Weblink Items (16)
▶ The Cynefin Framework - YouTube (8:38)
Uploaded on Jul 11, 2010
The Cynefin Framework is central to Cognitive Edge methods and tools. It allows executives to see things from new viewpoints, assimilate complex concepts, and address real-world problems and opportunities. Using the Cynefin framework can help executives sense which context they are in so that they can not only make better decisions but also avoid the problems that arise when their preferred management style causes them to make mistakes.
Cynefin, pronounced kuh-nev-in, is a Welsh word that signifies the multiple factors in our environment and our experience that influence us in ways we can never understand.
In this video, Dave Snowden introduces the Cynefin Framework with a brief explanation of its origin and evolution and a detailed discussion of its architecture and function.
The Cynefin Framework
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A Leader’s Framework for Decision Making
11/29/2017
The framework sorts the issues facing leaders into five contexts defined by the nature of the relationship between cause and effect. Four of these—simple, complicated, complex, and chaotic—require leaders to diagnose situations and to act in contextually appropriate ways. The fifth—disorder—applies when it is unclear which of the other four contexts is predominant.
Using the Cynefin framework can help executives sense which context they are in so that they can not only make better decisions but also avoid the problems that arise when their preferred management style causes them to make mistakes. In this article, we focus on the first four contexts, offering examples and suggestions about how to lead and make appropriate decisions in each of them. Since the complex domain is much more prevalent in the business world than most leaders realize—and requires different, often counterintuitive, responses—we concentrate particularly on that context. Leaders who understand that the world is often irrational and unpredictable will find the Cynefin framework particularly useful.
https://hbr.org/2007/11/a-leaders-framework-for-decision-making
▶ Keynote Speaker: Combining Complexity Theory with Narrative Research - YouTube (1:05:29)
Published on Oct 22, 2012
David Snowden, PhD; Founder and CSO, Cognitive Edge Pte Ltd.
▶ XP2012 - Dave Snowden Keynote Part 1 of 2 - YouTube (38:42)
Published on May 30, 2012
Dave Snowden talks about complexity thinking and contrasts it with design thinking and systems thinking in his XP 2012 keynote.
▶ XP2012 - Dave Snowden Keynote Part 2 of 2 - YouTube (39:56)
Published on Jun 1, 2012
Dave Snowden talks about complexity thinking and contrasts it with design thinking and systems thinking in his XP 2012 keynote.
▶ The Art of Hosting: Exploring a Framework for Working with Complexity - YouTube ( (55:22))
Published on May 1, 2013
This TeleSeminar will explore the Cynefin framework, a model used to help organizations and communities make good decisions and create useful actions especially when working with complexity. We will cover some theory of complex decisions and explore various forms of leadership needed in different contexts. This will lead us into a discussion of the role of participatory leadership and dialogue based leadership in addressing complex challenges.
Chris Corrigan is an Art of Hosting practitioner and has more than 20 years experience working with participatory methodologies in indigenous communities, Aboriginal organizations, non-profits and with government and business clients. He has worked with transition town organizers in Canada and Ireland and has supported the use of Open Space Technology with several transition initiatives in North America. He lives on Bowen Island, British Columbia. You can read more about Chris' work, and download tools and resources for your own work at his website www.chriscorrigan.com
Understanding complexity - The Cynefin framework
A brief overview of the Cynefin framework, with discussion on complexity, and why it is important to understand how organisations work in order to implement change.
Cognitive Edge Network
Cognitive Edge was founded in 2005 with the objective of building methods, tools and capability to utilise insights from Complex Adaptive Systems theory and other scientific disciplines in social systems. This approach, known variously as Naturalising Sensemaking and Cognitive Complexity has developed three main focus areas:
- The development of SenseMaker® a software ecology which integrates decision support, research, monitoring and knowledge management.
- A comprehensive global education programme has created a broad network of practitioners able to use the methods and tools developed by Cognitive Edge.
- The research agenda of Cognitive Edge remains at the heart of its methods and tools programme.
SenseMaker®
SenseMaker® lies at the heart of a range of applications. It has been extensively used in research, providing an quantitative approach to what has traditionally been a qualitative domain. Its origins lie in weak signal detection and understanding the impact of culture on decision making, work that continues to this day. Employee satisfaction, citizen journaling, attitudinal auditing (such as ethics and safety) have all been created using SenseMaker® as the core.
Increasingly SenseMaker® provides decision support capability permitting whole of workforce engagement and the creation of human sensor networks (wisdom of crowds in the popular literature) to enable a whole new approach to evidence based policy under conditions of uncertainty as well as real time decision support. This combines with knowledge management capability ranging from field operations to issues around the ageing workforce.
