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On Cynefin as a Sensemaking Framework: Part Two

May 29th, 2008 · No Comments



It’s now time to review Dave’s characterizations of the three remaining contexts and to comment on them. Again using the HBR article as the primary source for my discussion, the “complicated” domain is characterized as follows.


— Expert diagnosis required
— Cause-and-effect relationships discoverable but not immediately apparent to everyone;
— More than one right answer possible
— Known unknowns
— Fact-based management
— Sense-Analyze-Respond

This is the second domain in Dave’s global category of “order.” It is the domain of experts. Cause and effect relating our decisions to anticipated outcomes still applies and once analysis is completed, accurate predictions based on causal and other predictive rules is possible. But the problems in this domain require effort and often, experts, to solve, and the context of decision making relates to influencing or controlling a complicated construct in which multiple and “knowable” causes and effects are at work. Such systems may involve feedback and cybernetics, in addition to simpler causal relationships. Systems analysis can be used here, but the context involved is not one of emergence. Finally, the mode of action, once the context is deemed to be “complicated,” is Sense-Analyze-Respond.

Looking at complicated contexts from the viewpoint of the DEC-KLC-KM framework, Dave’s characterization raises a number of questions. First, the context seems to be limited to knowledge gaps related to complicated situations where some of the cause and effect patterns are unknown. On the other hand, the “simple” context contained only known “simple” cause and effect relationships, which raises a question about which of the contexts deals with simple cause and effect relations that are “knowable,” but as yet unknown. Dave’s exposition implies that such “known unknowns” are not in the complicated context, because they are too simple; and, according to Cynefin, they’re not in the “simple” context, either, because, once again, they are “unknown,” and “knowable,” “ordered” relationships.

The second question relates to the meaning of “analyze,” in the recommended Sense-Analyze-Response sequence. Earlier, I identified “sense” with monitoring and evaluating in the DEC, and “responding” with planning and acting. However, what does “analyze” correspond to? I think that at the level of a collective it can be identified with the Knowledge Life Cycle itself. In other words,”analyze” summarizes “Acquiring External Information/and farming the results of previous individual and group learning in one’s organization-creating new ideas-eliminating errors in those ideas-integrating ideas.” In short, from the viewpoint of the DEC-KLC-KM framework, “analyze” in the complicated context refers to implementing KLCs to make the unknowns knowns.

Looking at these two questions together, and keeping in mind that even simple cause and effect relationships, if unknown, may well require the efforts of experts to make them known, one resolution of the question of where unknown “simple” cause-and-effect relationships belong in the framework is that they belong in the “complicated” category, not because they’re complicated but because they’re unknown but “knowable” cause-and-effect relationships. And that, in turn suggests, that this category is perhaps more fundamentally about creating new knowledge about causally ordered relationships than it is about “complicated” contexts.

Moreover, the above problem suggests that an analogous, though reverse problem exists with Dave’s “simple” contexts, as well. That is, in the Cynefin “simple” context, no “known” complicated causally ordered systems are included. Since these are also not included in the complicated context, where are they in Cynefin? This problem can be solved by making the Cynefin “simple” context about “known” cause-and-effect and rules-based ordered relationships of whatever degree of complication. In short, the Cynefin framework, would be more clear if the “simple” domain were the “known” ordered domain, and the complicated domain were the “unknown” but “knowable” ordered domain.

In earlier versions of Cynefin, the framework did describe the “simple” context as the “known” domain, and the “complicated” context as the domain of the “knowable.” But even in these versions, it was still true that the “known” domain contained no “complicated” webs of relationships, and the “knowable” domain contained no simple relationships, so the shift to “known” and “knowable” just proposed would still represent a change from Dave’s earlier Cynefin constructs.

