The Minding Organization: Chapter 2

Transforming the Organization into an Organism

Science is often viewed as a source of complexity. In fact, science is dedicated to the pursuit of simplicity. Science provides models for encapsulating seemingly unrelated observations and giving them meaning in a larger context.

The time is ripe for a new metaphor for organizations. The metaphor we shall discuss is the metaphor of the minding organization – how complexity theory can be used to transform an organization into an organism. In such an organism, individual behavior is driven or attracted by a shared purpose. Each individual acts in such a way that if others were to do the same, it would be to the benefit of both the individual and others at one and the same time.  People are aligned and bound by mutual trust and driven by common purpose, like the organs within a living, thriving human being.

The new science of complexity suggests that order, as described by the model of evolution, is not entirely random and accidental. Natural selection, it suggests, is a refinement process that follows a spontaneous transition from chaos to order.

The new science of complexity suggests a model of complex systems that go through a rapid transition from chaos to order by self-organizing. Evolution, then, is the process of slowly paced refinements, working within a state or order initially achieved through an act of spontaneous self-organization.

The spontaneous emerging order can be likened to a revolution, or a major breakthrough innovation, whereas evolution involves the refinements of the new level of order resulting from the revolution.

The two stages of revolution and evolution are shown schematically in Figure 2.1.  The horizontal axis is the time scale. The vertical axis is the scale for the level of order; the higher on the scale, the more order.  To transition from one level of order to a new level, a surge of energy is needed to create a spontaneous event of very high impact, a revolution, that can create the lift to a higher state of order.

self_org

Applying Complexity Theory to Organizations

Miner’s Helmet Example

A small battery with an on/off switch is attached to the helmet and is wired to a light bulb, also on the helmet.

Focus is on the state of the light bulb.

Only 2 states: On or Off

If we have 2 people with such helmets, then we can assume 4 different states:

  1. Both Lights On
  2. Both Lights Off
  3. (1) Light on (2) Light off
  4. (1) Light of (2) Light on

For 3 people, we will have 8 states, every time we add a person with a helmet, the number of states is doubled.

thus for n people, there are 2 to the power of n states.

2^N

for 10 people, 2^10 = 1,024 possible states

The state space encompasses the complete range of possible behaviors that the system can assume.

2 extremes of the spectrum of rules governing the states:

  • Chaos: no links and each person is free to switch his light on or off independent of what all the others do.
  • Order: All the helmets operate off a single battery shared, so only 2 states are possible, on or off.

Between Chaos and Order extreme points, we can have a groups of people agreeing to follow some rules of behavior. This represents a partial connectivity to the system.  Namely, 3 groups, A, B C

  • Group A will assume an on state whenever both B and C are on, and assume an off state otherwise.
  • Group B will assume an on state if at least one or the other groups (A or C) is on, and assume an off state otherwise.
  • Group C will assume an on state if at least one of the other groups (A or B) is on, and assume an off state otherwise.

Adhering to the rules of conduct, the system will settle into the following 3 states in the state space, irrespective of the initial state (which could have been any one of the 1,024 possible states in the state space)

  1. All lights are off.
  2. A cycle of two states, called a state cycle, in which the system alternates between only Group B on to only  group C on, back to only B on, then only C on, and so on forever.
  3. All lights on.

All 3 resulting final behaviors are called attractors. Attractors are end state that the system flows into or gravitates toward when it starts in some other state. If a system starts in an attractor state, it remains there. The attractor can consist of a single state in the state space, or a repetitive cycle of state called a state cycle. The sequence of states that the system follows from an initial state through intermediate states until it reaches an attractor is called a trajectory.

In that environment, chaos transitions into order very quickly.

How a Complex System Becomes an Organism

To appreciate the concepts of chaos and order more fully, let’s assume we go with 200 people instead of 10.  The state space is absolutely staggering. Let’s assume we go through 1 million different states per second.  It would still take billions and billions of years to go through the entire state space.

Now, if that’s true about 200 lights, think of the complexity with 100,000 genes or the 100 billion neurons in the human brain.

Between the extremes of chaos and order, the system can self-organize into clusters that follow rules of behavior internally as well as externally, by influencing and being influenced by neighboring clusters. The size of each cluster and the number of clusters will determine where along the scale from order to chaos the system is positioned. Namely, the degree of order will be determined by the number of attractors, and how rapidly they can be reached from any state in the complex environment. To survive in a variable environment, living systems must strike a balance between the stability of order and instability of change. A system must be stable, but not frozen in one state, nor unstable to the point of quick departure s from one state to another as a result of the slightest change in the environment in which the system must adapt to survive.

Studies of complex systems have indicated that systems adapt best when they operate in a state of order on the edge of chaos, in which a measure of stable constancy is coupled with the flexibility of adaptability – that is, where evolutionary, planned, slow, and orderly changes within the state of order, rooted in habitual thinking, are augmented by revolutionary, rapid, breakthrough innovation rooted in unplanned spontaneous thinking.

Co-evolution

Systems, or parts of systems in the forms of clusters, do not operate in isolation. They are linked together with other clusters and systems and they co-evolve. We invent, shape, change, and refine artifacts; artifacts, in turn, change us. We co-evolve with artifacts that we create; we shape them as they shape us. The revolution is in the invention of the artifacts. Once invented and found useful, the reciprocity starts. The artifacts pull people to adopt them because they help attain human goals. Those who do not adopt them fall behind. Major breakthrough innovation and inventions create revolutions that usher new futures for mankind, futures that demand that we co-evolve with the innovation or invention at a new level or order.

We are in a state of flux. We are on the edge of chaos. It is time for great innovation and creativity to spark ideas for the next revolution.  Only with mutual trust can we remove the blocks and constraints that keep human potential for creativity from rising to ever higher levels.

To thrive on the edge of chaos the organization must be transformed into an organism in which deliberate planning is augmented by emergent strategies for adapting to a future that arrives unannounced.

 

 

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