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."
"And there is another problem with Lowry’s analysis here too. He engages in classic retrospective coherence, in which he traces the causes of the present situation back to a set of actions. There is nothing wrong with this, and again, he is not wrong in some of this analysis, probably. But he projects that limited root cause analysis into the future in such a way that he is claims to be able to judge the efficacy of responses to the crises. And in so doing, he does not use this look back at the situation to illuminate his own biases, which would actually be valuable. This is a fatal error in dealing with complex situations. The desire to “fix” Baltimore is fundamentally psychotic. You cannot “fix” a situation like this without eliminating the people that in the middle of it. Lowry does that. Not a single voice quoted in his article is a person living in the middle of this.
And you cannot evaluate the efficacy of responses based on past performance. We are dealing with a complex system. A transformative moment in Baltimore is just as likely to happen as a result of all those neighbours who cleaned the place up as it is but creating a fundamental policy shift or slapping your kid on video or rejigging the tax system to allow more Republicans to be registered.
The problem with that of course, is that in order to deal with situations like this you need to engage the people in middle of it to both make sense of what is going, make meaning of actions, initiate and lead multiple responses at all kinds of scales to what is going on. Having pundits at a distance pronouncing on the efficacy of efforts based on a pre-ordained ideological frame is not helpful because it entrains decision makers to look for data that supports their conclusions.
Unfortunately this is exactly what passes for public discourse and policy making these days. The best thing to do is be quiet and listen to what the people on the ground are saying and doing. They’re stories are the only ones that matter, and their leadership is the only leadership that will make a difference. they may require support from higher levels of government or broader contexts, but they are not helped by all of us pronouncing on their efforts. We have absolutely no benchmarks with which to gauge success or failure. No one has the “answer” for Baltimore. Instead it’s about shared work, shared meaning making, shared leadership and grounded sense-making. It’s about thinking and acting differently."
"Complex problems aren’t solvable; complicated ones are.
Address complexity by sense patterns and weak signals and amplifying them; solve complicated problems by analysing data and problem solving.
In complexity, pay attention to what works and ask why?; for complicated problems, keep your eyes on the prize and study gaps (ask why not?)
Be informed in your strategy by stories, myths and parables that translate across many contexts; for complicated problems, adopt “best” practices and rule based solutions.
Employ collaborative leadership to address complexity; employ experts to solve complicated problems.
In complexity, truth is found in stories; for complicated situations, truth is found in facts.
Complex planning requires anticipatory awareness, meaning that you have to constantly scan for meaning through the system; a vision won;t help you. In complicated situations a vision is useful and the end state can be achieved with logical, well planned steps.
In complexity, the future is already here, but it is quiet and hidden in the noise of the culture. in complicated systems the future is not here and it is well understood what it will take to get there from here.
In complex systems, the solutions will come at you obliquely, out of the blue and in surprising ways, so you need to cultivate processes that allow that to happen. In complicated systems, problems are tackled head on from a position of knowing as much as you can about how to proceed and then choosing the best course of action."
"Key insights include:
The need to manage rising complexity drives the value of collaboration.
67% of workers say that fewer than half the meetings they attend are worth the time, according to research by Ovum.
Properly deployed, today’s technologies deliver exponential improvements in the ability to collaborate.
Advancements in the ability to collaborate are driving extraordinary improvements in overall business performance.
74% of executives say collaborative tools are increasing speed to access knowledge; 58% say they are reducing communications costs."
"When you look at the four domains described here think incremental innovation for working in the obvious, distinctive innovation for the complicated domain, radical or breakthrough for the complex domain and disruptive innovation when it comes to the chaotic domain. Think disorder when you have no established innovation system in place, just expecting innovation to happen!"
"The future of a complex system is growing through perpetual creation. What emerges is something that is partially known and partially unknown because of the almost indefinite number of variables influencing what is going on. Complexity is a paradoxical movement in time that is both knowable and unknowable. Stability and instability cannot be separated here. It is a dynamic that is called stable instability or unstable stability. Uncertainty is a basic feature of all complex systems. Uncertainty means that you can’t predict. Although the specific paths are unpredictable, there is a qualitative pattern. The pattern is never exactly the same, but there is always some similarity to what has already happened. The patterns very similar to each other can sometimes be used to prepare appropriate behavior.
In the end it is about the combination and interaction of the elements that are present and how absolutely all of them participate in co-creating what is happening. None of the elements cause the end result independently. From this standpoint a lighted match does not cause a fire. Rather, the fire took place because of a particular combination of elements of which the lighted match was just one. In the same way, a rude remark does not start a fight. The argument starts as a combination of an offensive remark and a coarse response at the same time. It is about confluence.
The big new idea is to reconfigure agency in a way that brings complex relationships into the center. The task today is to see action within relationships. Knowledge was earlier seen as being stored in content. Today knowledge is understood to be perpetually constructed in communication.
