Unfortunately, as can be seen from his comments to those posts, one person who was definitely not happy about such ideas was the originator of Cynefin, Dave Snowden. So there’s evidently a major problem for us there.

What is clear is that, whether Dave likes it or not, a substantial community already uses Cynefin concepts and Cynefin terminology to describe a kind of meta-methodological ‘solution-space’ within which various methods, methodologies and tactics can be situated, and their respective appropriateness for specific contexts can be assessed. What’s also clear is that, as far as Dave is concerned, we are no longer permitted to use the term ‘Cynefin’ for this ‘framework-that-occupies-much-the-same-conceptual-space-as-Cynefin’: we do need to find an alternative term for this.

In short, to describe that ‘solution-space’, it seems we now need to move beyond Cynefin.

To do that, we need to identify:

• the role and purpose of this ‘not-Cynefin framework’
• how it draws from the published Cynefin framework and/or common usages of that framework
• how it extends and/or differs from the published Cynefin framework
• summarise how this framework would be used in practice

Once we’ve done that, we can perhaps start looking for an appropriate alternative term to describe it.

This is again going to be long, so I’ll stop here for a moment with a ‘Read more…’ link.

(Apologies: some of this will necessarily be somewhat technical at the start, but the examples later on should bring it down to the practical and concrete.)

It’ll be obvious that there’s a real risk of confusion here around how people have used the Cynefin framework, Dave’s now more-explicit intent for how the ‘Cynefin’ term ought to be used, and the somewhat different direction that this specific usage of Cynefin-like concepts needs to go. In the hope that it’ll reduce misunderstandings, I’ll use the following labels:

• CF (‘Cynefin framework’): the basic Cynefin diagram of four domains (currently labelled ‘Simple’, ‘Complicated’, ‘Complex’ and ‘Chaotic’) around a central region of ‘Disorder’
• CT (‘Cynefin/Cognitive-Edge techniques’): a specific set of techniques drawing on formal theory of complex-adaptive-systems and related science, using CF in various ways, but particularly as an illustration to describe both the problem-space and where those techniques should be situated in terms of overall ‘sense-making’
• UF or ‘the framework’ (‘unnamed framework’): a framework that, like CF, uses an intentionally-loose categorisation of four domains and central region to describe an overall ‘solution-space’, but also permits multiple alternative yet related ‘views’ into that overall solution-space

We also need to distinguish between problem-space and solution-space:

• problem-space is the context of the problem and its underlying factors – for example, in CF terms, whether the factors in the problem can be considered to be Simple (i.e. display linear causality), Complicated (e.g. linear causality with feedback loops and delays), Complex (e.g. retrospective causality) or Chaotic (i.e. display no apparent causality)
• solution-space is the context of the proposed solution and its relationship to the underlying factors of the problem-space – for example, in CF terms, whether the solution-method assumes that everything remains the same (Simple), contextual but predictable (Complicated), highly contextual (Complex) or in some part inherently unique (Chaotic)

CF and UF both describe the problem-space and solution-space; CT is in essence a defined set of techniques that can act on any part of the problem-space but centre on the Complex domain in solution-space.

The reason why this distinction is important is that CT goes “from solution to problem” – in other words, much like most other forms of pre-constructed ‘solutions’, it needs only to seek out those problems for which its techniques are appropriate. But enterprise-architecture, knowledge-management and many other disciplines necessarily operate the other way round, “from problem to solution” – the parameters of the problem need to be identified, and then an appropriate solution identified to match those requirements. The comparison between problem-space and solution-space also indicates the probable consequences of any mismatch. Solution-space is largely irrelevant to CT because for the most part it is already situated in solution-space (specifically, certain regions within the Complex-domain): all it needs to do is find appropriate problems in problem-space to match what it can do.

It should be emphasised that, unlike the CF/CT pairing, no specific problem-space techniques are associated with the framework. (In other words, there is as yet no ‘UT’ equivalent to CT, nor is there likely to be in future. Although both are related to CF, it should become evident below that CT and the framework have fundamentally different functions and purposes: it therefore makes no sense to suggest, for example, that the framework could somehow be ‘better’ than CT, or vice versa.)

Framework role and purpose

The framework (‘UF’) provides a meta-methodology model describing a generic ‘solution-space’, in principle covering every possible method, methodology and pattern of tactics.

The purpose of the model is to provide a means to identify, situate and assess appropriateness of methods within different contexts, and, by corollary, identify probable characteristics of appropriate methods (in solution-space) for different methodological contexts (in problem-space).

The model itself should provide a means to categorise and describe any possible methodological context, including itself.

Similarities to Cynefin

The framework needs to identify distinctive regions or ‘domains’ within the overall solution-space. Terms such as Simple, Complicated, Complex and Chaotic usefully and meaningfully describe such regions.

All potential solutions start from an initial condition of inherent uncertainty (“I don’t know what to do”), described in CF as ‘unknown’ or ‘disorder’.

The regions do not have strict boundaries; there are many contexts in which the regions may overlap or blur. The framework is not a simple two-axis model.

The process of identifying an appropriate solution to a given context in ‘problem-space’ consists of following pathways in solution-space. These pathways may lead to appropriate or inappropriate methods, in relation to the actual needs in problem-space.

The framework provides a means to situate methods and tactics within solution-space. As in CF (but not CT), in itself the framework does not mandate any specific method or technique for use in any given context in problem-space.

The solution-space described by the framework has strong cultural connotations; it is best understood as not so much an abstract concept as an experience, even a way of life, and also has complex connotations of belonging, ‘homecoming’, commitment to and responsibility for place and context ‘as itself’. Aspects are similar in some ways to the Australian Aboriginal concept/experience/etc inadequately translated as ‘the Dreaming’, and also to the Bantu notion of ‘ubuntu‘. In English this may be represented, if somewhat inadequately, by terms such as ‘place’ or ‘space’; Dave Snowden’s use of the Welsh word ‘cynefin’ is particularly apposite here. The framework will need a name that similarly reflects this richness, depth and complexity of meaning.

Extensions to or differences from Cynefin

The framework differs from and extends CF by permitting multiple alternate views into the overall solution-space, whilst still always using the primary set of regions as a guide for comparison and as a base for common interpretation. (Note that some of these views may be simple two-axis matrices, or two-axis spectra. However, this does not mean that the framework ‘is’ a two-axis model – each is simply a selected view into the solution-space, not the solution-space itself.

The framework differs from CT (and to some extent from CF) in that it is not explicitly based in one specific category of science or methodology. Instead, as a ‘umbrella’ description of solution-space, it permits multiple bases of theory, considering theory to be another type of view into the overall solution-space. The model does not inherently privilege any theory over any other: in principle, the overall frame should be theory-neutral. (Note that the purpose of the framework is meta-methodology, not methodology. Any methods used to assess the nominal ‘appropriateness’ of any other method should themselves be situated and assessed recursively within the solution-space. This recursion would apply, for example, to all the methods and techniques within CT.)

The framework differs from CT and extends CF in that it does not privilege any one domain within solution-space. (From all its descriptions, CT appears to privilege the Complex domain in solution-space.)

The framework differs from CT in that the emphasis throughout is on recursive, layered meta-methodology, not method; its methods apply to recursive assessment of appropriateness of methods within solution-space, not to resolution of specific problems in problem-space. In framework terms, CT represents only one amongst a near-infinite range of possible categories of method within the solution-space.

The framework differs from CT in that its methods apply to solution-technique selection, not end-problem; it relates to an earlier stage of problem-resolution than does CT, which assumes that the technique(s) to be used have already been selected. The emphasis is on identifying an appropriate solution-technique before problem-solution can take place. The methods used in conjunction with the framework will often include those familiar in other forms of quality-management and process-improvement, such as the PDCA cycle and After Action Reviews, but in some cases might well draw on CT methods.

The framework contextually differs from CT and extends CF in its description of solution-selection pathways. For example, within the framework, in principle all solution-selection pathways leave the initial ‘disorder’ domain via the Chaotic domain, and may traverse into other domains from there; some solution-techniques remain primarily or wholly situated within the Chaotic-domain of solution-space, though most do not. For CT, all solution-selection pathways should traverse through the Complicated-domain of solution-space (for evaluation in formal scientific terms), and should ultimately lead to regions that are situated primarily or wholly in the Complex-domain of solution-space.

Suggested practice

The previous posts ‘Complexity, chaos and enterprise-architecture‘ and ‘More on chaos and Cynefin‘ summarise the principles for use of the framework, though should now be read in terms of the above description rather the standard Cynefin (CF and/or CT) concepts.

Within solution-space, specific techniques tend to go through the standard Gartner hype-cycle:

• ‘Technology trigger’ (though note that the trigger for a new solution-technique is not necessarily technological): the technique becomes available (i.e. known) within solution-space, usually closely matched at first to a single narrow region in problem-space
• ‘Peak of inflated expectations’: usage of the technique is extended outward into broader and broader regions of solution-space, in some cases to the point where it is believed to be ‘The Answer To Everything’
• ‘Trough of disillusionment’: repeated failures with use of the technique outside of its appropriate region(s) in solution-space lead to increasing rejection of the technique (and often abandonment in search of the ‘next Best Thing’), to the point where it is sometimes believed to have no useful application at all
• ‘Slope of enlightenment’: there is a belated awareness that the technique is neither an ‘answer to everything’ nor ‘useless’, but needs to be situated in appropriate regions of solution-space (i.e. matched to appropriate contexts in problem-space)
• ‘Plateau of productivity’: once the appropriate regions in solution-space have been identified, it becomes ‘self-evident’ as to which types of end-problems the technique applies best, and the factors that drive process-improvement for contexts in which this specific technique is used

For example, Taylorism and Six Sigma apply very well to contexts which have very high repeatability (i.e. are firmly situated in the Simple-domain in solution-space); but they are not well-suited for use outside of that region (hence the misuses of Six Sigma that Dave Snowden decries as ‘sick stigma’). The slide from Dave’s seminar that contrasts Scientific-Management, hard-Systems Thinking and (CT) Sense-Making shows the historical progression of the early stages of the hype-cycle in each case, but crucially it does not situate each category of techniques in solution-space. It correctly describes how techniques have arisen in part as a response to the downward trend of ‘failures’ after the hype-cycle ‘peak of inflated expectations’, but the overall result is a misleading impression that each category is somehow ‘better’ than the previous category in the sequence, when in reality ‘better’ is actually a highly contextual term.

