, that’s for a different reason but nevertheless, their adaptability is impressive. The evolutionary pressure to better resemble sticks made them sacrifice their organ symmetry so that they can afford thinner bodies. Isn’t it amazing: you give up one of your kidneys just to be able to lie better? Now, why do I argue that deception in general, and pretending in particular, has a dual role in the variety game? Stick insects amplify their morphologic variety and through this, they attenuate the perception variety of their predators. A predator sees the stick as a stick and the stick insect as a stick, two states attenuated into one.

Obviously, snakes are more agile than stick insects but for some types that agility goes beyond the capabilities of their bodies. Those snakes don’t pretend 24/7 but just when attacked. They pretend to be dead. And one of those types, the hognose snake, goes so far in their act as to stick its tongue out, vomit blood and sometimes even defecate. That should be not just convincing but quite off-putting even for the hungriest of predators.

If pretending can be such a variety amplifier (and attenuator), pretending to pretend can achieve even more remarkable results. A way to imagine the variety proliferation of such a structure is to use an analogy with the example of three connected black boxes that Stafford Beer gave in “The Heart of Enterprise”. If the first box has three inputs and one output, each of them with two possible states, then the input variety is 8 and the output is 256. Going from 8 to 256 with only one output is impressive but when that is the input of a third black box, having only one output as well, then its variety reaches the cosmic number of 1.157×1077.

That seems to be one of the formulas of the writer Kazuo Ishiguro. As Margaret Atwood put it, “an Ishiguro novel is never about what it pretends to pretend to be about”. No wonder “Never Let Me Go” is so good. And the author, having much more variety than the stick insects, didn’t have to give his organs to be successful. He just made up characters that gave theirs.

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    There yet another way: to change our goal.
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    Some may prefer to put it more technically as “different level of recursion”.

Variety, Part 1

The cybernetic concept of variety is enjoying some increase in usage. And that’s both in frequency and in a number of different contexts. Even typing “Ross Ashby” in Google Trends confirms that impression.RossyAshby_as_seen_by_GoogleTrends In the last two years, the interest seems stable, while in the previous six – it was non-existent, save for the lonely peak in May 2010. Google Trends is not a source of data to draw serious conclusions from, yet it confirms the impression coming from tweets, blogs, articles, and books. On the one hand, that’s good news. I still find the usage insignificant compared to what I believe it should be. Nevertheless, little attention is better than none. On the other hand, it attracts some interpretations, leading to a misapprehension of the concept. That’s why I hope it’s worth exchanging more ideas about variety, and those having more variety themselves would either enjoy wider adoption or those using them – more benefits, or both.

The concept of variety as a measure of complexity had been preceded and inspired by the information entropy of Claude Shannon, also known as the “amount of surprise” in a message. That, although stimulated by the development of communication technologies in the first half of the twentieth century, had its roots in statistical mechanics and Boltzmann’s definition of entropy. Boltzmann, unlike classical mechanics and thermodynamics, defined entropy as the number of possible microstates corresponding to the macro-state of a system.

Variety is defined as the number of possible states in a system. It is also applied to a set of elements. The number of different members determines the variety of a set. It can be applied to the members themselves, which can be in different states, and then the set of possible transitions has a certain variety. This is the first important property of variety. It’s recursive. I’ll come back to this later. Now, to clarify what is meant by “state”:

By a state of a system is meant any well-defined condition or property that can be recognised if it occurs again.

Ross Ashby

Variety can sometimes be easy to count. For example, after opening the game in chess with a pawn on D4, the queen has a variety of three: not to move or move to one of the two possible squares. If only the temporary variety gain is counted, then choosing D2 as the next move would give a variety of 9, and D3 would give 16. That’s not enough to tell if the move is good or bad, especially keeping in mind that some of that gained variety is not effective. However, in case of uncertainty, in games and elsewhere, moving to a place that both increases our future options and decreases those of the opponent seems good advice.

