Natural science as counter-factual

July 11, 2021

active-sensemakingI originally intended to publish this post way back in May and got all the notes together but a mixture of work pressure and ill health (a bad foot infection now resolved) have hit the backlog.  If you have to spend two weeks with your foot elevated and iced taking strong anti-biotics its too easy to neglect things but they never go away!.  When that follows a pivot in your own company’s strategy with new staff to take on it gets even worse in terms of time to reflect and write.  But I’ve been making notes in this draft post on Mars-Edit over the last two months and more and it’s now time to publish.

I’m going to start by referencing the interaction with Mike Jackson that followed his critical review of the EU Field Guide.  But I will then move on (stimulated by Mike’s response) to further elaborate and define the naturalising element of sense-making which I regard as key to working at scale on the complex issues and problems faced by organisations and society.  A part of my criticism of Mike’s Critical Systems Thinking (CST) is that there are significant issues associated with scale and emergence.  I’ll address some of those here but will also pick up in subsequent posts.  Readers not interested in the response to Mike can skip to the section title below which has been written as a stand-alone piece.

I responded to Mike’s criticism in a post titled Naturalising narrated at the end of March.  One of the key points I made was that Mike was wrong to suggest that I was limiting naturalising sense-making to the natural sciences.  Given that I often quote Heidegger and Deleuze and recently introduced the term aporia from Derrida into Cynefin this seemed a little odd, to say the least.  But I used the occasion to elaborate the idea of using the natural sciences as an enabling constraint.  By understanding the nature of human decision-making, the nature of systems etc. we can focus on working with what we know rather than trying to overcome the consequences of many centuries of evolution in a few workshops and papers.  Mike responded on the 8th of April and his response has been a considerable help to my thinking. He says “while Dave does indeed make extensive use of social science [My statement that “Dave Snowden needs social theory to really get to grips with social complexity” was too loose.]” which is a delight and very, very, British in its use of  ‘too loose‘.  So I will take that admission as progress and move on.

I’ll do that as a series of bullet points to make it easier to follow.

  1. Mike states that in my view “social science, to be valuable, must follow the model of the natural sciences” and while I can see where he is coming from that is absolutely not the case. In fact I don’t think much of it can follow that model and when it attempts to gets things badly wrong (see the end of this post).   Social science and the humanities, in general, provide us with profound insights and explanatory sense-making frames and language.  My background is in physics and philosophy and no one who has studied philosophy in any depth would never assume that all sense-making (active-sensemaking) has to be confined to a domain where experiments can be repeated and tested by third parties.  It simply isn’t possible but it does need us to exercise caution and reflect that in the nature of truth claims that we make.
  2. I think part of the problem here is that talking about enabling constraints may not be the best way of getting the point over.  And that is not just Mike, I’m seeing that with a lot of people I respect in the various approaches to Systems Thinking that are around; and also a lot of people for whom I have little respect.  So I’m going to try again with different language, drawing on Constructor Theory in Physics in the section below.
  3. Mike and I are agreed on a concern about some approaches in complexity science that see to treat all human systems in the same way as we can model or simulate the behaviour of ants, birds, and crystals.  We also share a concern about the attempt in some social science to try and create recipes based on limited or restricted research.  I’ll pick up on that later as well.
  4. I don’t think we need to take Boulding’s nine-level hierarchy to get insight here.  It was a valuable paper and I think I read it a few years after university on the recommendation of Frank Land when I was engaged with the LSE systems group and others.  But I have a general antipathy to hierarchies in sense-making that I need to keep in check!  I fully agree with Mike that there are things we cannot fully know or understand from a natural science perspective, and I tend to fall on the side of those who think we never will – the exploration of the ideas around the numinous for example.  I’m happy to support Mike’s conclusion there but would want to qualify some of the assumptions he uses in the build.
  5. We have a substantial difference on mapping, strange attractors, into human systems, or maybe we don’t.  Again it may be using language and ideas in different ways from very different backgrounds.   Mike states “This is my issue, for example, with ‘strange attractors. The concept has a precise definition in the natural sciences which does not do justice to the complexity of human systems where, as I previously argued, it is shared appreciations, values, and intentions that actually lead people to act in concert”.  I don’t disagree with the complexity of human systems but I do disagree on what we can do to understand them.  I have argued that tropes in narrative theory and assemblages (DeLanda’s interpretation of Deleuze) are a type of strange attractor.  Our own work on distributed ethnography actively seeks to capture “appreciations, values and intentions” at scale, and is based on principles of epistemic justice and cognitive sovereignty.  Given that is a quant approach we can use a form of fitness landscape to represent strange attractors in human systems.  Yes, they are not complete and subject to error, but quantitative mapping at scale is a lot more sustainable than talking about something in a workshop.  It is also more likely to lead to action.
  6. Mike thinks that ‘complexity theory has a promiscuous relationship with social science’ which I find odd, but I canequally  argue that Mike’s advocacy of CST seems to have a deeply promiscuous relationship with any and all theories of uncertainty.   Mike is an advocate for CST and his criticism of the EU Field Guide includes strong advocacy for his own approach.  He’s done the same with the recent book by John Kay and Mervin King which has a lot of resonance with CAS and is something I will write a blog post on in the not too distant future.  I’ve never challenged the fact that Systems Thinking in its many forms seeks to deal with complexity but just as we were very effective in using gravity before Newton, once we had the science things can change and to my mind, that is where we are.  So we are probably all going to be very promiscuous while we sort all of this stuff out.
  7. I will repeat what I have said before that there is a whole body of methods and approaches in CST which are interesting.  Mike makes a slightly Borg-like statement to the effect that “I do think Dave would benefit from engaging with the full range of social theory brought to the table in CST. CST is pluralistic and is not restrictive in terms of the social theories it engages with. It has a place for the naturalising approach, that Dave regards as essential, and much else besides”.  I have no doubt that I will, and I have Mike’s book and plan some posts based on my reading of it and I am trying to resist the temptation to start by pointing out the errors in his description of Cynefin!  But I don’t think that my rejection of some of the underlying theory represents a lack of engagement.  There is a danger here, and for both of us, of falling back to the You don’t really understand argument and I’m doing my imperfect best to avoid that.  I’m very keen to get methods and tools from CST into the pluralist approach we are developing for use in organisations; designed on the principles of coherent heterogeneity, the ability to work with differences that make a difference.

But at the heart of all of this is in point one above, namely Mike’s false assumption that I am arguing social science must follow the model of natural science.  So I need to try again here and that is the purpose of the next section.

Using natural science as a counterfactual

This is going to be a relatively short piece, in part because I am writing a significant paper with John Turner of North Texas University and Nigel Thurlow of the Flow Consortium on the subject.  So take it as a flag for something more substantial in a peer-reviewed journal later this year or early next and I am been necessarily cautious.  Complexity and sense-making (as well as active sensemaking) have been increasingly popular of late and there is a lot of very ‘shallow’ work going on that fails to respect the depth of the field and critically doesn’t acknowledge its sources.  As complexity starts to grow rapidly as a field of practical application I am having to be a lot less open with early thinking than I used to be.

I am using counter-factual here in the sense of Constructor Theory which was first published in 2012.  I first came across this back in 2013 when I picked up on Deutsch’s theory having bought a copy of Scientific American to read on a plane.  So when his collaborator Chiara Marletto ran a session at the Hay Festival a few years later I attended it and took copious notes.  I’ve been playing around with the ideas for some time since and of late I’m starting to see links to my earlier work on knowledge management back in the late 1990s and current work in naturalising sense-making.  I realised in the exchange with Mike that using this might give an easier explanation of the way I see natural and social science.  So here goes.

The essence of constructor theory is the break the pattern of a reductionist approach to creating predictability.  Instead, it starts from identifying what is the counterfactual landscape in order to identify what is not possible.  It then seeks to identify constructors that will produce replicable outcomes.  At the simplest level that can be a machine, but at a more complex level, it could well be culture.  There are really strong links to epigenetic and material engagement theory here and the fact that we now know the mechanism by which culture can be inherited, something Darwin always said was the case but he didn’t understand the mechanism. Odling-Smee and others have argued that humans evolved culture in part to create exaptive evolution (his work was on lactose tolerance).

Constructor theory also focuses on transformations rather than components which fits well with most of us in complexity who argue that interactions are more important than the entity which interacts.  This switch is critical as it means that while we need a sense of direction, goals aren’t possible and neither are statements about what should be the case; a type of implicit goal all too common to methods that depend highly on facilitated workshops.

Back in 1998 and 1999, in two CBI Handbooks on the field of Knowledge Management which I edited, I challenged two then-dominant models in the knowledge management space.  One was Nonaka’s SECI (later BA) and that challenge eventually created Cynefin.  The second was to the DIKW pyramid.  My distinctive dislike of hierarchical models of meaning coming to a fore here.   Instead, I argued that Knowledge was the means by which we created information from data.  If we had sufficient shared knowledge then we would create consistent information.  The example I used was that of two accountants who have the same knowledge and will therefore interpret data in the same way so we can highly abstract forms of information.  The same is true of any field where specialist language and knowledge of associated theories allow for more rapid and energy-efficient communication.  That also fed into Cynefin but in effect, to use the language of Deutsch and Marletto some 14 years later I was seeing knowledge as a constructor.

I need to do a bit of research to find when I first started to talk about the role of natural science in sense-making but it was well before 2008. It certainly does back to the Genoa II program in DARPA which launched in 2002 and in which I was one of the lead contractors looking at human terrain mapping and weak signal detection.   I was fascinated by the inability of people to see a gorilla in plain sight if they were not expecting it.  That was when I started to argue that we needed to use natural science as a constraint on what is possible.  If we do that then we save considerable energy trying to train people to do things that humans did not evolve to do and for good reason and start to work.  Deacon’s work in his book Symbolic Species was also important here as it challenged the whole way much of social science and computational theory thought about language.

So, and maybe this will help a wider understanding, rather than talk about natural science as an enabling constraint we can instead suggest that natural science can be used to create the counterfactuals.  A theory in social science for example, such as assemblage theory, thus acts as a constructor which allows for the emergence of common meaning.  it is not necessarily empirically derived and may emerge from abstractions, which include semiotics and aesthetics.  That is one of the key aspects of human evolution, the ability to use abstractions for meaning making and radical re-purposing or exaptation.  But creating constructions informed by, and constrained by the counterfactuals gives us a new way to reduce energy, but also to provide authentication and validation.

Of course, it is not just natural science that provides counterfactuals, it can be politics, history (including assemblages and identity) but the ones from natural science cannot be challenged, whereas others may usefully be in specific contexts.  So we can now ask some interesting questions (trying to make it simpler here)

  1. What in the current situation we are working with are the existing counterfactuals?
  2. For those which are not based on natural science, but appear to be emergent properties of multiple interactions over time, which can we challenge?
  3. Of those, we can challenge where is there utility in making that challenge?  What is the energy cost of doing so?
  4. Within the counterfactual universe what constructors already exist (a process is one, education another, and so on)?
  5. Of those what level of abstraction and codification exists which will (per Boisot) allow for diffusion at scale?
  6. What new constructors can we create?  Here the parallel safe-to-fail probes of Cynefin can come into play.
  7. What has the lowest energy cost within the landscape of constructors and counterfactuals?  That is the most likely to happen.

Now I know all of this is a bit abstract, and I am still experimenting with language, metaphor and illustrations so expect a lot more on this.  I promise not to get too excited about the ideas coming out of physics that information may have mass, which I find fascinating and elegant and which would extend our thinking here.  So don’t complain if you don’t understand it – I am sharing early and I am not there yet myself.  But if this works it is revolutionary and gives more substance to the whole naturalising sense-making field.

Within this way of thinking,  Affordances are probably counterfactuals, Assemblages, and Agency a type of constructor that can be challenged or modified.  More work to do here but critically all of this is another phase shift that makes a lot of the modernist/postmodernist and social constructivist/critical realist conflicts irrelevant.  It also breaks the crude Scientism of people like Dawkins and there, as previously stated, I agree with Mike that social science cannot be constrained to that model of scientific understanding.

That said the whole field of management science which seeks to imitate the natural sciences is subject to physics envy and the confusion of correlation with causation as well as inadequate sample selection.  Recent challenges to books such as Good to Great echo some of my criticism of that work (there are better explanations if you take an apex predator perspective on the companies he selected) as well as Lean Start Up (biased sampling) and Reinventing the Organisation (no pretense at research, cherry-picking material to support an ideology).

As I say a lot more to do.  But in this post I am flagging up an area of concept and practice development, as well as trying to find another means to take the dialogue forward with Mike Jackson.


Banner picture of the Arid forest Research Institute in Jodhpur is cropped from an original photo by Pawan Parihar.  The early 20th Century model of the brain is by David Matos both on Unsplash

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