Concept Lifecycles ((c) Dave Snowden / Cognitive Edge 2010)

As suggested in the slide, these three examples also provide stereotypes for techniques that are primarily or wholly situated in specific regions of solution-space:

• Scientific Management: Simple-domain
• hard-Systems Thinking: Complicated-domain
• (CT) Sense-Making: Complex-domain

But crucially there are no techniques listed for use in the Chaotic-domain of solution-space, in which the context is primarily or wholly non-repeatable. (An example given in one of the previous posts was sales, in which each individual sale is ultimately a unique ‘market of one’.) Although systematic processes exist for identifying and validating techniques in the Simple, the Complicated and, increasingly, the Complex domains, very little seems to have been done in the Chaotic domain in solution-space – it seems instead to have been dumped into the ‘too-hard’ basket as ‘outside the scope of science’, and hence all but abandoned.

Yet most if not all real-world problems incorporate elements of inherent uncertainty. Hence disciplines such as enterprise-architecture, quality-management and knowledge-management, which of necessity cover the whole of problem-space and solution-space, do definitely require meta-methodology techniques that are able to work within the Chaotic region. Since the other regions of solution-space are already fairly well covered (such as with narrative-knowledge techniques, statistical quality-analysis and so on), transits into, out of and within the Chaotic regions of solution-space (i.e. low-repeatability to non-repeatability) will probably provide the most urgent uses for the framework. Dave Snowden alludes to this in the quote used in one of the previous posts:

I have a strong bias towards the natural sciences and the Cynefin framework is built from a science based position. However … I have seen too many examples of dowsing not to believe it works in some way, I can also see that in all the cases it is a deeply embodied skill that cannot be taught. …  I also have to respect the fact that all controlled tests have failed to establish authenticity. This provides an interesting dilemma. On the one hand I have seen it work with water engineers, and with the man/jcb symbiosis that dug out the drive to the side of my house, on the other hand controlled tests have failed to validate. That means we have a really interesting anomaly that requires investigation.

Dowsing therefore provides a good test-case for the Chaotic-domain of the framework’s solution-space: the techniques must produce concrete real-world results, yet by definition operate in inherently non-repeatable conditions.

Perhaps relevant here is that there is now a fully-worked example for this, documented as the book Disciplines of Dowsing, which I co-authored with archaeographer Liz Poraj-Wilczynska. (Archaeography is a relatively new sub-domain of formal archaeology, providing a disciplined, structured bridge between conventional ‘objective’ archaeology, art and culture.) Within it we used a variant of the framework with domains described as follows:

• Simple domain (‘inner / truth’): ‘Mystic’ mode (aka ‘Priest’)
• Complicated domain (‘outer / truth’): ‘Scientist’ mode
• Complex domain (‘outer / value’): ‘Magician’ mode (aka ‘Technologist’)
• Chaotic domain (‘inner / value’): ‘Artist’ mode

A set of characteristics are defined for each mode:

• its role within the overall discipline or practice (of dowsing, in this case)
• the type of ‘world’ (region of solution-space) which the mode will cover
• the core emphasis and types of response within the mode
• when to use this mode within the overall discipline
• how to identify when this mode is already in use – “You’ll know you’re in this mode when…”
• rules, guidelines or principles that inherently apply within this mode
• warning-signs of potential problems within this mode (i.e. misalignment between problem-space and solution-space)
• ways to bridge across to other modes (i.e. triggers for transitions and/or pathways within the solution-space)

These are summarised in a two-page reference-sheet and described in more detail in pages 31-70 of the e-book version of ‘Disciplines of Dowsing‘, which can be downloaded for free from here.

During live field-work, the practitioner will, in effect, frequently return to solution-space to tackle routine methodological concerns, including:

• “Which mode am I in now?” (i.e. which region of solution-space) – compare the roles for each mode, or the “You’ll know you’re in…” lists, or work it out backwards from the rules or guidelines in use at present
• “If I’m in this mode, which rules apply?” (i.e. intersection between problem-space and solution-space) – look up the rules from the list for the respective mode – and check against the respective ‘warning-signs’ list to test if they’re being blurred with the rules for another mode
• “Which mode do I need to be in?” (i.e. selection of region within solution-space) – look up the “You need to use when…” lists, and pick out the appropriate mode for the task in hand (i.e. in problem-space) – and remember also to use the new mode’s rules, not those of the previous mode
• “If I’m in this mode, how do I switch cleanly to another mode?” (i.e. transition within solution-space) – look at the respective ‘bridge’ list – though note that switching to the ‘diametrically-opposite’ mode (i.e. Chaotic <-> Complicated, or Complex <-> Simple) may well seem hard at first

The book also includes (in pp.71-95 of the e-book) what are described as ‘Seven Sins of Dubious Discipline’, which exemplify particular ‘clusters’ of common errors within solution-space:

• The Hype Hubris – in effect the same problems as described at the ‘Peak of Inflated Expectations in the Gartner hype-cycle
• The Golden-Age Game – similar to the Hype Hubris, but frequently coupled with an extraordinarily arrogant ‘holier-than-thou’ type of self-image
• The Newage Nuisance – a kind of half-baked misuse of techniques, in effect a complete mismatch between solution-space and problem-space
• The Meaning Mistake – a specific class of of errors that can occur in relation to the Complicated-domain of solution-space (for a real-world example, see my earlier post ‘Dowsing the flames‘)
• The Possession Problem – a specific class of errors that occur at another level of meta-methodology, when understanding of both problem-space and solution-space is forced into the Simple-domain via mistaken notions about ‘possessing the truth’ about a given problem and/or solution
• The Reality Risk – a more subtle class of risks (and sometimes genuine dangers) that may occur in the Chaotic-domain of solution-space
• The Labyrinth Lessons – issues that arise as a consequence of specific stages in the sequence of skills-development, both in implementation of the actual skill (i.e. in problem-space) and in developing and understanding the principles and practice of the skill (i.e. in solution-space)

These tactics are all directly applicable and re-usable in just about any other discipline or business-domain, especially those that of necessity must transit into or through the Chaotic-domain of solution-space. For example, we’ve already adapted these for use in archaeography and archaeology; I’m currently in the process of adapting them for use in values-architecture and enterprise-architecture.

Constructive comments, suggestions and other ideas most welcome – there’s a lot we can do with this.

Over to you, anyway – and thanks.

Anyone have any suggestions for an alternate term that could be used for this purpose, please?

Many thanks.

Cynefin framework (original (c) Dave Snowden / Cognitive-Edge c.2003)

Cynefin is probably one of the most useful conceptual tools that I hold in my ‘consultant’s toolkit’. It is an enormously powerful and enlightening framework to understand the relationships between the simple, the complicated and the complex, and to understand why long-proven approaches such as Taylorism and Six Sigma can sometimes (or often, these days) go spectacularly wrong.

Yet for several years now – in fact pretty much since I first encountered Cynefin – I’ve been concerned that there’s been very little attention paid to the role of the Chaotic domain. So that’s the theme I want to tackle here: how may we reclaim the Chaotic, to make Cynefin more complete?

(I’d better say upfront that there’ll be a fair amount here that Dave and others may disagree with, sometimes quite vehemently – and that’s okay, because this is definitely a ‘work in progress’, and probably with gaping holes in the reasoning in places. I need that critique if this is going to work in practice. In no way do I consider that any of the other work in Cynefin is somehow ‘wrong’ – particularly not the work that Dave and others have been doing in the Complex space, which I regard as crucially important in business and elsewhere. All I’m suggesting here is that perhaps we need to approach the Chaotic domain with the same degree of discipline as we do with the others – and not simply ‘run away’ to the Simple or the Complex as soon as we hit the Chaotic, which is about all that standard Cynefin offers at the moment.)

This one will again be long (my apologies…), but should be useful to anyone who’s familiar with Cynefin, or has any practical concerns about how to handle inherent uncertainties in business and elsewhere. More after the ‘Read more…’ link, anyway.

As I understand it, the Cynefin framework describes a ‘diagnostic/solution space‘ – four distinct categories of tactics to filter impressions of an unknown (‘disordered’) context, so as to support sense-making and then decisions for appropriate action. To put it at its simplest if perhaps most tangled, Cynefin is a decision-support framework to support the decisions needed to support subsequent decision-support.