The Unordered Contexts

In addition to the two “ordered contexts,” the Cynefin framework also specifies two “unordered” contexts, the “complex” and “chaotic” contexts. These contexts are “unordered” in the sense that “there is no immediately apparent relationship between cause and effect, and the way forward is determined based on emerging patterns.” If this is to serve as a viable distinction between the “ordered” and unordered contexts, we must take it to mean that even after investigation by experts, cause and effect relationships between our decisions and actions and outcomes, that will withstand our evaluations, can’t be formulated; because If they could, we would be dealing with a complicated and not a complex decision making context. The primary characteristics of “complex” contexts according to Cynefin follow.


— Flux and unpredictability
— No right answers;
— emergent instructive patterns
— Unknown unknowns
— Many competing ideas
— A need for creative and innovative approaches
— Pattern-based leadership
— Probe-Sense-Respond

I think there are a number of difficulties with this characterization of complex contexts. First, does the claim that there are “no right answers” mean that we can’t make knowledge claims that are true, I.e. that correspond to reality? If so, how can we know that? How can it be proved?

The answer is that it can’t. It is fine to say, as Dave does, that a complex system is in constant flux and that the whole is more than the sum of its parts. But this is not enough to imply or even suggest the claim that our descriptive statements about such systems cannot be true, or that we cannot formulate true statements relating our decisions to act on such systems to outcomes. We may, in complex contexts, come up with right answers, as far as we know. We may come up with answers that work. Of course, in saying that there are no right answers, Dave may mean that there is more than one solution to a problem and more than one way to affect such a system; but if that’s the case, then, in this respect, the complex context is not different from the complicated context, where Dave also indicates that there may also be more than one solution to a problem.

Second, emergent patterns appearing in complex contexts may be very instructive, but why would they be instructive if they didn’t guide us toward decisions that are more right than others? In particular, emergent patterns involve the emergence of new higher level structures that, in turn constrain agents in complex systems. But don’t such structures introduce a measure of predictability into complex contexts and into the results of our decisions acting upon such contexts? That is, can’t we formulate predictive rules relating new constraining structures to probable behaviors of agents arising, in part, from the constraints imposed by the structures, and, in part, from our decisions? I think we can and often do just that in everyday life.

Third, regarding “flux and unpredictability,” certainly the details of emergent patterns following upon our decisions can’t be predicted in detail in complex contexts, but is the response to our decision entirely unpredictable in such contexts? Can’t our decisions create greater or lesser propensities for certain outcomes to occur? In both the “simple” and “complicated” contexts of “order” in which cause and effect relationships and rules apply, prediction is possible; but does “unorder” imply that predictions of lesser probability are impossible, or just that predictions that are too detailed, or that we can be fairly certain about, are impossible?

Fourth, complexity is considered to be the domain of “unknown unknowns.” That is, it is a domain in which we don’t know what we don’t know. In Dave’s discussion of complicated contexts, these are characterized as the domain of “known unknowns.” But is this really the case? If the complicated context is one where we must produce new knowledge, then how can we know beforehand what this knowledge is? We may know what our knowledge gaps are, alright, and we may even develop ideas about the kind of knowledge we need in general. But how can we know what the unknowns are beforehand, without those unknowns being known?

What if the unknowns that will solve a problem can’t be known without developing knowledge that expresses an entirely novel point of view or theory? Would we then have a “known unknown?” So, in the end, is the “known unknown” state of the complex domain really that different from the state of needing to make new knowledge in the “complicated context? Perhaps it is somewhat different, but I think it’s doubtful that the difference is as great as Dave indicates.

Fifth, though I agree that complex contexts are characterized by many competing ideas, I also think that complicated contexts can also be characterized by many competing ideas, as is also perhaps suggested by Dave’s statement that in such contexts there can be more than one right answer to a question. So, again, I doubt that this characteristic of complex contexts distinguishes it from the complicated context.

Sixth, a similar comment applies to the need for creative and innovative approaches. Sure those are very important for complexity, but new ideas are important in any situation where there’s a knowledge gap. Since, by definition, complicated contexts require closing knowledge gaps and problem solving, they require creative and innovative approaches, as much as any of the other contexts.

