Simulation game: Change architecting
Put the "chi" back in Ar"chi"tecting
By nature, humans manage complexity by categorization and modeling, whether we call it architecting or not. When “architecting” we decompose (break down), abstract and encapsulate according to meanings that were made from information we have available at a certain point in time. A system metaphor for how we generally architect in IT could very well be
We aim, we shoot, we seem to miss,
and then we endlessly discuss, think or get angry about why we missed,
or we absolutely ignore that the “dividing” goes really well but that the “conquering” does not deliver desired results.
Audience
People wishing to reframe "Divide and Conquer" strategies to a “Divide and Unite” version
We aim, shoot, and if and when we seem to miss we take in the results of our shot,
investigate why we aimed at what we aimed at (obviously),
and then we congruently take aim again ...
or not.
Benefits
- Put the "chi" back into architecting.
- Explore the (re)usefulness of wheels for the purpose of adapting the way we architect to become more effective at building systems that are more effective.
In its current form the workshop synergizes triangles and circles in refactoring spirals. (see history below for other forms and shapes).
Content outline and choreography
In aikido, triangles are often associated with "attack", squares with "defense" and circles with "flow". In human architecture we use these concepts. In this workshop the usefulness of wheels for changing the way we architect and architecting effective organizational and cultural change are explored.
We use the Satir Mandala and Change Model for changing the way we architect and architecting effective organizational and cultural change in an experiential learning cycle:
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Getting acquinted; short explanation on experiential learning and on spirals; Simulation instructions.
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Powerring simulation of a development process in which spirals are used as metaphors guiding development of a system made of Powerrings. The simulation can run in maximally three contextually different groups.
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Read out of product and spiral results by each of the groups and observations made by observers
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Group discussion on how well spirals meet our desires to have an effective tool, a gestalt, to guide development of complex systems.
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For people wishing to actively experiment with spirals on the job: Using the Satir Change Model; Tie-in with simulation experiences and work related experiences.
Cycles within cycles in Natural Patterns Inc.
In the simulation we will go through two or three small experiential learning cycles, using mandalas to record reflection, generalization and tie-in results. This happens within a larger cycle implemented in the whole session. The reflection and tie-in of this cycle are done after the simulation exercise. We end with a mini-retrospective and preparing participants for application of a cycle in the work environment.
Workshop history
This workshop has a long history in finding form. A first version of Change Architecting ran at AYE 2001, facilitated by Bob King, Nynke Fokma and Jerry Weinberg with subtitle, Setting the Essential Unchanging Nature of the Way a System Changes. With containers based on Kaner's facilitation categorization in the corners of a square to put lessons learned from a Powerrings simulation in, the result was a pyramid form.
Building on the in the article Metaphor Architecture and XP (pdf) by David West reported experiential exploration, Change Architecting at OT2003 used a preset form: mandalas (circles) for building powerring products in the simulation company Natural Patterns Inc. This resulted in eXtreme chaos.
Architectures are not.
Architectures provide
realities in which its occupants can create their own realities
More Information on cycles and spirals in Stone Circles
In order of "latest first":
The Oxford Universal Dictionary states a cycle is:
“A circle or orbit in the heavens. A recurrent period in a definite period of years. A period in which a certain round of events or phenomena is completed, recurring in the same order in equal succeeding periods. A long, indefinite period, an age. A round, course […]
On the largest scale, everything is held together by gravity. All matter, all energy attracts other things by the gravitational “force” (or “interaction”), and all matter and energy is affected by it.
Yet gravity is actually the weakest of the forces. It dominates everything on a large scale because it always attracts: everything attracts […]
From “Understanding why the groundhog comes out when he does proves valuable to human medicine”
“For more than 15 years, Cornell researchers have been raising the world’s only disease-free woodchucks (Marmota monax) to study hepatitis B infection and the liver cancer it can cause. Those studies, supported by the National Institutes of Health, have resulted in […]
There are close to 400,000 known different species of plants on the Earth. They range from tiny, single-celled algae to huge sequoia trees.
Life on Earth would not be possible without plants because they are the […]
Rocks have a cycle too!
Rocks are continually circulating in the mantle just below the crust of the Earth and are sometimes thrust up into the crust due to convection currents. That’s when their journey starts!
Imagine stone soup slowly cooking in a big pot in a kettle on a fire. The soup gets thicker as […]
Atoms are forever!?
The mineral (stone) world has no beginning and no end??
Atoms are made of a central core containing a collection of protons and neutrons. Almost all of the mass of the atom is contained in its nucleus. Around that we can imagine a cloud of electrons whose number equals that of the number […]
Our human ability to model “reality” seems amazing. And our thinking extensions seem to deliver way beyond expectations. Verrry reuseful for creating gestalts, be those collective or individual.
If the systems we wish to create are to be more fully human or woolly mammoth, we need to self-organize in more fully humane or woolly mammothy systems […]
Systems thinking: Population density of companies
Much of the (managerial) pictorial language used in organizations can be expressed in differential form:
dN/dt = f (N)
This expression assumes that growth is continuous and that the growth rate dN/dt will be some function of a density N. The density N can be used to represent quantities like capital, resources, information, biomass, etc.
