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.
Complexity, chaos and enterprise-architecture http://bit.ly/bvIx4r #KM #complexity
Analysis has a dominant place in organizational and community life. It provides a sense of security that we can figure things out and operate in the space of the known. If we just analyse a situation enough we can identify all if the aspects if the problem and choose a solution. Of course in the complicated domain, where causes and effects can be known even though they are separated in time and space, analysis works beautifully. But in complex domains, characterized by emerged phenomenon, analysis tends to externalize and ignore that which it cannot account for with the result that solutions often remain dangerously blind to surprise and “black swan” events.
Bezos does have an answer though: Break big problems down into small ones. Distribute authority, design, creativity and decision-making to the smallest possible units, and set them free to innovate. Small teams focus on small, measurable components that customers value.
Task variety and standardization: Routine vs. knowledge work
Instead of over generalizing the value of any solution it’s best to truly understand the skill and knowledge requirements of the jobs, roles or initiatives you support. I’m not talking about task or needs analysis (through both are valuable tools). Instead go up one notch higher and categorize of the types of “work” you support in your organization. Almost all work, indeed entire organizations and industries, vary on a continuum of two broad factors: task variety and task standardization.
Automation of higher-level jobs is accelerating because of progress in computer science and linguistics. Only recently have researchers been able to test and refine algorithms on vast data samples, including a huge trove of e-mail from the Enron Corporation.
"The economic impact will be huge," said Tom Mitchell, chairman of the machine learning department at Carnegie Mellon University in Pittsburgh. "We're at the beginning of a 10-year period where we're going to transition from computers that can't understand language to a point where computers can understand quite a bit about language."
Nowhere are these advances clearer than in the legal world.
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