Relationships cannot be understood through spatial metaphors such as process maps or network charts. This is important because unhelpful or wrong metaphors are often a big obstacle to moving forward after the technological constraints are gone.
We need to move towards temporality, to understand what is happening in time.
An organization is not a whole consisting of parts. There is no inside and outside. An organization is a continuously developing or stagnating pattern in time. Industrial management was a particular pattern based on specific assumptions about causality and human agency.
The sciences of social complexity change our understanding of causality and recent developments in psychology/sociology have shown that human agency is not located or stored in an individual, contrary to what mainstream economics would have us believe. The individual mind arises continuously in communication between people."
The simple exercise typically gets a quick and easy solution. One player suggests something obvious and the others follow.<br /><br />The complicated exercise needs a bit of planning, typically everybody suggests something, a quick decision is made and process is adapted according to feedback during the build.<br /><br />The complex exercise doesn’t get better with more planning. The right process emerges and is continually adapted. The sooner teams start to build, the sooner they feel comfortable. It helps if all team members know what the animal/vehicle they want to build actually looks like.<br /><br />The chaotic exercise leads to quite surprising, sometimes not very good solutions (see the building without roof in the picture). People feel uncomfortable all the time, the solution takes longer than before. Especially for managers, this is an aha-experience: “So this is how it feels to change a team...”
This is a brief introduction to concepts and tools of complex systems that can be applied to a wide range of systems. The central notion was the development of an understanding of the complexity profile which quantifies the relationship between independence, interdependence and the scale of collective behavior. By developing such tools we may discover much about ourselves, individually and collectively. The merging of disciplines in the field of complex systems runs counter to the increasing specialization in science and engineering. It provides many opportunities for synergies and the recognition of general principles that can form a basis for education and understanding in all fields.
"These elements, to a great or lesser degree, are present in the informal interaction among people doing the same work, as we saw in the copy repair example. But in a world where the community of practitioners is spread across the globe, and many practitioners work at locations out of the office, the development of judgment cannot not be left to chance and proximity. It must be designed and supported by the organization including: 1) experimentation that leads to learning, 2) treating failure as an opportunity for learning, 3) establishing a systematic process through which reflective conversation occurs about both team and individual actions, 4) and promoting communities of practice."
"Complex business means we should be able to access not only collective intelligence, but emergent intelligence — solutions that emerge from complex systems, without residing in any part in any individual’s head. Emergent outcomes — the ones promised by social collaboration, social marketing and social in general — are not just a hope and a prayer, but real. Trusting in them is not foolish but wise. Our human networks, thoughtfully connected, with some smart methodologies will help us to apply complexity to complexity and make progress against now-intractable problems.
Enterprise 2.0 is not about social, it is about thinking very differently about what is hard. About what is impossible. About what IS possible. About your role in it, and about how a human chorus of intellect can help.
Enterprise 2.0 will measure outcomes dispassionately (with equipoise) as a way to ask questions without assigning blame. It will focus on learning as innovation, and disentangle accountability, blame and outcomes. It will depend on the connected circulation of insight and information of a network, often knowledge-less solutions, and the deepest respect for what people can and will bring to the table, given the chance.
Our mission in the next handful of years is to seek to understand these issues better, so that we can build organizations that can look complexity in the eye and not blink."
"The OODA loop is Colonel Boyd’s attempt to capture what it takes to adapt to a rapidly changing environment. He also pointed out that the best place to put pressure on the enemy is Orient: behave in such a way that they they struggle to understand what is happening around them. And the best place within Orient to attack is Analysis & Synthesis, by presenting forcing them to reconcile New Data that conflicts with their existing beliefs (Previous Experience, Cultural Traditions, and Genetic Heritage).
So success in a rapidly changing environment depends on your ability to learn, which many people will find unsurprising. What’s more interesting though, is that unlearning, setting aside past beliefs that are no longer relevant, is actually much more important and much harder.
In many ways, its your ability to unlearn that drives your long term performance."
Excellent overview of complexity thinking vs systems thinking
Management literature typically emphasizes individuals and locates explanatory power in their personal properties. Leaders are the sources of motivation, control and direction. The manager’s perspective is taken for granted as setting the limits of action and what is thought of as right or wrong.
Management theory is based on the same Cartesian assumptions of self as subject, other as object and relationships as influence and manipulation. This is why the present management thinking severely restricts what is thinkable and doable in the world of networks.
The potential of social media cannot be realized without the very different epistemological grounding of the relational perspective. Power in networks is about “power to” or “power with”, and not “power over”. Independently existing people and things become viewed as co-constructed in coordinated networked action.
The emergent pattern changes when the local interactions change. Self-interest in the network economy looks different from self-interest in the market economy. By seeing one’s actions in a network of mutually beneficial reciprocal relationships aiming to enrich the individual and the collective effort, each individual’s success is more likely to be guaranteed.