Thinking of any company as a population (of humans) organized somehow to survive together through being part of this organism (organisation) a part of their time, we can use mass dimensions and use the form dN/dt = f (N) to describe the behavior of such populations. Biomass dimensions translate easily to population characteristics. Population can be all of the people employed by an organization or people in specific stakeholder populations. Growth of biomass in an organization can depend on a wide variety of interrelated factors. Some are non-human characteristics of the environment some are characteristics from the culture and some stem from contact with other cultures.
For now I'm assuming all non-human factors remain constant -no economical waves or random environmental fluctuations- and will ignore age and spatial structures and behavioral variation: I'm considering an organizational population homogenous in every respect. For this population type we can write the growth equation in the form:
dN/dt = N [i (N) - e (N)]
Where i is the per capita immigration rate and e the per capita emigration rate of employees. One possible form for such curves is the following:
Where growth in the immediate future is determined by the value of (N). The per capita immigration rate inclines steeply from zero at low densities perhaps because of a capital injection when the company was formed. At a later stage in a companies' life and coming from higher densities its rapid decline at low densities can be the company caught in a death spiral. It has found no way to renew or change strategy effectively and can have chosen for a hiring stop.
The per capita emigration rate also increases rapidly at low densities, perhaps because of high stress levels when starting a company. At a later stage of the companies' life and coming from higher densities the steep decline can be related to downsizing tactics while being caught in a death spiral. With higher densities the per capita emigration rate increases gradually perhaps due to lack of resources or quiet places to think. The per capita emigration rate declines again after this second peak. This could happen, for instance, if group defense strategies with similar companies like non-competition treaties are used.
The resulting growth rate f = N (i-e) leads to the following system dynamics for such homogenously populated companies.
The dashed arrow indicates the direction of growth in the immediate future, in case the initial value of N falls in the region occupied by the arrow. For each value N of the population, the growth in the immediate future is determined by the value of (N).
In those regions of the N-axis where f (N) is negative, the dynamics dN/dt = f (N) will cause the population density to decrease. In those regions of the N-axis where f (N) is positive, the dynamics dN/dt = f (N) causes the population density to increase. If (N) = 0 for some population density N, then the value K is called an equilibrium value. If N has initially the value K, then it will remain at this value [since dN/dt =f (N,) = 0] until it is displaced away from this value by some external pressure. Equilibrium is almost always a point at which the curve f (N) crosses the N-axis. The slope of the curve f (N) is negative at equilibrium K:
df/dN|k < 0
If the population density is moved below K, it will be in a region of positive growth and increase back to K. If it is moved above K, it will be in a region of negative growth and decrease back to K. The tendency of the population's intrinsic dynamics is to push the density toward K once it gets close enough. Such an equilibrium K is called stable. Stability can be used in many different ways, and different concepts may be appropriate in different contexts. For example, the word "stable" can mean population composition (that the same employees are present each year) or the population density of a specific type of company remains constant. Or perhaps some "structural stability" is meant. In all of the above, "stable" is a property that is either possessed or not. It can have also been used in a relative sense instead, for example, calling a population whose density has a smaller coefficient of variation over time a "more stable" population.
In this case "stable" means asymptotically stable in the sense of Liapunov or neighborhood stable or locally stable. The set of population densities E < N < B is called the domain of attraction or basin of attraction of the equilibrium. At equilibrium E the slope of the curve f(N) is positive:
df/dN|e < 0
In this case, the population density will move away from its equilibrium value after the slightest initial displacement away from its equilibrium E, and the equilibrium is called unstable. E can be called an extinction threshold: if for some reason (bad economical climate, downsizing, whatever) the density falls below E, then the company will die and suddenly we're all without a job.
The slope of the curve f(N) is also positive at equilibrium B. This equilibrium is also unstable and can be called a breakpoint, for once we get past equilibrium B we're heading for the next stable state in the life of our case company.
So even with such simple homogenously populated companies we can have multiple domains of attraction. When a company and its environment are stratified and diversified I would expect multiple domains to be all the more likely.
Systems thinking: Attractors
Multiple domains of attraction can be used for investigating "jumps" in cultural issues. "Jumps" are often associated with a continuous change in some system parameter. This can mean that a continuous change in a system parameter can cause a discontinuous -- sometimes large -- change in the system.
A stable focus inside an unstable limit cycle
An unstable focus inside a stable limit cycle
A stable limit cycle with causality loops
An unstable limit cycle with dualism
In a region of the Satir Change Model where the slope is positive, the slightest displacement will cause system effectiveness to move away from the equilibrium, as is the case with the Foreign Element and Integration equilibria. These equilibria are locally unstable.
In a region where the slope of the change curve is negative, like between the beginning of the curve and Late Status Quo and between the top of the middle positive maximum and the Transforming Idea, a sufficiently small displacement can cause the system to move back to the respective equilibrium values, resp. Status Quo and Chaos. In such cases the equilibrium is called a locally stable equilibrium. "Sufficiently small" is within the domain of attraction. The Virginia Satir model has multiple domains of attraction:
N0 < N < Late Status Quo and N1 < N < Transforming idea.