Variety can be expressed as a number, as it was done in the chess example, but in many cases, it’s more convenient to use the logarithm of that number (in case that sounds like a distant memory from school years, nowadays there are easy ways to refresh it in minutes). The common practice, maybe because of the first areas of application, is to use binary logarithms. When that is the case, variety can be expressed in bits. It is indeed more convenient to say the variety of a four-letter code using the English alphabet is 18.8 bits instead of 456 976. There is an extra bonus. When the logarithmic expression is used, varieties of elements are combined by adding instead of multiplying.

Variety is sometimes referred to and counted as permutations. That might be fine in certain cases but as a rule it is not. To use the example with the 4-letter code, it has 358 800 permutations (26 factorial divided by 22 factorial), while the variety is 456 976 (26 to the power of 4).

Variety is relative. It depends on the observer. That’s obvious even from the word “recognised” in the definition of state. If, for example, there is a clock with two hands that are exactly the same or at least to the extent that an observer can’t make the difference, then, from the point of view of the observer, the clock will have a much lower variety than a regular one. The observer will not be able to distinguish, for example, 12:30 and 6:03 as they will be seen as the same state of the clock.

Clock with indistiguishable hands

This can be seen as another dependency. That of the capacity of the channel or the variety of the transducer. For example, it is estimated that regular humans can distinguish up to 10 million colours, while tetrachromats – at least ten times more. The variety of the transducer and the capacity of the channel should always be taken into account.

When working with variety, it is useful to study the relevant constraints. If we throw a stone from the surface of Earth, certain constraints, including those we call “gravity” and the “resistance of the air”, would allow a much smaller range of possible states than if those constraints were not present. Ross Ashby made the following observation: “every law of nature is a constraint”, “science looks for laws; it is therefore much concerned with looking for constraints”.

There is this popular way of defining a system as something which is more than the sum of its parts. Let’s see this statement through the lens of varieties and constraints. If we have two elements, A and B, and each can be in two possible states on their own but when linked to each other A can bring B to another, third state, and B can bring A to another state as well. In this case, the system AB has certainly more variety than the sum of A and B unbound. But if, when linking A and B they inhibit each other, allowing one state instead of two, then it is clearly the opposite. That motivates rephrasing the popular statement to “a system might have different variety than the combined variety of its parts”.

If that example with A and B is too abstract, imagine a canoe sprint kayak with two paddlers working in sync and then compare it with a similar setting, with one of the paddlers rowing while the other holds her paddle in the water.

Yet, “is more than the sum of” can be retained but then another modification is needed. Here’s one suggested by Heinz von Foerster:

The measure of the sum of the parts is greater than the sum of the measures of the parts. One is the measure of the sum; the other is the sum of the measures. Take, for example, the measurement function “to square,” which makes this immediately apparent. I have two parts, one is a, the other b. Now I have the measure of the sum of the parts. What does that look like? a + b as the sum of the parts squared, (a + b)2 gives us a2 + 2ab + b2. Now I need the sum of the measures of the parts, and with this I have the measure of a (= a2) and the measure of b (= b2): a2 + b2. Now I claim that the measure of the sums of the parts is greater than the sum of the measures of the parts and state that: a2 + b2 + 2ab is greater than a2 + b2. So the measure of the sum is greater than the sum of the measures. Why? a and b squared already have a relation together

Heinz von Foerster. The Beginning of Heaven and Earth Has No Name (Meaning Systems) (p. 18)

And now about the law of requisite variety. It’s stated as “variety can destroy variety” by Ashby and as “only variety can absorb variety” by Beer, and has other formulations such as “The larger the variety of actions available to control system, the larger the variety of perturbations it is able to compensate”. Basically, when the variety of the regulator is lower than the variety of the disturbance, that gives high variety of the outcome. A regulator can only achieve the desired outcome variety if its own variety is the same or higher than that of the disturbance. The recursive nature mentioned earlier can now be easily seen if we look at the regulator as a channel between the disturbance and the outcome or if we account for the variety of the channels at the level of recursion with which we started.

To really understand the significance of this law, it should be seen how it exerts itself in various situations, which we wouldn’t normally describe with words such as “regulator”, “perturbations” and “variety”.