These four sense-making modes or ‘domains’ are usually presented in flat two-dimensional form, as in the diagram above. But for reasons I explained in the previous post, they also fit well with the traditional Western ‘four elements’, which, roughly speaking, equate to physical, conceptual, relational and aspirational. These represent intersections of fundamentally different properties:

• physical: entity or entity-property, transferable, alienable (if I give it to you, I no longer have it)
• conceptual: entity or entity-property, transferable, non-alienable (if I give it to you, I still have it)
• relational: exists between entities, not transferable, requires active support from both ends
• aspirational: exists between entities, not transferable, requires active support from one end only (but may be dropped at the other end)

(In business, a simple example of an aspirational property is a brand: the commitment to the relationship comes from one end only – the ‘consumer [I hate that term, but it does apply here...] – but the relationship is destroyed if the brand is lost, and may not easily be substituted fro another.)

If we view Cynefin from that perspective, the four domains can also be understood as dimensions that mark out a ‘solution-space’ surrounding the initial ‘disorder’ of the unknown:

Cynefin domains as tetradian dimensions

To find appropriate techniques for solutions (responses) for the context, we need to be able to move around the solution-space in an intentional, integrated way. The Cynefin dimensions align somewhat with the ‘four elements’, so we can use the latter to suggest probable places to start: if we’re dealing with physical things, Simple is probably best, if it’s conceptual we would start with the Complicated, and so on. But the catch is that pushing hard in one dimension tends to preclude use of the others – hence Taylorism, which works so well in the predictable physical world, would instead be a flat-out disaster if we assume that it will work just as well in the complex messiness of interpersonal relations (the Complex domain).

Which brings us back to Dave’s slide from the seminar, about the lifecycles of scientific modalities in the Cynefin space:

Concept Lifecycles ((c) Dave Snowden / Cognitive Edge 2010)

Dave here implies that there’s something close to a linear development of management-science, with each modality superseding the next. But that’s actually not what happens: instead, each new modality becomes the ‘scientific fashion‘ for a while, following much the same adoption-pattern as the well-known hype-cycle. The slide above only shows the first part of the hype-cycle, the initial trigger, and then the rise to the ‘plateau of inflated expectations’ – where attempts are made to use the techniques for everything (as in the old adage that “if all you have is a hammer, everything looks like a nail”) - followed by the descent into the ‘trough of disillusionment’. In the later part of the hype-cycle, we reach the ‘plateau of productivity’ – which, in essence, means that we learn to use the techniques only in contexts where they are appropriate. Hence, again, Taylorist time-and-motion analysis does work very well in certain specific contexts; likewise Six Sigma, and so on.

The danger with the ‘ascent’ notion implied in the slide above is that it risks leading us to the same kind of deluded ‘holier-than-thou’ supremacism. In other words, much the same that we can see, for example, in Don Beck and Ken Wilber’s so-called ‘Integral‘ version of Spiral Dynamics – initially a set of tools that are very useful in certain specific contexts, but one that’s been mangled beyond sense and context into an overblown cult-like ‘Theory That Explains Everything’, that has understandably triggered Dave’s ire on more than one occasion. Yet we can see the same risk also applies here: that initial excitement and exuberance at breaking free of the constraints of the ‘old regime’ are what drive the mistaken millenialism of the hype-cycle. And would not be helpful if Cynefin too becomes a cult, in the same way the Taylorism and Six Sigma have done in the past.

One way to prevent that from happening is to recognise that the S-curves in the slide aren’t a linear development: rather, they’re explorations into solution-space, with a specific emphasis in each case along a specific dimension of that solution-space – Scientific-Management for Simple/Physical, Systems-Thinking for Complicated/Conceptual, Dave’s Sense-Making for Complex/Relational, and so on. Right now, it’s the “and so on” bit that interests me – because right now there isn’t an equivalent science for the Chaotic/Aspirational segment of the solution-space – yet it’s something we definitely do need.

The reason why it’s important should become clear once we look at Cynefin in relation to time:

In real-world practice, the closer we get to the real-time of ‘NOW!‘, the less time we have to think – all we have time for is to do. In effect, this forces us towards a very limited range of choices across a spectrum of ‘truth’ to ‘value’ – in Cynefin terms, either Simple or Chaotic, because the time needed for Complicated analysis or Complex experiments is a luxury we simply do not have.

But Cynefin at present does not have any means to operate within the Chaotic domain. Instead, we’re told, we must ‘act > sense > respond’, either ‘taking control’ so as to push the context into the Simple domain, or grab hold of a few random nominally-unrelated items as the content for subsequent abductive reasoning in the Complex domain. As can be seen in the later part of the seminar, Dave has done brilliant work with fitness-landscapes in his SenseMaker software to bring the Complex closer to real-time – but it’s still not actually the in-the-moment ‘now-ness’ of the Chaotic, and arguably never will be, because by definition the assessment always happens after rather than during the event.

So we need something in Cynefin to fill that hole – because on the one hand the Simple-domain ‘solutions’ are likely to be too simplistic, and on the other we don’t have the time we need to do anything else.

As for why we need it, contrast marketing with sales.

Almost all the classic marketing techniques sit either in the Complicated-domain – trend-analysis, market-segment analysis and so on – or somewhere near the Complicated/Complex border – test-marketing, the dreaded ‘focus groups’ and the like. More recently there’s been a lot more emphasis on viral-marketing, ‘social CRM‘ and the like, which is more solidly into the Complex-domain regions of the solution-space. The point is that it takes time, and it works with large numbers of actual and/or potential events.

But at the point of action, sales is always dealing with ‘market-of-one‘ – an individual quantum-decision to either buy or not-buy. Marketing helps us before that event; it will probably help us after the event; but by definition it can play no part at the immediate instant of the event. Each sale is an internalised quantum-event, a literally one-off decision in the midst of chaos – and no amount of external analysis or assessment is going to change that.

Using this concept of a Cynefin ‘solution-space’, the preferred approach for marketing/sales for most of the past century was to prevent the apparent ‘need’ for a decision – in other words, to ‘take control’ of the market, and force everything into the Simple-domain. A single monopoly for every industry; you can have any colour you like as long as it’s black; no choice, other than to buy or not-buy – and marketing-pressure, peer-pressure and blanket advertising aimed to remove even that apparent choice. Yet these days that Simple option has been eroded by factors such as proliferation of vendors, globalisation and, especially, the internet – so much so that sales-folk might well say “it’s Chaos out here in the market”. In other words, it’s shifted from Simple to Chaotic – from ‘truth’ to value.

(Hence, in my own field of enterprise-architecture, the importance of values-architecture, as summarised here, here and here.)

This is the point where Dave and I diverge, philosophically speaking.

Dave’s background is in physics, and thence to cognitive-science. I would say that he is, without question, one of the few real masters that we have at present in applying that category of knowledge and experience to real-world problems. As someone with such a strong sciences background, I would imagine his natural reflex when faced with any methodological difficulty is to go back to ‘truth’, go back to the science – and in most cases, that’s probably the most reliable approach to take. Yet by definition, it cannot succeed with the Chaotic domain, because in every science, all sensemaking is fundamentally dependent on repetition – and again by definition, there is no real repeatability in Chaos. The cross-over point varies from one context to another, but conventional physics suggests that Heisenberg uncertainty is only resolvable once we move above ten quanta or so; below that point, conventional ‘truth’-based scientific analysis no longer makes sense. Dave in fact alludes to this in one of his comments to the previous post:

I have a strong bias towards the natural sciences and the Cynefin framework is built from a science based position. However … I have seen too many examples of dowsing not to believe it works in some way, I can also see that in all the cases it is a deeply embodied skill that cannot be taught. …  I also have to respect the fact that all controlled tests have failed to establish authenticity. This provides an interesting dilemma. On the one hand I have seen it work with water engineers, and with the man/jcb symbiosis that dug out the drive to the side of my house, on the other hand controlled tests have failed to validate. That means we have a really interesting anomaly that requires investigation – but it does not allow a strong claim for authenticity and the solution will [not?] be scientific.

Dowsing (or ‘water-witching’, in the US) is a good example of a Chaotic-domain context – one which we’ll come back to shortly. Yet as Dave implies, it seems that the only available option in the scientific approach is to force an immediate break-out from the Chaotic domain, into somewhere where repeatability can apply. This would usually be a move to the Complicated domain, such as with statistics-based ‘chaotic attractors’ and the like; or, as Dave has demonstrated so well, into the Complex domain, with fitness-landscapes and ‘outlier-detection’. But it still doesn’t work on the Chaotic domain as itself: from the scientific frame, it seems that the only way we can work with Chaos is by not being there. Which, these days, is hardly a realistic option, because it’s chaos everywhere. And yet we’re still stuck with no way to fill that gaping hole in Cynefin’s solution-space.

My own background is almost the opposite to Dave’s. Although I majored in sciences all the way through school, at university-level I switched over to the arts: specifically, to graphic-design – typography and the like – which is why some years later I became one of the pioneers in creating what is nowadays called desktop-publishing. In essence I’m a technologist, not a scientist; I place much more emphasis and much more trust in usefulness than purported formal ‘proof’. Hence perhaps unlike Dave, my natural reflex when faced with any methodological difficulty is to go to the ‘value’-side of the spectrum – Complex or Chaotic – rather than the ‘truth’-side – Complicated or Simple. Where Dave would, I presume, turn to the peer-reviewed journals, my reflex is to go back to first-principles, usually by direct observation of the context. I’ll freely admit that in Complicated-domain contexts my approach is arguably less reliable than that of the scientist; but because it makes no assumptions about repeatability, it can work within the Chaotic domain on its own terms. That’s the crucial difference.