Seventh, complex contexts require “pattern-based leadership” in the sense that leaders must learn which emergent patterns ought to be reinforced and stabilized and which should be discouraged, and this is different from “complicated contexts” because in those there is no emergence. So, considering all of the above points, it seems that the characteristics that really distinguish the “complex” from the “complicated” domains most sharply are (a) those having to do with emergence and (b) those having to do with predictability, which even if one feels less inclined to accept the idea that predictability is absent from the “complex” domain, it certainly seems easy to accept the idea that there is less predictability and more uncertainty in complexity than there is in complicated contexts.

Eighth, the recommended mode of action for “complex” contexts is Probe-Sense-Respond. From the point of view of the DEC-KLC-KM framework, however, we can place a slightly different interpretation on this recommendation. Specifically, prior to the classification of a context as “complex,” there is the determination that it is not “simple,” and that a KLC is required to decide what the context is and what decision is appropriate. Next, a determination of which of the other contexts a case is, involves acquiring information either externally or internally as appropriate, and for Cynefin projects involves assembly and analysis by sensemakers of sensemaking items from narrative databases, alternative histories, fables, etc.

Now, this process of classification, from a DEC-KLC-KM point of view, means that the alternative knowledge claims that a given domain is “complicated,” “complex,” “chaotic,” or “disordered,” all get considered and evaluated during the sensemaking process, before Probe-Sense-Respond becomes relevant. In fact, once the decision is made that the domain in question is not ‘simple,” and it’s recognized that there’s a gap in what is “known,” then in the Sensemaking process we actually have: acquiring external information/and farming the results of previous individual and group learning in one’s organization-creating new ideas-eliminating errors in those ideas-integrating ideas, all occurring before Probe-Sense-Respond. So, from the DEC-KLC-KM point of view, we have the following pattern of knowledge processing: problem-acquiring external information/and farming the results of previous individual and group learning in one’s organization-creating new ideas-eliminating errors in those ideas-integrating ideas-Probe-Sense-Respond.

And then, when we look at “Probe” more closely, what do we see? To “probe” is, after all, to “act.” The act may be a safe-fail experiment, or a number of parallel safe-fail experiments. Now, we do not undertake such experiments randomly and without expectations. In the case of complex domains, specifically, we are experimenting by “seeding” the domain in the hopes of obtaining patterns whose effects merit reinforcement.

So, one way to look at this, is as a test of of our “cause and effect” expectations that at least some of the safe-fail experiments we choose to conduct will provide results we will want to reinforce. The experiments that don’t have such results are errors precisely in the sense that the expectation that they will have desirable effects is false, and the patterns they produce will be eliminated. So, we can look at “probes,” and safe-fail experiments, as error elimination activities testing our expectations that they will produce outcomes we may wish to reinforce.

Alternatively, we can look at “probe” from the DEC point of view as involving a plan-act sequence, when we then move on to “sense” the results we become involved in monitoring and evaluating them, and then, if necessary, in acquiring information/and farming previous organization learning-creating new ideas-eliminating errors in ideas-integrating ideas. That is, we become involved in engaging in a KLC focused on determining the effects of the safe-fail experiments. Once that KLC is done, then to “respond,” we plan and act again in the next DEC round in accord with new expectations about the effects our actions will have. In other words, I think it’s quite easy to interpret the Cynefin view of what one would do in the complex domain from the viewpoint of the DEC-KLC-KM framework.

In talking about the chaotic context, Dave Snowden had this to say:

“In a chaotic context, searching for right answers would be pointless: The relationships between cause and effect are impossible to determine because they shift constantly and no manageable patterns exist-only turbulence. This is the realm of unknowables. . . .”

Here are the characteristics of the chaotic context in the Cynefin framework.


— High turbulence
— No clear cause-and-effect relationships, so no point in looking for right answers
— Unknowables
— Many decisions to make and no time to think
— High tension
— Pattern-based leadership
— Act-Sense-Respond

I think it will help to understand these statements if we look for a moment at some of the ideas of “chaotic dynamics.” First, these dynamics are of two types: “deterministic chaos” and “stochastic chaos.” Both types of dynamics appear to be indistinguishable from random motion, but can be distinguished from randomness by the use of certain tests. Deterministic chaos is governed by dynamical laws, and it turns out that these laws are causal in nature. Stochastic Chaos is not governed by such laws but has probabilistic generative mechanisms that account for chaotic dynamics. This kind of chaos is also non-random, but incorporates a random component in its probabilistic framework.

