Recently, I had the privilege to listen to a talk by Dave Snowden. Dave is the Founder and Chief Scientific Officer of the Cognitive Edge. He is the creator of the Cynefin Framework which is revolutionising current thinking about and understanding of the nature of reality, learning and evolution.

I first listened to Dave ten years ago while he was still working with the IBM Institute of Knowledge Management. (There, he led a programme on complexity and narrative.) He was invited as the main speaker to an event co-organised by the British Countryside Agency (currently part of Natural England) with whom I was working on a Learning Networks dissertation project. Then, he spoke about the nature of communities of practice and effective collaboration supported by the use of information and communication tools and frameworks.

Dave’s talk I had the pleasure to recently listen to was on ”Linear versus Complexity, New Thinking Paradigm for the Development World”. It took place at the International Fund for Agricultural Development.  As Roxy Samii at the International Fund for Agricultural Development (IFAD) said, it was a truly humbling experience! The IFAD blog post and webcast of the lecture is here.

Dave Snowden as Speaker

With Dave being such a good speaker, it is good to note a thing or two about his style. He may come across as slightly (if not more) opinionated, but his points are good, speak truth, and quickly relate to you being so personal. Being a great story teller, he oscillates between deeply personal and fairly abstract. I find it interesting that he has a background in both Philosphy and Physics, which I think makes for an approach that is rigourous yet effectively tackles the abstract, i.e. what we often find so difficult to define. He takes a natural science approach to social science. A well-rehearsed speaker, Dave throws pearls of wisdom at you as he speaks, and mixes these with satire which may taste bitter but being so true is refreshing. Sounds good, right? It is a pleasure listening to and learning from him.

Below are some of Dave’s talk points. These are not meant to be a comprehensive account of what he talked about. For a comprehensive account, please watch the webcast.

Cynefin Framework – Four Different Kinds of System

The Cynefin Framework is a sense making model in which data precedes frameworking (as opposed to categorization models in which frameworking precedes data). The framework is there to help define the system we are having to deal with and therefore define our optimum approaches to it. It is a decision-making framework that has been used for knowledge management, project management, IT Design, strategy making and so forth. Its purpose is to help us assess a situation and then apply a most appropriate approach of addressing and learning from it.

There are four different kinds of system that are there to frame our experience. What is complex and chaotic to one can be merely simple to another. Part to defining the type of system we are dealing with lies with its nature, part with our experience and expertise. These two can be quite hard if not impossible to distinguish though.

  • Simple Systems (cause and effect relationships are simple and predictable). Here, we sense, categorise and respond. Applying best practice (i.e., established  examples of what works in a particular context) works well in simple systems.
  • Complicated Systems (cause and effect relationships exist but they are not self-evident). Here, we sense, analyse and respond. Applying good practice (i.e., a range of examples of what works well in a given context) works well in complicated systems provided we have the right expertise.
  • Complex Systems (cause and effect relationships are only obvious in hindsight, learning by doing). Here, we probe, sense, and respond. Here we apply emergent practice (i.e., new practice, some combination of best practice and good practice, or not, which is different and unique). When the system is complex we apply emergent practice in order to adequately ”work” it.
  • Chaotic Systems (no cause and effect relationships can be determined). Here we act, sense, and respond. In order to effectively understand and function in a chaotic system we must act very quickly to either innovate or stabilise it and therefore learn from it. In complex systems we apply novel practice.

Depending on the ontology that applies to the situation, we should think and analyse accordingly. One size does not fit all!

The Catch behind Disorder

The central space on the above diagram is key. It is Disorder, i.e., the space where we dont know which space or system we are in. The danger is that when we are in Disorder we would interpret the situation according with our preference.

Complacency Zone

Furthermore, Dave points out that whereas the boundaries between:

simple <-> complicated

complicated <-> complex

complex <-> chaotic

are there for transitions, the

simple -> chaotic boundary is a complacency zone.

When we get used to believing that ”simple” paradigms underlie everything then we get to see all problems as a failure of process. In reality, this is often not the case. In other words, simple is highly vulnerable to rapid change, whereas complicated and complex are not. If we have learnt to function in primarily simple, i.e., very bureaucratic environments, we would apply best practice approaches even when the situation calls for good practice, emergent practice, or even novel practice.  And, as Dave says, this is a recipe for disaster.

Pearls of Wisdom

Some pearls of wisdom Dave threw at us as he spoke:

  • Partial fragmented stories of failure create more learning than documented examples of good practice.
  • Failure can have more learning potential than success.
  • Delegation is not distributed decision-making/distributed cognition.
  • Micro-management is a deadly enemy of understanding complexity and complex systems.
  • It is important to not confuse measures with targets, i.e., focus too much on measuring and forget the thing to measure! (British National Health Service) It is important to manage the evolutionary potential of the present rather than measures and targets.
  • Computer Science and Economics graduates are often partially autistic (Asperger’s syndrome). However, this means they will often detect patterns other people will not. This is very valuable and can be highly adaptive to the human species.
  • Computers will always only mimic people’s intelligence. They won’t replace it.
  • We evolve to make decisions based on limited data. We like ”messy coherence”. Deep inside, we perceive order as threatening.
  • Different cultures are defined by patterns we tend to experience in that culture. These patterns define our brain function and get us to go about things in some ways and not others.
  • Tacit knowledge is at the heart of deep expertise. Tacit knowledge can not be made explicit! (Polanyi is right, Nonaka and Takeuchi have not read Polanyi)
  • Explicit knowledge without tacit knowledge makes no sense!
  • Knowledge Management often assumes knowledge can be codified whereas it can not!
  • Communities of Practice are often too structured and therefore we do not need them.  What we do need are more adaptive social computing structures. Peer-to-peer knowledge is better than focusing on achieving targets. Blogs can build communities very fast.
  • Technology is so pervasive these days that Twitter can be more effective than Google.
  • Important to no longer design applications but rather design architectures in which applications can emerge.
  • Architectures for resilience are better off than architectures for effect.
  • Development projects are almost never planned for resilience which is on the other hand much more effective. If they are planned for resilience they currently will find it hard to be funded.
  • Adduction is the ability to make connections among things not normally connected. (I see, so this is how it was called…) It is a source of innovation and has a lot of adaptation potential. It is however often discouraged, why?
  • History of Science goes through three stages: 1. Management Science (simple, all about targets), 2. Systems Dynamics (complicated, Senge’s learning organization framework, learning objectives, imposing ready models on reality) and 3. complex dynamics (complex systems change at every level, not just at system level).

Applications

At his lecture, Dave pointed out the Cynefin Framework has been used to frame challenges experienced by businesses and foundations, such as Bill and Melinda Gates Foundation and Rockefeller Foundation. The point is to enable donors to fund projects and programmes without having a clear idea of the objective. I wonder how it could be used to transform the corporate sector and enable socially responsible and environmentally sustainable businesses around the world.

I love complicated and complex. Even chaotic can be exhilarating!