The other key difference is that I approach the Cynefin frame not as a scientist, but as a methodologist – which is not necessarily the same thing! I’ve been working on various aspects of these themes for probably more than forty years; for example, it’s almost a quarter of a century since I first published my book Inventing Reality, which addresses the same overall space in a rather different way. So when I first came across Cynefin, way back in 2003, I already had a lot of background to connect it with – and that background did include the Chaotic domain. For example, to me it makes useful sense to cross-map Cynefin with a variety of Jungian concepts:

I suspect Dave might not approve of this cross-map, but the point is that it’s useful; unlike a scientific context, it’s neither necessary nor appropriate to claim that it is in some way ‘the truth’. (Which from Dave’s perspective it isn’t, of course: it’s doubtful that there’s any direct scientific cross-map between the two.) And the reason it’s useful is that this kind of cross-map gives us pointers as to how to fill that Chaotic-domain gap in Cynefin – by focussing on usefulness rather than ‘truth’.

As Dave implies, dowsing is probably a good place to start. I’ve been involved with dowsing in various forms since studying with Keith Critchlow at the Architectural Association, many decades ago; I’m probably one of the very few people doing systematic methodological study of the field, and likewise one the few people who can formally identify the fundamental flaws  in every so-called ‘scientific’ study of dowsing to date by self-styled Skeptics. To put it bluntly, I do know what I’m doing there; most people don’t (and many – especially from the so-called ‘New Age’ of the market – frankly don’t have a freakin’ clue… ) Which means there’s a real need for discipline there – and in many other contexts too.

So whilst Dave might be horrified at what I’ve done, in fact Cynefin provides a very powerful base-frame for a systematic approach to methodology in dowsing – including the Chaotic domain. (See the book Disciplines of Dowsing – you can download the full e-book for free from here. There’s also a two-page reference-sheet that summarises all the basic principles and practice.) The same principles apply in other fields: for example, I’ve recently been working with a number of well-known archaeologists to develop formal methodologies for subjective archaeology. And the same principles also apply in business – as in the discussion above about marketing versus sales.

Very short summary from all of the above:

• the Cynefin framework defines a ‘solution-space’ within which to select tactics to resolve problems in business and elsewhere
• Cynefin, as currently defined, severely constrains the solution-space by providing almost no means to manage the Chaotic domain
• if we include the Chaotic domain in a disciplined way, it greatly expands our range of options in the solution-space

So, how do we include the Chaotic domain? Here are a few suggestions to start with:

Don’t panic! In the extremes of the Chaotic domain, everything and nothing is true; nothing is certain, nothing stays the same for long. Hence Douglas Adams’ immortal catchphrase may prove very useful here… Standard Cynefin asserts that the appropriate tactic here would always be ‘act > sense > respond‘, to push us into another domain as quickly as possible: but that kind of panic-response may well lose us the information that we most need. Often the best advice here is the exact opposite of that: “Don’t just do something – stand there!”

Find the still-point, ‘the calm at the centre of the storm’. Every tradition asserts that there is such a still-point; every tradition also admits that finding that still-point ain’t easy… Hence the importance of practice, practice, more practice, and yet more practice. Which brings us to…

Repetition. In the Complicated domain, doing the same thing and expecting it to come up with different results is considered crazy; but in the Chaotic domain, it’s one of the few tactics that really helps. In a truly chaotic context, doing the same thing will always lead to different results: so here, repeating the same thing over and over provides us at least with something that will remain the same each time. That kind of repetition may technically be subjective, but it’s about the closest that we can have to scientific-style ‘controls’ here. Repetition works.

Use principles as a focus. Principles provide a stable point of reference amidst the chaos. In the business-context, this is the vision and values of the organisation. (By which I mean ‘vision’ in the ISO-9000 sense, as a stable anchor for the quality-system – not the flaccid marketing-puff that’s usually passed off as ‘our vision’, and about which Dave rightly complains.) “When in doubt, go back to first-principles”: that will help a lot here.

Allow serendipity. In the seminar, Dave describes abductive reasoning as “the logic of hunches”, a bringing-together of “seemingly unrelated items”. In the Chaotic domain, it becomes clear that everything is related in some ways to everything else: the patterns that we find there – and that we then evaluate via abductive reasoning in the Complex domain, as Dave describes – are actually little more than our choices, about connections that we choose to perceive between ‘seemingly unrelated items’. But we first need to create space for those ‘unrelated items’ to arise in the first place. In his classic Zen and the Art of Motorcycle Maintenance, Robert Pirsig describes this as ‘fishing for facts’; and likewise William Beveridge includes chapters on the role of chance, the use of intuition, and the hazards and limitations of reason, in his equally classic The Art of Scientific Investigation. Yet to quote Louis Pasteur, “chance favours the prepared mind“: in the Chaotic domain we create space for the unexpected to happen, yet prepare the space with principles, with repetition, and ‘the calm amidst the storm’. But there’s also one other essential instruction…

Listen. Probably the single most important advice for any consultant – and for any salesperson, for that matter. Stop. Don’t talk. Just listen. “Nature abhors a vacuum”: if you provide a ‘still-point’, a quiet calm, you may be surprised at what comes in to fill in the empty space. And very useful, too. Create the space. Listen. That’s almost all we need to know about working in the Chaotic domain.

To conclude…

My apologies that this has been such a long post: I hope it’s been worthwhile for you. But I guess what it comes down to is this:

Dave’s view of Cynefin is, as he puts it, “built from a science based position”. The advantage is that it is rigorous, largely context-independent, and firmly grounded in current cognitive research. The disadvantage is that, almost by definition, it can provide no real guidance on how to operate in the Chaotic domain.

My view of Cynefin is based in methodology-practice rather than formal scientific theory, and focusses more on individual difference and individual skill. The disadvantage is that it is less rigorous, and often highly context-dependent, hence arguably less reliable in the Complicated domain, and perhaps the Complex domain too. The advantage is that it does provide consistent means to operate in all domains, including the Chaotic domain – and hence can provide more options and opportunities in the overall Cynefin ‘solution-space’.

Which approach is best? I would argue that it depends what you want to do: hence neither and both, really. But I hope this exploration helps in the choice of how to move around within the Cynefin solution-space, and that it provides some useful suggestions about other ways to use the real power of Cynefin.

Over to you for comments and suggestions, if you would?

From Induction to Abduction: a new approach to research and productive enquiry

This seminar will provide a summary of both the theory and practice of a new approach to research based on the large scale capture of self-interpreted micro-narrative.  The approach has been described as the first technique for distributed ethnography and has been developed over the past decade with project based funding from the US, UK and Singapore Governments in the context of risk assessment, horizon scanning, cultural mapping and weak signal detection.  It allows the linkage of research with knowledge management and impact based measurement.  Current projects involve measuring the impact of development projects in Africa, narrative based knowledge management for the US Army in Afghanistan and cultural mapping of various inner city communities within the UK.

The theoretical origins lie in the application of complex adaptive systems theory to social systems together with new understanding about the nature of human decision making from the cognitive sciences. The seminar will summarise the theory, but will also use a series of projects to combine theory with practice.  One of the goals is to create learning systems that work on continuous capture of material in the field as it happens linked with a capacity for feedback loops and sophisticated representations that allow people to learn by doing, building on the micro-narratives of day to day experience.  Narrative forms of knowledge lie between the experiential and the symbolic, allowing complex interactions and interventions in multiple social situations.

Abductive reasoning is, as Dave explains, “the logic of hunches”, and plays a key role in helping to develop understanding of how themes emerge in social contexts such as in business and elsewhere. It’s all fascinating stuff – very strongly recommended. The depth and versatility of the techniques will be a real eye-opener to anyone who hasn’t previously seen Dave’s work, and its applicability to whole-of-enterprise architecture and the like should be self-evident.

I will admit I do have mixed feelings about the way Dave develops and presents his work. On the one hand, he has a brilliant mind and is a brilliant presenter, and there’s no doubt at all that his tools and techniques, such as Cynefin and the theory and practice behind his Sensemaker software-suite represent real paradigm-shifts in the way we think about organisations and enterprises (in the broadest sense of those terms). But I do find it beyond tedious that he spends so much effort denigrating other people’s work – for example, Nonaka, Weick and Six Sigma (endless derided by Dave as ‘sick stigma’) all come in for attack in the first few minutes of the seminar. And I too have been on the receiving end of that same… well, I would have to describe it as an odd kind of sort-of-scientific bigotry… which is more than just annoying at times. And annoying not least because pretty much everything I’d tried to explain to him and that he’d dismissed with such vehemence – such as the nature of ‘magical’ processes and the role of ritual – Dave in fact now incorporates (though probably unconsciously) as significant if unacknowledged sub-themes in his work (as can be seen in various places in the video). But we have to take the ‘Dave Snowden’ package as a whole, I guess: and most of the contents of that package are important – definitely.

But there’s one slide, right at the start of the presentation, that I find especially fascinating:

Concept lifecycles (image (c) Dave Snowden / Cognitive Edge 2010)

There is indeed a clear historical sequence here, paralleling the shifts in the underlying scientific paradigms, from Newtonian to hard-systems to complex-systems in the present day. But there are two important points that are easy to miss here:

• in each case the old dominant paradigm remains useful, though is seen to describe a distinct set of special-cases rather than a grand ‘Theory of Everything’;
• the sequence does not stop here, with Dave’s ‘sense-making’ – it continues on to at least one more layer, and possibly two.

The reasoning for those assertions comes from Cynefin itself, plus a cross-map to this diagram above. As Shawn Callahan of Australian consultancy Anecdote explains in his excellent intro on the same web-page, Cynefin has a central (and ‘initial-state’) domain of ‘disorder’, and has four distinct domains of sensemaking and action:

• Simple: assumes simple cause-effect rules; sensemaking tactic is ‘sense -> categorise -> respond’
• Complicated: assumes linear causality, but accepts that these may include many factors, delays, feedback-loops etc; sensemaking tactic is ‘sense -> analyse -> respond’
• Complex: accepts that cause and effect are intertwined, leading to non-linearity and non-reversibility; sensemaking tactic is ‘probe -> sense -> respond’
• Chaotic: no identifiable cause-effect relationships; sensemaking tactic is ‘act -> sense -> respond’

Dave links the first three of these to the respective S-curves: Scientific Management is Simple, ‘classic’ Systems-Theory is Complicated, and his version of Sense-Making is Complex. Yet he provides no equivalent linkage for the Chaotic domain, and the listed tactic of ‘act -> sense -> respond’ literally consists of running away from the problem. Which is hardly a valid approach if the chaos insists on being sustained. Which, in the real world, it all too often does…

It’s often struck me that Dave’s great strength in the Complex domain seems also to create a real inability to describe any means to tackle the Chaotic domain. In this sense it does seem that, to use his own words, ”the old dominant paradigm suppresses the new idea” – where the ‘new idea’ is that the Chaotic domain does need to be respected in exactly the same depth as we now do for Complex and the others.

To me there are two key clues here.

The first is a direct warning from the classical tradition and elsewhere that running away is not a viable response in the Chaotic domain. The clue here is the Greek-derived word ‘panic’, which is what many people will experience when facing any kind of chaos. But the ‘pan-’ root literally means ‘the everything’ (hence panorama, pandemonium, and so on): what’s happening in panic is that everything and nothing is true at the same time. Yet that’s exactly what we need when we’re striving for innovation, or any other kind of search for new ideas: we need the ability to bring apparently unrelated themes together in new ways. And in practice we do that by not running away, but instead by ‘holding to the centre’, ‘finding the still-point’, ‘the calm at the centre of the storm’, and so on.

To find that centre, we turn to the other clue in what’s not in Dave’s diagram above.

The Simple domain is about control of Function, says Dave – in other words, the physical world, the physical dimension. Hence scientific-management came to the fore in the heyday of the assembly-line – and it still makes perfect sense within that type of context, where everything remains exactly predictable, exactly the same, just like most physical objects do.

The Complicated domain, says Dave, is about control of Information, the conceptual dimension. Hence ‘hard-systems’ thinking came to the fore in the heyday of the mainframe and the supercomputer, massive number-crunching and the like – and it still makes perfect sense in that context, of massively complicated cause-effect relationships between information-items.

The Complex domain, says Dave, is about control (or ‘situating’, rather) of the Network – otherwise known as the relational dimension. Hence Dave’s ‘Sensemaker’ and the like come to the fore in genuinely-complex social contexts, where meaning and ‘truth’ emerge from the interweaving between the individual and the collective in the respective physical, conceptual, social and aspirational milieu, in which everything and anything may become both cause and effect of everything else.

But what’s not there in Dave’s model is any consistent framework to tackle the Chaotic domain – instead, we’re just told to run away back to the safety of one of the other domains. And yet, following that same logic above, we can see straight away what its base would be: the aspirational dimension, the explicit choice of meaning and purpose – otherwise known in the enterprise-architecture as vision, values and principles.

From my discussions with him, Dave seems to dismiss this whole domain because to him it appears to have no identifiable science behind it. Yet I would suggest that this too may be the result of a too-close identification with his own concept of ‘science’, because as soon as we allow ourselves to move outside of the constraints of Western tradition of science, we will immediately find other traditions with at least the same levels of precision and discipline, if not more. The Australian Aboriginal concept of the Dreaming is one obvious example, an extremely sophisticated study of relationship with the land that is only now beginning to be understood in Western terms; likewise the Tibetan research into the period immediately before and after death; or, to give a more tangible example, the Polynesian science of navigation:

‘The Wayfinders’ [lecture-series] is a profound celebration of the wonder of human genius and spirit as brought into being by culture.

The entire science of wayfinding is based on dead-reckoning. You only know where you are by knowing where you have been and how you got to where you are…that your position at any one time is determined solely on the basis of distance and direction travelled since leaving the last known point… If you took all of the genius that allowed us to put a man on the moon and applied it to an understanding of the ocean, what you would get is Polynesia.

The consistent theme in each of these traditions is a very strong sense of purpose, intentionally embedded by and within the individual to act as a personal ‘guiding star’ that provides a known, certain ‘still-point’ under conditions on uncertainty and chaos. Hence, although Dave somewhat characteristically dismisses and debunks vision in the business context, I do believe he’s missed the point. Those are genuine skills, genuine sciences, every bit as valid as as the sciences behind scientific-management, systems-thinking and complexity-theory – and their niche of greatest applicability is the Chaotic domain.

Which, once we think of it that way, makes Cynefin complete.

Or rather, there’s one more layer to this. Each of the Cynefin domains has its own respective science, its own technologies and so on. But there’s also a need for a ‘meta-discipline’ to switch between the Cynefin domains, linking them together into a unified whole.

Checklists can provide some of that discipline; likewise a consistent iterative methodology such as the extended TOGAF-like cycle that I use in my own enterprise-architecture work. An explicit multi-dimensional model such as the tetradian can also help in this. And we have much that we could learn from the many non-Western traditions – or even from a better understanding of how science really works in practice.

But perhaps more, for here, we perhaps need to note that whilst Dave’s complexity-theory is useful – very useful indeed – it’s unlikely to be ‘the last word’ in the sciences that we need in enterprise-architecture. There’s still some way to go: and a more consistent, more honest approach to how we handle the Chaotic domain would seem to be the necessary next step in that journey.

After Dave Snowden started accusing everyone – especially me – of ‘pseudoscience’ and ‘psychobabble’ – I began to worry. What if he’s right? What if everything I do is just pseudoscience, caught up in a cult?

(Oops – another long one: better split it here with a ‘Read more…’ link)

I re-read Beyerstein’s list of characteristics of pseudoscience in Patrick’s Lambe’s post on “Is KM a pseudoscience?“, and started to worry even more. Here’s that list:

1. Isolation – failure to connect with prior and parallel disciplines
2. Non-falsifiability – no means to invalidate hypotheses
3. Misuse of data – leveraging data out of context or beyond validity
4. No self-correction, evolution of thought – often centred round a single ‘thought-leader’
5. Special-pleading – the claim that this is a special-case that can’t be measured in any other terms
6. Unfounded optimism – unrealistic expectations
7. Impenetrability – an over-dependence on complicated ideology and obfuscation, or bluster in place of debate
8. Magical-thinking – such as “the belief that good things will result from willpower alone”
9. Ulterior motives – particularly ulterior motives of a commercial kind
10. Lack of formal training – including certification schemes that link back to #4
11. Bunker mentality – such as complaints about being ‘misunderstood’ by others, and often linked to #5 and #7
12. Lack of replicability of results – especially replicability by others under controlled conditions

For example, I often work at the places where the IT-industry and consulting-industry converge, so I would need to test both of those against that list, putting a check-mark against any of the criteria that fail:

• #1? Check – not always, but way too often for comfort.
• #2? Check – ditto.
• #3? Check – often. Usually from myopia and questionable competence (“I guess we failed to take enough account of the human factors”: BPR), though occasionally from a rather more deliberate ‘sexing-up’ of the statistics to prop up the purported position (‘In Search of Excellence’ etc).
• #4? Check – often. (All those management fads…)
• #5? Check – again, too often for comfort. (Real business-case for IT-only KM or Enterprise 2.0, anyone?)
• #6? Check. (In fact rarely anything other than ‘unfounded optimism’ – at the start of a project, anyway.)
• #7? Check – lots.
• #8? Check – ditto.
• #9? Check. (Rarely anything else, perhaps? – BPR, anyone? ERP? the dot-com bubble?)
• #10? Check. (Look at most enterprise-architecture training, for example.)
• #11? Check. (It’s usually called ‘the IT/business divide’ – or worse, of course.)
• #12? Check. (Often we don’t want to replicate the results that we actually get…)

And so on, and so on. According to that review, it looks like almost the entire industry is based on little more than pseudoscience. Oops.

And we’ve already seen from Patrick Lambe that knowledge-management is perilously close to a pseudoscience too. Also ‘Oops’, I guess.