In the chess example, the power of each piece is a function of its variety, which is the one given by the rules and reduced by the constraints at every move. Was there a need to know about requisite variety to design this game? Or any other game for that matter? Or was it necessary to know how to wage war? Certainly not. And yet, it’s all there:

It is the rule in war, if our forces are ten to the enemy’s one, to surround him; if five to one, to attack him; if twice as numerous, to divide our army into two.

Sun Tzu, The Art of War

Let’s leave the games now and come back to the relative nature of variety. The light signals in ships should comply with the International Regulations for Preventing Collisions at Sea (IRPCS). The agreed signals have a reduced variety to communicate the states of the ships but enough to ensure the required control. For example, if an observer sees one green light, she knows that another ship is passing from left to right. If she sees one red light, it passes right to left. There are lots of states – different angles of the course of the other ship – that are reduced into these two, but that serves the purpose well enough. Now, if she sees both red and green, that means that the ship is coming exactly towards her. That’s a dangerous situation. The reduction of variety, in this case, has to be very low.

The relativity of variety is not only related to the observer’s “powers of discrimination”, or those of the purpose of regulation. It could be dependent also on the context. Easop’s fable “The Fox and the Stork”comes to mind.

Fables, and stories in general, influence people and survive centuries. But is it that do you need a story instead of getting directly the moral of the story? Yes, it’s more interesting, there is this uncertainty element and all that. But there is something else. Stories are ambiguous and interpretable. They leave many things to be completed by the readers and listeners. To put it in different words, they have a much higher variety than morals and values.

That’s it for this part.

And here is the next.

Requisite Inefficiency

In his latest article Ancient Wisdom teaches Business Processes, Keith Swenson reflects on an interesting story told by Jared Diamond. In short, the potato farmers in Peru used to scatter their strips of land. They kept them that way instead of amalgamating them which would seem like the most reasonable thing to do. This turned out to be a smart risk mitigating strategy. As these strips are scattered, the risk of various hazards is spread and the probability to get something from the owned land every year is higher.

I see that story as yet another manifestation of Ashby’s law of requisite variety. The environment is very complex and to deal with it somehow, we either find a way to reduce that variety in view of a particular objective, or try to increase ours. In a farming setting an example of variety reduction would be building a greenhouse. The story of the Peruvian farmers is a good example of the opposite strategy – increase of the variety of the farmers’ system. The story shows another interesting thing. It is an example of a way to deal with oscillation. The farmers controlled the damage of the lows by giving up the potential benefits of the highs.

Back to the post of Keith Swenson, after bringing this lesson to the area of business process, he concludes

Efficiency is not uniformity.  Instead, don’t worry about enforcing a best practice, but instead attempt only to identify and eliminate “worst practices”

I fully agree about best practices. The enforcement of best practices is what one can find in three of every four books on management and in nearly every organisation today. This may indeed increase the success rate in predictable circumstances but it decreases resilience and it is just not working when the uncertainty of the environment is high.

I’m not quite sure about the other advice: “but instead attempt only to identify and eliminate “worst practices”. Here’s why I’m uncomfortable with this statement:

1. To identify and eliminate “worst practice” is a best practice itself.

2. To spot an anti-pattern, label it as “worst-practice” and eliminate it might seem the reasonable thing to do today. But what about tomorrow? Will this “worst-practice” be an anti-pattern in the new circumstances of tomorrow? Or something that we might need to deal with the change?

Is a certain amount of bad practice necessarily unhealthy?

It seems quite the opposite. Some bad practice is not just nice to have, it is essential for viability. I’ll not be able to put it better than Stafford Beer:

Error, controlled to a reasonable level, is not the absolute enemy we have been thought to think of. On the contrary, it is a precondition for survival. […] The flirtation with error keeps the algedonic feedbacks toned up and ready to recognise the need for change.

Stafford Beer, Brain of the firm (1972)

I prefer to call this “reasonable level” of error requisite inefficiency. Where can we see this? In most – if not all – complex adaptive systems. A handy example is the way immune system works in humans and other animals having the so called adaptive immune system (AIS).