Cooperation is the new competition.
In today’s manufacturing plants, information systems are usually very hierarchical and depend on predetermined rules. As manufacturing systems become more complex, it will become much more difficult for individuals to spot small deviations and trends within the system. This means that factories, in a way, will need to become “self-aware” in order to support optimized systems. This self-awareness will cause transformations in the way people work. There will be far greater use of simulation to look at the manufacturing systems’ ability to react to changes, such as the introduction of a new product or factory rearrangement. The line between the virtual and physical world will also start to blur, forcing most manufacturing engineers to become more adept at dealing with virtual control systems simulation and other such technologies.
Complex predicaments (like running a social event or a business, or coping with economic, energy or ecological collapse) have these four characteristics:
The number of variables that can have an effect on the system/situation/event is infinite
Most of these variables are unknown or unknowable; only the most obvious ones can be listed or diagrammed
The relationships between cause and effect in the system are unfathomable; at best you can notice correlations that may or may not be meaningful
It is impossible to predict the outcome of an intervention in the system/situation/event (or when Black Swan events and other unforeseeable interventions will occur)
As we come to understand complex predicaments better, we’re learning that the best approaches to them are very different from what works best for simple or complicated problems. Because all the variables cannot be known, and because cause-and-effect relationships cannot be established in complex situations, analytical approaches (like systems flowcharts) used in complicated problem-solving simply won’t work.
The best approaches in complex situations are, well, complex. They entail the use of many different techniques, some of which we are not very good at, and some of which are quite sophisticated, novel, or nuanced. What I have learned so far is that an effective approach to a complex predicament should have these attributes (and I’ll be using the challenge of peak oil and how the Transition movement is working to address it, to illustrate these attributes):
We can contrast strategic planning and Strategic Doing in another way. The role of metrics and the process of accountability are fundamentally different. In strategic planning, metrics are set by the small group of people who develop strategic plan. The metrics provide a measuring rod to make sure that people lower in the hierarchy––the people charged with the responsibility of executing––are following the plan. Accountability comes from reporting against these metrics: command and control at work.
In Strategic Doing, metrics play a different role. We use metrics to facilitate learning. Whereas strategic planning is a deductive process of thought and action, Strategic Doing using inductive reasoning. We learn as we do. Metrics provide a convenient tool to accelerate our learning. With them, we figure out what works. Without them, we would be lost. Accountability in Strategic Doing comes through transparency and the mutual interdependence embedded in the relationships of the network. Forget command and control. It does not work in open networks. Mutual trust becomes the fuel for economic transformation.
You need multiple parallel experiments and they should be based on different and computing theories.
They must be safe-to-fail, which (to state the obvious) means that if they fail you must be able to survive and consequences and recover
A percentage must fail, if not you are not stretching the boundaries enough and your scanning range is reduced in consequence
Each experiment must be coherent, not just a stab in the dark (hence my liking of the T-Shirt). Ideally coherence should be based on evidence, at a minimum ritual dissent should be used to test the ideas.
Actions speak louder than words, if you are trying to counter a negative story then taking small visible actions that make the story impossible to tell is the best policy. Countering stories with stories rarely works as does countering them with facts. Doing things makes all the difference.
You don't start any experiment, safe-to-fail or otherwise unless you can monitor its impact in real time, or at least within correction time of you ……
… damping or amplification strategy. Working both out in advance is key, so you are ready to respond quickly to either success or failure.
In a complex economy, the way to think about the future is this:
We can’t predict the future.
But we can learn about the patterns from which the future will emerge.
In fact, while we can’t control the future, we can influence it.
The best way to influence the future is by innovating through experiments.
An essay on complex adaptive systems
Complexity science is not a single theory. It is a combination of various theories and concepts from a variety of disciplines—biology, anthropology, economy, sociology, management and others that studies complex adaptive systems (CAS).
All three terms in the name CAS are significant in the definition of a CAS:
Complex implies diversity, many connections among a wide variety of elements.
Adaptive suggests the capacity to alter or change, the ability to learn from experience.
A system is a set of connected or interdependent things. From this definition, it is possible to approach organizations, communities and societies as complex adaptive systems.
The same applies for an organisation as for an individual. Its not enough to run experimental programmes, the key word in deliberative practice is deliberative. There are some key additional requirements:
Experiments need to be constructed to run in parallel
All experiments must be designed as safe-to-fail
Amplification and dampening strategies need to be in place before the experiments are run
Managers need to be targeted on the basis that at least half of their experiments should fail
Research and monitoring needs to provide real time feedback
Now those are all features of interventions in the complex domain of Cynefin. However it may not be enough if we ignore the social processes of learning that create collective capability as much as individual competence. Matrons (something I blogged about back in 2006) had individual competence, but it was developed within a highly ritualised social situation. We need to start thinking about the social context of knowledge development as much as, if not more than, individual competence.
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