But what about Cynefin? Surely that can’t be a cult – especially given Dave’s position on pseudoscience and the like. Better go through that checklist again, just to make sure:

• #1: Isolation? Plenty of reference to cognitive-science and suchlike – but I don’t see any evidence of cognitive-science etc connecting back to Cynefin. Looks suspiciously like spurious science to me, then. Oops.
• #2: Non-falsifiability? References to ‘retrospective causality’ in the Complex domain look a bit questionable in this regard; likewise much of the definitions of the Complex and Chaotic domains, and the interactions therein. Oops.
• #3: Misuse of data? Ditto, it would seem. Oops.
• #4: No self-correction? There is a genuine community-of-practice here, but it seems often to be silenced by a single figurehead who claims to hold ‘the only real truth’ about the discipline. Oops.
• #5: Special-pleading? Tends to be very good about challenging ‘pattern-entrainment’ in others, but not so good at applying the same analysis to itself. Claims to be a ‘sense-making’ framework, but the only way to test the ‘sense’ that’s derived is in terms of the framework itself. Kinda circular, really. Oops.
• #6: Unfounded optimism? Probably. Best let that one pass as only a minor ‘oops’.
• #7: Impenetrability? Lots. There’s the ‘ganglionic cross‘ with its cryptic markings, and the insistent demand that all devotees acknowledge that there are five domains, not four; also near-religious wars as to what each of the domains ‘really means’. Oops.
• #8: Magical-thinking? All we really know is that Dave is almost obsessively against it – which by the usual psychological games probably means there’ll be lots. Complicated pathways between domains that somehow magically change things might be a good example. A bit uncertain, perhaps, but very likely to be ‘oops’.
• #9: Ulterior motives? Lots. Celebrity-status, serious-money consultancy-fees, training-fees (see #10), sales of software that can only be used by registered practitioners (see #10), and consumable-supplies that can only be purchased from the central organisation: sounds a bit like Scientology, doesn’t it? We’d have to be fair and remind ourselves that that applies to much of the IT-trade and consultancy-trade too, but even so that’s a really big ‘oops’.
• #10: Lack of formal training? Would-be practitioners generally need some serious consultancy-time under their belt, but the Cynefin training itself is defined, run, certified and validated only by the central organisation. In other words, worryingly circular and self-referential. Kinda sounds like NLP, doesn’t it? Oops.
• #11: Bunker-mentality? Probably not in most cases, but it’s notable that the figurehead has an unfortunate habit of fulminating about anything else that can’t be forced to fit within the preferred assumptions – such as denigrating Six Sigma as ‘Sick Stigma‘, and so on, regardless of where or how it’s used. So most practitioners probably okay, but the figurehead probably not. Oops.
• #12: Lack of replicability? Lots. By definition, pretty much anything in the nominal Complex or Chaotic domains is going to have limited replicability. (There’s good replicability in the Simple and Complicated domains, of course, but also no real need for Cynefin-style ‘sensemaking’ in those two domains, so we can’t really claim that one as a plus.) Just about any consulting-assignment will be in part unique, too, so again little to no replicability there, again by definition. Also, as Dave puts it, “every diagnostic is an intervention”, so the very act of enquiry changes the conditions of the experiment, impacting on any possible replicability. And if Cynefin experiments are only repeatable by Cynefin practitioners, and everything has to be assessed in Cynefin terms, it somewhat blocks the possibility of proper third-party ‘outside’ review – kinda like the worst of ‘armchair Freudianism’, for example. Another big ‘oops’.

So is Cynefin a cult? Apparently the answer is ‘Yes’, because according to Beyerstein’s criteria, it seems to fail the ‘pseudoscience’ test on just about every count. Almost the only place where it doesn’t fail, in fact, is in the two logic-based domains, Simple and Complicated, where Cynefin isn’t much use anyway. Either way, definitely ‘oops’.

[Brief note to Dave: yup, I'm well aware that that assessment above ain't exactly rigorous and peer-reviewed and the rest, but it's a darn sight more rigorous and honest than the cheap hatchet-job you tried to do on me over the past couple of days... yes, I am indeed still angry over that...]

But that result is kind of odd, because most of us find that Cynefin is a very useful tool in consulting practice – especially in dealing with what Cynefin describes as the Complex and Chaotic domains. Hmm. Seems like something doesn’t quite match up here, does it? And we’re left with two probable reasons for that mismatch:

• either Beyerstein’s criteria do test well for pseudoscience in areas where simple Newtonian-style logic applies, but tend to break down as soon as we hit anything closer to real-world chaos – so we’ll need something other than Beyerstein and the like to validate quality in those areas
• or the whole idea of ‘pseudoscience’ is a red-herring that can be used by superannuated academics to bully others and prop up some vain and misguided ‘mediaeval delusions’ of their own ‘superiority’, in areas where their putative expertise in formal ‘proof’ by definition can no longer apply, because by definition the ‘normal’ rules of replicability and the like are no longer reliable once we move into the Disorder, Complex or Chaotic domains

Both of these could be true, of course. But let’s be polite, and assume that it’s only the first of these: Beyerstein is probably useful in the Simple and Complicated domains, but we’ll need something else outside of that simplistic rule-based world.

But how can we tell when we’re outside of the rule-based world? And what can we use in place of Beyerstein and its ilk?

For the former, the key criterion is, once again, repeatability and replicability. In both the Simple and Complicated domains, there’s always an identifiable ‘right answer’, and if we do an experiment in the same way, we’ll always end up at the same results. (A few special-cases such as symmetries in complex-math give two or more ‘right answers’, but the set of answers in that case is still identifiable, so the basic principle remains sound.) In short, it’s repeatable, which means it’s also replicable. There are definable, straightforward (more or less!) and linear sequences of cause and effect, so if we don’t get the same right-answer under the same conditions, something’s wrong – hence falsifiability. Either true, or false: hence it makes sense to describe Cynefin’s ‘Simple’ and ‘Complicated’ as the two ‘truth’ domains.

In the other Cynefin domains, things get kinda messy. The Disorder domain is where we start, before we do any sensemaking, but it’s probably best to leave it out of this discussion for now. Yet in the other two domains – Complex and Chaotic - doing the same thing in the same way does not guarantee the same results. In the Complex domain, any apparent causality will at best become apparent only after the event (a context which Dave Snowden describes as ‘retrospective causality’); in the Chaotic domain, where everything is inherently unique in some way, even the concept of causality itself makes no sense, by definition. In effect, one way that we know we’re not in the ‘truth’ domains because it’s not repeatable. Clearly Beyerstein isn’t going to be much use to us here.

But there’s a nasty corollary that follows from this. If one test of the Complex or Chaotic domains is that it’s not repeatable, how can we tell the difference between that and plain ordinary bad-science in the ‘truth’ domains? – because that’s also not-repeatable too. Following the logic of this, we discover quite quickly that there’s no simple ‘truth’-based test that could distinguish between the two, because in both cases doing things the same nominal way may lead to different answers. Beyerstein in this instance would not only not be helpful, but could be actively misleading, always labelling the workings of the Complex domain as ‘wrong’ and therefore ‘pseudoscience’. Which it might be, or might not be, but there’s no way to tell: even the concept of ‘true’ versus ‘false’ doesn’t make much sense in that kind of context. Which is a problem.

But instead of trying to cling on to a notion of ‘true’ versus ‘false’ in a context where it won’t and can’t work, what does make sense is to use some concept of value. In other words, the test-criterion we need in the two ‘value’-domains Complex and Chaotic is usefulness, not ‘truth’.

Next question: what determines ‘usefulness’? By definition this is always going to be somewhat subjective and context-dependent – but that doesn’t mean that it’s a random free-for-all. Feyerabend’s anarchic dictum “anything goes” does indeed need to hold sway here, but it’s a disciplined ‘anything goes’ – a considered, functional form of anarchy, if you like (or even if you don’t). In turn, this brings us into the well-understood (if, by its nature, not necessarily well-defined) realm of quality-management. Which brings us to all those tools that Dave has so happily despised, such as Six Sigma and the like.

The problem – and again for some impenetrable reason Dave doesn’t seem to like this fact – is that each of these tools is context-dependent. Six Sigma, for example, is all about managing quality in terms of defects per million events: so it only makes sense to use Six Sigma if we have millions of exactly-identical events, which in practice places us in Cynefin’s Simple domain. If we’re not in the Simple domain, don’t use Six Sigma: simple, really. No need to make a song-and-dance about it and denigrate it as ‘Sick Stigma’, because it’s perfectly fine where it does work. Same with every other tool and technique: we switch between them according to context.

Within any given context, I’ve also found it useful to compare against a relatively simple yardstick of effectiveness. In my own practice, for the past decade or so, I’ve used a frame which describes effectiveness in terms of five distinct dimensions:

• efficient: makes the best use of available resources – typically the least wasteful use
• reliable: can be relied upon to deliver the required results, optimised over the required timescale
• elegant: aligns best with simplicity, clarity, ergonomics and other ‘human factors’
• appropriate: ensures that the delivered results are ‘on purpose’
• integrated: assists in bringing everything to work together as a unified whole

All of these need valid metrics: and in general, any appropriate metric will do – even if sometimes it’s just a 1-5 subjective scale, such as I use, for example, in my SEMPER organisational-capability diagnostic. Once again, in effect ‘anything goes’ here: the selection-criteria for metrics revolve around effectiveness, not ‘truth’.

And ‘truth’ approaches – such as Dave has so aggressively promoted in the comments to the previous post and elsewhere – really aren’t much help in deciding metrics and models here, because ‘truth’ only applies to part of the context, as we’ve seen above. True/false logic can’t lift itself by its own bootstraps: it can work within a set of assumptions and postulates, but it can’t be used to define or validate them. (Attempting to do so is known as ‘induction’, otherwise known as ‘cheating’ – or ‘pseudoscience’, of course.) So to make it work we have to jump up a step to a kind of ‘meta-level’, which, as I said in the previous post, might be called ‘nonrational’ or ‘arational’ or ‘metarational’, but I prefer to use the good old classic term ‘magical’. Which Dave doesn’t like, but that’s too bad, bluntly. (He also doesn’t like the alternate term ‘technology’, so he’ll just have to lump it, really.)

To help in deciding metrics and models and the like, we need to run the whole thing reflexively and recursively. (I’ve described in some depth how to do this in whole-of-enterprise architecture in my book Real Enterprise Architecture, if you’re interested.) The Cynefin frame is useful for this: we run it backwards, so to speak, to help us identify what needs to be handled in an appropriate manner for Simple, Complicated, Complex or Chaotic.