Cynefin dynamics - Cognitive Edge
7/2/2015
In yesterday’s post I looked at my decision to use constraints in the definition of the main Cynefin domains. I talked about those in the content of explains dynamics, the shifts between domains which are as if not more important than the domains. In Complex Acts of Knowing I used various dynamics to handle the instantiation of an informal community as official in the context of need and as a means of understanding when to use (the then popular) Communities of Practice or Intranets. Later in New Dynamics of Strategy Cynthia and I indulged ourselves over several conversations creating multiple dynamics. Later I focused back to the original three KM dynamics (albeit in a wider context) and more recently I have started to use what I call the Grazing Dynamic; a representation of near continuous change with little stabilisation.
Typology or Taxonomy?
10/22/2011
There is no clear agreement on the definitions of typology and taxidermy, well there are in biology but as we move into other fields it gets harder. I’d also make it clear that I am pushing the boundaries of my knowledge here so if Patrick Lambe (or someone else who does understand this) wants to correct me on my use that is fine. To be honest I am less concerned about the labels than I am about the distinctions. Knowledge management is closer to policy formation than it is to biology so I am going to use the definitions in Kevin Smith’s September 2002 paper in the Policy Studies Journal Typologies, taxonomies, and the benefits of policy classification as my starting point.
In a typology the dimensions represent concepts, they do not necessarily exist in physical reality (although they can). As such typologies generate heuristics which are more adaptive under changing circumstances. On the downside the concepts can be arbitrary may not be exhaustive and can easily be subject to clashes of interpretation.
A taxonomy on the other hand classifies things based on clear empirical characteristics and will have rules that allow determination of location. They have clear boundaries mostly determined by cluster analysis allow rapid decision making . On the downside, once a taxonomy is established if something does not fit, it will be made to fit as the taxonomy itself creates a filtering mechanism through which we filter observable characteristics.
A Cynefin postscript
Now Cynefin is a bit of a hybrid. It is a conceptual framework so at that level its a typology, but the dimensions are based on natural science so there is an empirical aspect but its not from any form of cluster analysis.
I’ve always said that the approach I and colleagues have developed over the years is a form of naturalising sense-making; the naturalising is a philosophical reference to rooting theory in the natural sciences. Now this is useful but it can lead to some confusions if people seize on it and make it a two by two matrix.
It can, and is used as such but properly used it is a lot more. Some quick points here and I plan some more posts on aspects of this:
Cynefin has five domains; simple, complicated, complex, chaotic and disordered.
The disordered domain recognizes the essential inauthenticity to any typology of human experience so disorder is the state of not knowing which of the domains you are in (and you may be in several). Disorder is frequently left out which is a pity.
The chaos domain is always a transitionary state and Cynefin is as much about movements between domains (dynamics) as it is about the domains themselves. Now anyone with the right science background gets that quickly, order comes for free to quote John Holland (I think but it might be one of the other Santa Fe founders and I have limited internet access so can’t check that).
Its very important to use the constrain based definition of the domain as that allows you to understand how to move between them.
- If there are no constrains its chaotic. If the constraints are severe enough to make agent behaviour predictable then its ordered,
- if the nature of those constrains are self-evident then its Simple,
- if they require analysis then they are Complicated.
- If the constrains and agents co-evolve then its Complex.
That means that changing the constraints can change the nature of the system and consequently the nature of situational assessment and decision making.
Simple is next to Chaotic as complacency, or inappropriate application of constrains which can lead to catastrophic failure (again movement)
Ideally the framework is social constructed (the boundaries emerge from the data), ideally with the four points method I described in my paper on the history of Cynefin.
However it can also be used as a categorisation framework (boundaries precede the data) and the HBR article largely focuses on this aspect as it was an approach more familiar to the readers.
Bounded diversity: A typology of foresight
10/21/2011
The dimensions of the typology (which is very different from a taxonomy) were:
Distributed cognition (the term goes back to Hutchins 1995 book Cognition in the Wild) is a key aspect of any complex adaptive system and is to my mind a much more accurate term that Wisdom of the Crowds and less prone to practice error. Language remains the master of man to quote Heidegger and what language we use is very important to practice.
For the second dimension I took the probable-possible-plausible dimension which I had used on day one and which is key to the idea of resilience. If you are not familiar then I posted the material as The mantra of resilience in March of this year and there is also a recorded presentation. There is a further elaboration in the form of a table in my post Meaning and the content heresy.
Cynefin: a suggestive framework for problem solving
7/22/2014
####Obvious
- Obvious is the domain of best practices. (Note: This category was referred to as “Simple” in prior versions of the model.)
- Characteristics: Problems are well understood and solutions are evident to anyone with a reasonable amount of common sense. One example is server patching – this is a well-documented procedure that can be scripted and regulated. Minimal expertise is required.
- Approach: Problems here are well known. The correct approach is to sense the situation, categorize it into a known bucket, and apply a well-known, potentially templated, solution.
Complicated
- Complicated is the domain of good practices.
- Characteristics: It’s categorized by a list of known unknowns – in other words, you likely know the questions you need to answer and how to obtain the answers. Expert knowledge is required to assess the situation and determine the appropriate course of action. You could reasonably spend enough time in analysis to identify known risk and devise a relatively accurate plan.For example, the development of a new CMS system would be a complicated problem. Competitors exist, the market is well understood and there is significant precedent. Expertise is required, but the work is evolutionary, not revolutionary.