The main agents of the AIS are T and B lymphocytes. They are produced by stem cells in the bone marrow. They account for 20-40% of the blood cells which makes about 2 trillion. The way the AIS works is fascinating but for the topic here of requisite inefficiency, what is interesting is the reproduction of the B-cells.

The B-cells recognise the pathogen molecules, the “antigens”, depending on how well the shape of their receptor molecules match that of the antigens. The better the match, the better the chance to be recognised as antigen. And when that is the case, the antigens are “marked” for destruction. Then follows a process in which the T-cells play an important role.

As we keep talking of the complexity and uncertainty of the environment, the pathogens seem a very good model of it.

The best material model of a cat is another, or preferably the same, cat.

N. Wiener, A. Rosenblueth, Philosophy of Science (1945)

What is the main problem of the immune system? It cannot predict what pathogens will invade the body and prepare accordingly. How does it solve it? By generating enormous diversity. Yes, Ashby’s law again. The way this variety is generated is interesting in itself for the capability of cells DNA to carry out random algorithms. But let’s not digress.

The big diversity may increase the chance to absorb that of pathogens but what is also needed is to have match in numbers to have requisite variety. (This is why I really find variety, in cybernetic terms, such a good measure. It is relative. And it can account for both number of types and quantities of the same type.) If the number of matches between B-cell receptors and antigens is enough to register “attack”, the B-cells get activated by the T-cells and start to release antibodies. Then these successful B-cells go to a lymph node where they start to reproduce rapidly . This is a reinforcing loop in which the mutations that are good match with the antigens go to kill invaders and then back to the lymph nodes to reproduce. Those mutations that don’t match antigens, die.

That is really efficient and effective. But at the same time, the random generation of new lymphocytes with diverse shapes continues. Which is quite inefficient when you think of it. Most of them are not used. Just wasted. Until some happen to have receptors that are good match of a new invader. And this is how such an “inefficiency” is a precondition for survival. It should not just exist but be sufficient. The body does not work with what’s probable. It’s ready for what’s possible.

(Note: This is the mainstream explanation of how the immune system work. There are other theories, and some of them  – this one for example – I find way more convincing, especially when  comes to the self/non-self problem. However, in all explanations the phenomenon of requisite inefficiency is equally prominent. )

The immune system is not the only complex system having requisite inefficiency. The brain, the swarms, the networks are just as good examples. Having the current level of study, the easiest systems to see it in are ant colonies.

When an ant finds food, it starts to leave a trail of pheromones. When another ant encounters the trail, it follows it.  If it reaches the food, the second ant returns to the next leaving trail as well. The same reinforcing loop we saw with the B-cells, can be seen with ants. The more trails, the more likely the bigger number of ants will step on it, follow it, leave more pheromones, attract more ants and so on. And again, at the same time there always is a sufficient amount of ants moving randomly which can encounter new location with food.

The requisite inefficiency is equally important for social systems. Dave Snowden gave a nice example coincidently again with farmers but in that case experiencing high frequency of floods. Their strategy was to build their houses not in a way to prevent the water coming in but to allow the water to quickly come out. He calls that “architecting for resilience”:

You build your system on the assumption you prevent what can fail but you also build your system so you can recover very very quickly when failure happens. And that means you can’t afford an approach based on efficiency. Because efficiency takes away all superfluous capacity so you only have what you need to have for the circumstances you anticipate. […] You need a degree of inefficiency in order to be effective.

It seems we have a lot to learn from B-cells, ants and farmers about how to make our social systems work better and recover quicker. And contrary to our intuition, there is a need for some inefficiency. The interesting question is how to regulate it or how to create conditions for self regulation. For a given system, how much inefficiency is insufficient, how much is just enough and when it is too much? May be for the immune systems and ant colonies these regulatory mechanisms are already known. The challenge is to find them for organisations, societies and economies. How much can we use from what we already know for other complex adaptive systems? Well, we also have to be careful with analogies. Else, we might fall into the “best practice” trap.

(See also More on Requisite Inefficiency)