Even more useful than Cynefin for this, as mentioned in the previous post, is the frame that we developed for Disciplines of Dowsing. And as you’ll see from the two-page reference-sheet, the reason why it’s even more useful is that not only does it describes characteristic to help us identify which mode or domain we’re in, but also how to recognise when we’re losing discipline within that domain, and reasons and tactics to move from one domain to another. For example, if we’re in the ‘Scientist’ domain (i.e. Cynefin ‘Complicated’ domain), and we start getting emotional and defensive or aggressive about it, that warns us straight away that we’ve allowed ourselves to drift towards the ‘Priest’ domain (Cynefin ‘Simple’ domain), and either need to get the emotion out of it to return to the Scentist, or else intentionally switch to the Priest, or one of the other domains, as appropriate. The result is that we maintain discipline throughout the whole space – not solely in the ‘truth’ domains, as with Beyerstein and the like.

I’m well aware that dowsing and suchlike may feel a bit uncomfortable for some folks here, but unfortunately it’s the only example I have available right now. (There’s another variant in the Berg Time & Mind article, showing how to balance subjective disciplines in archaeological research with the more conventional ‘objective’ disciplines, but it’s essentially the same as in the reference-sheet.) Likewise the examples in the useful set of ‘seven sins of dubious discipline’ in the Disciplines book mostly relate to dowsing and archaeography. So if you’re working in knowledge-management or enterprise-architecture, for example, you’ll probably need to do some significant translation to make it work in your own work-context. But I assure that it is worth the effort: the result makes it a heck of a lot easier to work out what’s going on in a context –  especially the kind of dysfunctional, chaotic, blame-filled business-contexts that we so often have to deal with these days – because it helps to ensure that discipline of an appropriate kind is kept in play at all times.

So, to come back to the original question, is Cynefin a pseudoscience, a cult? Short answer, as we’ve seen above, is “probably not” – but you’ll probably need a little bit of magic to help you prove it!

Constructive comments and suggestions welcomed, of course – and many thanks for sticking with me this far on this.

(This has turned out to be an enormously long post – I’d better put a ‘Read more…’ link in here before continuing.)

This time the Tweet was from Cynefin creator Dave Snowden:

• snowded: NLP as Cargo-Cult psychology. Great paper http://jarhe.research.glam.ac.uk/media/files/documents/2009-07-17/JARHE_V1.2_Jul09_Web_pp57-63.pdf

The link points to a 7-page academic paper [PDF] by Gareth Roderique-Davies of University of Glamorgan, which purports to indicate that NLP (‘Neuro-Linguistic Programming‘ – a kind of self-hypnosis psychological tool) has no scientific basis, and is therefore ‘cargo-cult psychology’. I do take his point that there are some worrying flaws in NLP itself, and even more worrying flaws in many of the ways in which NLP is promoted and used these days. But I’ve seen this kind of ‘scientific’ review before, and I said so in my re-Tweet of Dave’s first message:

• tetradian: @snowded: “NLP as Cargo-Cult psychology. Great paper http://tr.im/IjbF ” <disagree: NLP has serious flaws but this is just a hatchet-job

The problem is that the reviewer is trying to assess NLP in conventional scientific terms – which makes no sense right from the start, though his world-frame would itself make it impossible to see why it makes no sense. (For enterprise-architects, by the way, this is the same underlying reason why IT-centrism or organisation-centrism is such a problem: the frame itself makes it impossible to see beyond the frame.) The title of Bandler and Grinder’s original book that defined NLP way back in the 1970s gives the reason why the scientific frame won’t work: it’s called The Structure of Magic.

Yup, that’s right: magic.

Most self-styled ‘scientists’ treat that word in the same way as IT-centric ‘enterprise’-architects treat business-architecture and beyond: namely a randomised, undifferentiated grab-bag of all the bits of reality (or business-reality, in IT-EAs’ case) that they don’t understand. And then complain that it’s a mess, and doesn’t make sense in their own chosen terms, and therefore doesn’t exist. Which is not exactly honest – and it’s certainly not helpful in practice, because magical-thinking is often the only way out of many everyday scientific, technological and business dilemmas and problems.

A small tale here. Everyone ‘knows’ that Isaac Newton was one of the world’s greatest scientists, yes? (Which he was, of course.) But not many people know that he was also interested in a great many other subjects, including religion, alchemy, astrology and much else besides: in fact he wrote more on alchemy, for example, than on all of his scientific studies put together. Edmond Halley, the then Astronomer Royal, was berating Newton for the latter’s studies of astrology: it was all nonsense, he said, ridiculous, utterly unscientific – or words to that effect, anyway. Newton’s short, sharp retort: “I have studied the subject, sir, and you have not!” End of conversation…

Which brings us back to NLP, and the structure of magic. As it happens, I have indeed “studied the subject, sir” – for more than forty years, in fact – and I guess most people reading this blog probably haven’t, so it might be useful if we do a quick tutorial here on the role and limitations of the scientific frame and mindset, and the contrasting role of magical-thinking. To do this I’ll pick up on another of today’s Tweets, from knowledge-management (KM) guru David Gurteen:

• DavidGurteen: Is KM a Pseudoscience? #KM http://www.greenchameleon.com/gc/blog_detail/is_km_a_pseudoscience/

This link points to an article by another key figure in KM, Patrick Lambe – much better thought-through and much more considered than the previous piece. Using a checklist from an article by Barry Beyerstein, he scores KM overall as having a score of only 5.4 out of 10 as a ‘rational endeavour’, and concludes that it is too close to a pseudoscience: “must do better”, he says.  But what that article misses, yet again, is the bald fact that trying to assess most of KM in scientific terms makes no sense. The only way we can make sense of it is via a magical approach.

(Yes, I know I still haven’t explained yet what I mean by “a magical approach” – give me a chance, I’m getting to that in a moment! )

Before we can look at magic, we need to understand science – as much for what it isn’t as for what it is. What it isn’t – as any competent scientist would admit – is “the answer to Life, The Universe, Everything”. Instead, it’s a particular body of knowledge, developed in terms of a specific set of methods and assumptions, and which can only make sense – or be useful and valid, rather – within a very specific set of constraints. Science has been extremely successful within those constraints - so successful, in fact, that many people fail to realise that by its own definitions it is not and cannot be successful outside of them. Therein lie many huge problems for KM, for enterprise-architecture and for many other disciplines – including magic.

This is perhaps best described in one of my all-time-favourite books, WIB Beveridge’s The Art of Scientific Investigation. First published in 1950, it’s been continually in print ever since, and remains one of the great classics of scientific research. I’ll have to quote from memory, as my copy is back in Australia, but his introduction starts like this:

Complex equipment plays a central role in the science of today, but it should never be forgotten that the most important instrument in research must always be the mind of the researcher.

Beveridge expresses concern that “perhaps not enough attention is paid to making the best use of it”. To this end he focusses on the actual practice of science, rather than solely on the end-products of that practice. Hence his book includes detailed descriptions and examples on strategy, hypothesis, the use of chance and intuition, and “the hazards and limitations of reason”. (Most of his examples come from his own field of biology and biochemistry, but they’re just as applicable to every other branch of science.) The summary in his chapter on reason is particularly important, though forgive me if I again have to quote from memory:

The origin of discoveries is beyond the reach of reason. The role of reason in science is to come afterwards, to review and reassess and to build a general theoretical scheme. … Most biological ‘facts’ are so uncertain that at best we can only reason on probabilities and possibilities.

And that last sentence remains just as true as ever, despite the advances of molecular biology and the like over the past half-century: the only certainty in science is that many things will always remain uncertain. But it’s all too easy to forget that fact: that’s where the problem starts.

It’s also all too easy to forget that ‘the scientific method’ depends entirely on its base-assumptions: it cannot be relied upon outside of their remit. For our purposes, the most important of these assumptions include:

• causality – all events are connected via cause/effect chains in a linear ‘arrow of time’
• repeatability – given the same conditions, all experiments and results must be repeatable by others
• falsifiability – every hypothesis must be framed in such a way as to enable its negation by experiment
• consistency – the results and hypotheses in each domain of science cannot contradict those of other domains of science

Within those constraints, science works extremely well – and likewise, usually, any technology based on that science. But it’s essential to realise that it only works within those constraints – and there are plenty of conditions where those assumptions break down. Repeatability and falsifiability will seem to make sense whilst we’re dealing with the mid-range of scales, but in fact they break down as we move more towards  the very small – down into quantum levels, as per Heisenberg’s Principle - or to the very large – where experimentation and repeatability are often inherently impossible (at least on the kind of time-scales that we live in!). The same applies as we move more towards unique events: chaos-mathematics makes the level of unpredictability more predictable, but does not reduce the unpredictability itself. Consistency also frequently breaks down between domains: last I heard, for example, the most likely theory of star-formation requires a universe much older than ‘permitted’ by the most likely theory of cosmology. And out at the fringes of science – particularly in nuclear physics – there are plenty of examples where any linear concept of causality will break down, and at times looks remarkably like traditional magic. For example, the old magical notions of ‘action at a distance‘, teleportation and telepathy are all ‘permissible’ in current quantum-entanglement physics, and in some cases have even been demonstrated in laboratory-experiment – even if only at quantum scales.

And there are plenty of real-world, everyday examples of where those assumptions will break down – especially in KM and the like, where we’re often dealing with contexts which, by definition, are either unique or near-unique. So complaining that KM might be considered by some to be a ‘pseudo-science’ is to miss the point, because there’s no way that it can be a ‘science’ in those formal terms above. Instead, to make sense of what’s going on, we may well need to turn to other approaches: science is one approach that we might use, but it’s not the only one.