- Approach: Sense the problem and analyze. Apply expert knowledge to assess the situation and determine a course of action. Execute the plan.
Complex
- Complex is the domain of emergent solutions.
- Characteristics: It is categorized by unknown unknowns – you don’t even know the right questions to ask. Experimentation is required to even understand the problem, let alone begin to solve it. The final solution is only apparent once discovered. No matter how much time you spend in analysis, it’s not possible to identify the risks or accurately predict the solution or effort required to solve the problem.For instance, the emergence of Twitter® was a complex situation in 2007. When developed, Twitter defined a new market. And it wasn’t possible to predict what features would stick and which would miss the mark. Expertise was needed, but it required experimentation to discover the nature of the product.
- Approach: Develop and experiment to gather more knowledge. Execute and evaluate. As you gather more knowledge, determine your next steps. Repeat as necessary, with the goal of moving your problem into the complicated domain.
Chaotic
- Chaotic is the domain of novel solutions.
- Characteristics: As the name implies, this is where things get a bit crazy. The immediate priority is containment. Production defects could be an example of a chaotic situation. Your initial focus is to correct the problem and contain the issue. Your initial solution might not be the best, but as long as it works, it’s good enough. Once you’ve stopped the bleeding, you can take a breath and determine a real solution.
- Approach: Triage and stop the bleeding. When you’ve gotten a measure of control, assess the situation and determine next steps. Take action to remediate or move your problem to another domain.
Disorder
- Disorder is the space in the middle.
- Characteristics: If you don’t know where you are, then you are in disorder. Priority One is to move to a known domain in one of the four corners.
- Approach: Gather more info on what you know or identify what you don’t know. Get enough info to move to a more defined domain.
It’s worth noting that Cynefin emphasizes the cliff between Obvious and Chaotic. There is an ever-present danger that even well-controlled systems might slip into chaos due to apathy, negligence, or any number of other reasons. It’s important to remain vigilant to keep things in control.
So what use is the Cynefin model? What keeps it from being just another in a long line of “next big things?” I’ve found two general cases where it’s proven its worth:
https://www.godaddy.com/garage/cynefin-suggestive-framework-problem-solving/
A return to constraints - Cognitive Edge
12/10/2016
Now all of that has worked to get people talking about the issue, but the next stage is to make it more tangible as a method, or series of methods that can be put into operation and scaled. Given that constraints are things that can be mapped, and where change is possible, a typology as much as a taxonomy. Now the way I work is the explore an idea through conversations and reading, then I start to play with the ideas in front of an audience, post here then repeat several times until I can teach it. So this is all still a work in progress. However I have been modifying my September thoughts somewhat and I’ve increasingly made a distinction between robust and resilient types of constraint. Then helps with the order-complex distinction in Cynefin and also (I think) helps people get the idea. More on what we do with these over the next week with some interruptions on other themes. Oh and I am keeping with the idea of Containment and Coupling as a master distinction. Each of the constraint types below apply to both.
The knotty issue of constraints - Cognitive Edge
8/27/2017
The typology of constraints I posted last December has survived the test of multiple presentations and discussions and has the advantage of combining the distinction between resilience and robustness which is critical to any understanding of strategy. I’m assuming familiarity with that earlier post so if you haven’t read it use the link. A search of this site on constraints will also give you over a hundred other posts on the subject, but they need to read in context of the dates they were written. There are a lot of posts on resilience and robustness as well but to save time I’ll quickly summarise how I use them:
A robust system is one that survives as is, or with only minor modifications (Shoring it up until Christmas might resonate with older British readers). It can be known, defined and provides a clear boundary state or type of linkage which is explicit in nature.
A resilient system is one that survives with continuity of identity over time, but it survives by changing and that change may not be explicit or easily understood. Taleb’s anti-fragility fits here and I don’t buy his argument for difference. Self-healing systems, those that become more resilient under stress have been known for a long time.
Freedom through constraints - Cognitive Edge
9/29/2017
So over multiple doodling on planes and, most recently in the various waiting rooms of the Great Western Hospital in Swindon, I started to create a taxonomy of constraints and containers that will become a part of Cynefin over the next few months. At its most basic I am introducing the idea that in any system there are containing constraints and coupling constrains, developing an earlier metaphor of exoskeletons and endoskeletons. The first defines the boundaries around something, the second defines the interactions between entities that form a part of the system.
So what follows is an initial taxonomy of the nature of both containers and coupling types. For a period they were different with some overlap, but then I realised they were the same. I’ll list them below but in subsequent posts I will work on different combinations of them in terms of different levels of uncertainty and manageability. I’m also starting to play with a set of symbols but I really need a graphic designer to work on these! Obviously one type of container may contain different types of coupling. That is for a future post.
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