Second, even though chaotic dynamics is either governed by law or by probabilistic generative mechanisms, it is correct to say that chaotic dynamics is in large measure unpredictable. However, it is important to note that chaotic dynamics is not always unpredictable. Its unpredictability arises from (a) the sensitivity of the dynamics of chaotic processes to the initial starting point of those processes, and also from (b) sensitivity of the dynamics to small differences in the form of the laws or probabilistic models used to make predictions. Sensitivity to starting points means that even if one has a good model of dynamics, one’s predictions will diverge from reality very quickly if one’s measurement of the starting conditions is imprecise. Since it is never possible to get a precise enough measurement of the starting conditions in chaotic dynamics it will always be the case that our predictions will diverge from reality within some period of time, normally a short one. However, short term predictions may be possible if the time intervals used in compiling a time series are the right size, so it is not quite correct to say that chaotic dynamics is unpredictable. Moreover, analysts of financial data can use this limited predictability of chaotic series to good advantage provided they repeat their analyses often enough to account for the divergence of the predicted from the actual which will inevitably occur. Now, a similar difficulty in prediction will arise from small differences in the dynamical laws governing a system, so once again, we are talking about very limited, but at least some predictability of chaotic dynamics.

Third, I’ve just said enough, I hope, to indicate that chaotic dynamics is not “unknowable”, in the sense that we must remain ignorant of the mechanisms generating the dynamics of chaotic interactions. Moreover, the fact that we can come to know what the generating mechanisms of chaotic behavior are, can help both our understanding and provide us with a capability for short-term prediction of such dynamics.

Fourth, these first three points may be taken as a corrective to one interpretation of Dave’s characterization of the chaotic context. However, Dave may also be taken as asserting that cause and effect relations between our decisions and chaotic dynamics are unknowable, and that predictability in the area of the impact of our decisions on the chaotic context is absent. If this interpretation of Dave’s characterization of the chaotic context is correct, then I largely agree with Dave, so long as it’s clearly stated that what we can’t predict is the impact of our decisions on the future course of the strange attractor describing chaotic dynamics in a phase space.

Fifth, however, this doesn’t mean that we can’t predict the impact of decisions intended to end chaotic dynamics and to shift an area of interaction out of a chaotic regime. In fact, Dave’s “Act-Sense-Respond” action recommendation for chaotic contexts, is aimed at ending chaotic dynamics, by shifting the context either to a simple or a complex one. Looking at that recommendation from a DEC-KLC-KM point of view, the recommended pattern looks like this: Act-Monitor-Evaluate-Acquiring Information/and farming previous organization learning-creating new ideas-eliminating errors in ideas-integrating ideas-Plan-Act.

And again, acts performed in a chaotic context carry with them expectations of outcomes and ideas about cause and effect, as Dave and Cynthia Kurtz make clear in their article on “The New Dynamics of Strategy . . . “ There, action involving the chaotic context is mostly about shifting dynamics away from the chaotic context into the “complex” or “known” (simple) contexts. More specifically, exerting coercive authority to move dynamics into a rule-governed, “known,” domain, creates a transition to a simple context called “imposition.” That is, exerting coercive authority (the cause), creates a transition to a simple ordered system (the effect). And acting to create multiple attractors to stimulate self-organization (the cause) creates a transition called “swarming,” and its effect, which is location to a complex context.

In short, while chaotic dynamics may be unknowable and unpredictable, within the chaotic domain, in the long term, due to deterministic and stochastic chaos, we can and do formulate cause and effect relationships expressing our expectations about getting out of chaos and into the simple or complex domains.

End of Part Two

Tags: Complexity · Epistemology/Ontology/Value Theory · KM Software Tools · Knowledge Making · Knowledge Management