Which, by a round-about route, brings us back to where we started, with Dave Snowden and the Cynefin framework. Starting from the unknown – what Dave describes as the domain of ‘Disorder’ – we have four distinct methods to ‘make sense’ of what’s going on:

• the Simple domain: apply rules to ‘categorise – sense – respond’
• the Complicated domain: apply algorithms and logic to ‘sense – analyse – respond’
• the Complex domain: apply guidelines and heuristics to ‘probe – sense – respond’
• the Chaotic domain: force change through action, to ‘act – sense – respond’

I know Dave can get ‘curmudgeonly‘ when we place these Cynefin domains in a simple two-axis frame, but in this case there’s one frame that aligns extremely well, and does add quite a lot to our understanding of Cynefin itself. These two axes are value versus truth, and inner (personal) versus outer (collective), which gives us four domains: inner truth, outer truth, outer value, inner value. These domains map almost exactly to those four main Cynefin domains and their sense-making tactics:

• ‘inner truth’: Simple domain – rules or supposed ‘universal truths’ that purport to apply to everyone, everything, everywhere
• ‘outer truth’: Complicated domain – algorithms and the like, often with multiple factors and complicated interactions and delays, but always amenable to causal analysis
• ‘outer value’: Complex domain –  use ‘seeds’ and experiments to probe into the context, to allow meaning to emerge
• ‘inner value’: Chaotic domain – any meaning that may be derived is context-dependent and probably personal only

(The chapter ‘Can’t we explain this scientifically?‘ in my 1990 book “Inventing Reality” likens each of these modes with a means to survive within a swamp: run too fast to sink; climb up a pole; weave a platform between a group of poles; or spread your weight on swamp-shoes. The advantages and disadvantages of each mode are summarised in some detail there: might be worthwhile to read that chapter now and then come back here.)

In practice we would – or should – usually switch between each of these modes, much as Beveridge implies in The Art of Scientific Investigation. But the key point here is that a ‘scientific’ approach – which depends on causality and logic – can only make sense in the two ‘truth’ domains. Trying to use ‘truth’ tactics in the ‘value’ domains is not a good move: we risk ending up with what Dave Snowden calls ‘pattern entrainment’, such that in effect we use a quasi-religious belief as a substitute for true science or sense – which is not a good idea. (For more on this, see, for example, Amory Lovins’ video on “How the practice and instruction of engineering must change“.). Which means that we need to use entirely different approaches in the two ‘value’ domains. We could use terms such as ‘non-rational’, ‘arational’ or ‘meta-rational’ for this, but we might as well use the term that already exists for this: magical.

Magical-thinking isn’t a mistake: it’s what we need to use in the two ‘value’-domains – or, in Cynefin terms, the Chaotic domain and, especially, the Complex domain.

This post has rambled long enough already, so I’d better not go into too much detail. But one of the key tactics here is to deliberately use beliefs as tools, especially in the Complex domain, using them as if they are true whilst still recognising that they may not necessarily be ‘true’ in absolute sense. In classic scientific terms, another name for this tactic is hypothesis, as contrasted with idea (Chaotic domain), theory (Complicated domain) and law (Simple domain). It’s what we do in most technology-development: for example, we might use ideas from science, but we might also use analogy, metaphor, serendipity or even images from a tarot-deck – what works is whatever happens to work. And the fundamental question here is not science’s ‘How does it work?’, but ‘How can it be worked?’ – not how do we make it more ‘true’, but how do we make it more effective, more efficient, reliable, elegant, appropriate, integrated.

(Incidentally, this is one of several reasons why using the term ‘applied science’ as a synonym for ‘technology’ is misleading and even dangerous, because we end up applying the wrong criteria to measure that technology’s value – assuming ‘technology’ in the original sense of ‘tekne‘, a body of knowledge and related practices rather the rather incomplete sense as ‘machines and stuff’. Another concern is that by purporting to be ‘science’, a usage of technology can also attempt to claim science’s status as ‘value-free’ – and hence supposedly not subject to the ethical and other value-constraints that, by definition, are actually the core of every technology. And magic too, for that matter . In this sense, technology and science are fundamentally different from each other, whereas technology and magic are fundamentally the same. In fact the only real difference between the latter is that magicians tend to be a bit more ‘way out’ in their choice of beliefs, especially when the technology is more about mind than matter.)

Whichever mode we use at any given time, the key to all of this is discipline. (This applies in magic as much as in any other technology: as the pseudonymous author of the influential SSOTBME put it, “all those boring meditation books are just the magical equivalent of a school chemistry primer”. But that’s another story… ) Which, finally brings us to why I wrote this post in the first place, because we need a disciplined approach not only to the use of each domain, but also to how not work work within each domain, and how instead to switch between the domains in an effective, intentional manner.

Most readers of this blog would know me as a specialist in whole-of-enterprise architecture. But my real interest, and real work, is in methodology and meta-methodology – the design of methodologies to suit each specific context and need. Behind that, what really concerns me is the process of developing skills as true skills capable of dealing with the complexities and chaos of the real world – rather than as glorified ‘trainings’ that are only usable in the safe, easy purported-predictability of the ‘truth’ domains. I’ve been engaged in this work for well over forty years: for example, one of the tools I developed that you may have seen is the Skills Labyrinth, a live-metaphor for the skills-learning process.

But one of my primary test-cases for this – mainly because it’s almost the closest I can find to a ‘pure’ interpretive-skill, with very little manual-skill and technical-knowledge required to get started – is what’s known in Britain as dowsing, the generic for ‘water-divining’ and the like. (Each country has their own term for this: Americans would know this as ‘water-witching’, for example, whilst Dutch might call it ‘wichelen’.) It’s a classic ‘magical’ skill, sufering – as so many do – from an overdose of idiots, and much-derided by self-styled ‘skeptics’ who rely only on ‘scientific’ theory rather than technological practice and hence don’t have any real grasp of what they so obsessively dismiss. (As it happens, we know a great deal about the physics, physiology and psychology of the skill: one key point we now know for certain is that there is no single mechanism involved, but rather a complex ‘weighted-sum’ merge of multiple mechanisms. Hence most of the classic means of scientific enquiry – “how does it work?” – make little sense, whereas technological enquiry – “how can it be worked – does indeed work well here.)

Worldwide, I’m actually better known as a writer on dowsing and related subjects than on IT or enterprise-architecture: my first book on this – nowadays known as The Diviner’s Handbook – was first published in 1976, translated into some dozen languages, has been in print continuously ever since, and is regarded as one of the standard reference-works on the subject (or learning-guide, rather, because that’s its real purpose). And I apply exactly the same rigour to my work in that field as I do to anything else: I insist on keeping myself, and others, strictly to the correct discipline in the appropriate domain. Which at times is not ‘scientific’, of course – but so what? If the ‘scientific’ mode is not appropriate in that part of the technology, don’t use it! Which is exactly the same principle as we need to apply in KM, or enterprise-architecture, or anything else that is inherently complex and in any way inherently unique, and hence where the usual constraints of ‘rational repeatability’ and the like do not and cannot always apply.

Hence yet another book of mine, co-authored with the archaeographer Liz Poraj-Wilczynska, and published late last year, called Disciplines of Dowsing. (You can download the e-book version for free from the website, though please consider buying the print version if you’re going to use it in practice!) Parts of this work have also been published in the Berg peer-reviewed academic journal on archaeology, Time & Mind. In it we explore the application to dowsing practice of the same four approaches to sense-making and action, linked to Cynefin as above, and cross-linked to standard quality-improvement tactics such as kaizen, the Deming/Shewhart PDCA cycle, ISO-9000:2000 and reflective methods such as After Action Review. It’s the same principles, applied in a slightly different area to what most KMs and EAs might know, but otherwise no different at all. What is different – and which we haven’t seen anywhere else – is an explicit emphasis on how and when and why to switch between each of the disciplines. Which, in turn, we could – and, I would argue, we should – apply in turn to our other everyday work-domains such as KM and EA and the like.

There’s also a strong emphasis in the book on how to identify and avoid some all-too-common pitfalls, the ‘seven sins of dubious discipline’ such as the Hype Hubris, the Newage Nuisance and the Meaning Mistake. (‘Newage’ is perhaps a more accurate term for much of what purports to be ‘new age’: it rhymes with ‘sewage’, ‘the discarded remnant of what was once nutritious’… yup, I can be a cynic too! ). But the point here is that, again, there are exact equivalent of the ‘seven sins’ in every other kind of skill, including those in the sciences: for example, Roderique-Davies’ paper on NLP includes several all-too-obvious examples of the Meaning Mistake. If we don’t understand the limitations of science, and worry too much about seeming ‘unscientific’ or ‘pseudoscience’, we’re likely to end up damaging the quality of our skill and our results rather than improving it. In that specific sense at least, magic is real – and as Cynefin shows us, it matters just as much as science and the like to the quality and validity of our practice.

In addition to the e-book of Disciplines of Dowsing, there’s also a two-page reference-sheet that summarises the four sets of disciplines, and that’s perhaps more immediately usable in practice. (The material on the ‘seven sins’ is only in the book, though.) It’s written for dowsers, of course, but it doesn’t take much translation to apply it direct to KM, EA, software development or any other complex-domain skill. Download it, perhaps, and let me know how it works for you? And thence it might be worthwhile writing another version specifically for KM or whatever. Something different to play with, anyway.