One of my favourite concepts from the complexity world is the fallacy of thinking that comes from the truth of retrospective coherence. The mistake is that, because we can look back in time to understand causes of our current condition, we can therefore see forward in time and affect the causes of a future condition. Complex systems are emergent, so we can never be sure what the future holds, regardless of how well we can trace how we got here.
Despite the fact that it is illegal to sell an investment instrument without the warning that “past performance does not guarantee future results” falling for the trap that retrospective coherence gives you a reliable path forward is basically a feature of doing any strategic work at all. It leads to planning that puts out a future preferred state and then backcasts a set of steps that, if we follow them, will take us there or nearly there.
So there are all kinds of issues with this, and the Cynefin framework’s greatest gift is that it helps us create strategy to avoid to pitfall of retrospective coherence.
Today though, a surprise in my morning reading. A lovely article on Robert Frost’s “The Road Not Taken.” We all think we know what that poem is about: about the adventure that will ensue if we just take the less beaten path. But you might be surprised to learn that the poem is actually about retrospective coherence and not adventures strategic planning (emphasis mine):
Most readers consider “The Road Not Taken” to be a paean to triumphant self-assertion (“I took the one less traveled by”), but the literal meaning of the poem’s own lines seems completely at odds with this interpretation. The poem’s speaker tells us he “shall be telling,” at some point in the future, of how he took the road less traveled by, yet he has already admitted that the two paths “equally lay / In leaves” and “the passing there / Had worn them really about the same.” So the road he will later call less traveled is actually the road equally traveled. The two roads are interchangeable.According to this reading, then, the speaker will be claiming “ages and ages hence” that his decision made “all the difference” only because this is the kind of claim we make when we want to comfort or blame ourselves by assuming that our current position is the product of our own choices (as opposed to what was chosen for us or allotted to us by chance). The poem isn’t a salute to can-do individualism; it’s a commentary on the self-deception we practice when constructing the story of our own lives. “The Road Not Taken” may be, as the critic Frank Lentricchia memorably put it, “the best example in all of American poetry of a wolf in sheep’s clothing.” But we could go further: It may be the best example in all of American culture of a wolf in sheep’s clothing.
Brilliant.
A small elevator speech I shared on the OSLIST yesterday:
Self organization works by a combination of attractors and boundaries. Attractors are things that draw components of a system towards themselves (gravity wells, a pile of money left on the ground, an invitation). Boundaries (or constraints) are barriers that constrain the elements in a system (an atmosphere, the edges of an island, the number of syllables in a haiku)
Working together, attractors and boundaries define order where otherwise there is chaos. We can be intentional about some of these, but not all of them. Within complex systems, attractors and constraints create the conditions to enable emergence. What emerges isn’t always desirable and is never predictable, but it has the property of being new and different from any of the individual elements within the system.
Self-organization is where we get new, previously unknown things from.
Dave is working on a theory of change, which I think is a good thing. In this latest post he has a nice summation of the way to move to action in complex situations (like cultures):
So where we are looking at culture change (to take an example), we first map the narrative landscape to see what the current dispositional state is. That allows us to look at where we have the potential to change, and where change would be near impossible to achieve. In those problematic cases we look more to stimulating alternative attractors rather that attempting to deal with the problem directly. Our method is the look at the narrative landscape and then ask the questions What can I (we) do tomorrow to create more stories like these and fewer like those? The question engages people in action without analysis and it allows us to take an approach that measures vectors (speed and direction) rather than outcome. The question also allows widespread engagement in small actions in the present, which reduces the unexpected (and potentially negative) consequences of large scale interventions.
In sum, complexity work is about understanding the context to understand where the potential for evolution might lie. From there you try experiements to see what you can learn, and support what works while removing support for what doesn’t
It’s an old saw, but it’s actually a simple thing. And I keep writing about it because it seems TOO simple for most folks. Shouldn’t strategy be more ordered, laid out and thought through than this.
As always the answer depends, but with complex situations the answer is no. Save your discipline and rigour for understanding things as they evolve rather than trying to get it all right from the start.
via Change through small actions in the present – Cognitive Edge.
I have been teaching the Cynefin framework for a number of years now. Like Dave Snowden i learn as much or more from needing to share it than I do from actually deploying it. I find myself sharing the framework for three applications: strategy and decision making, leadership and basic understanding of complexity. Because the framework is both simple to describe and supported by a deep set of theory and practice, it is always a challenge to make my description simple enough to be understood, but full enough to be appreciated.
So I thought I would put out some step-by-step guides based on how I explain Cynefin to clients and in teaching situations. This is the first one, on using Cynefin to introduce complexity and it’s implications for strategy and decision making. I would love feedback in the comments, especially if I have used terms that are defined poorly or hard to understand, and also if I have made any leaps in logic that are too large to understand in one bound. The goal is simplicity and clarity in description.
Cynefin, complexity and decision making.
1. There are two kinds of systems and problems: ordered and unordered.
2. Ordered problems are predictable and knowable, unordered problems are unpredictable and unknowable. It is important to understand this point deeply, because this is a fundamental distinction that has massive implications.
3. Ordered systems have a reliable causality, that is, causes and effects can be known, and usually display a clear finish line. Sometimes this causality is obvious to everyone, such as turning a tap to control a flow of water. Sometimes this causality is only obvious to experts, such as knowing what causes your car engine to start making strange noises.
4. Unordered systems throw up complex problems and chaos. Complex problems such as poverty and racism, have causality that that only be understood retrospectively, that is by looking back in time, and they have no discernible finish line. We do a reasonably good job of seeing where it came from, but we can’t look at the current state of a system and predict what will happen next.
5. Chaotic problems are essentially crises in which the causality is so wild, that it doesn’t really matter. For example, in the middle of a riot, it does you no good to understand causes until you can get to safety.
6. Because ordered systems display predictable outcomes, we can more or less design solutions that have a good chance of working. We just need to understand the system well enough and enlist the right experts if it’s unclear what to do. Once we have a solution, it will be transferable from one context to another. Designing and building a car, for example.
7. Because unordered systems are unpredictable we need to design solutions that are coherent with the context. For example, addressing the role of stigma in the health care system requires a solution to emerge from the system itself.
8. Complex problems can be addressed by creating many small probes: experiments that tell us about what works and what doesn’t. When a probe has a good result, we amplify it. When it has a poor result, we dampen it. Strategies for amplification and dampening depend on the context, and the problem.
9. In ordered systems, linear solutions with well managed resources and outcomes will produce desired effects. We can evaluation our results against our intentions and address gaps.
10. In complex systems, we manage attractors and boundaries and see what happens. An attractor is something that draws the system towards it. A boundary is something that contains the work. For example addressing the effects of poverty by creating a micro-enterprise loan program that makes money available for small projects (attractor) and requires that it be paid back by a certain time and in a certain way (boundaries). Then you allow action to unfold and see what happens.
11. When you have a solution in an ordered system that works, you can evaluate it, create a process and a training program around it and export it to different contexts.
12. When you have a solution that works in a complex system, you continue monitor it, adjust it as necessary and extract the heuristics of how it works. Heuristics are basic experience based, operating principles that can be observed and applied across contexts. For example, “provide access to capital for women” provides a heuristic for addressing poverty based on experience. Heuristics must be continually refined or dropped depending on the context.
I want to invite you to bite down hard and read this article by Rich Lowry, the editor of the National Review: Baltimore, a Great Society Failure: