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Complexity Science

"We cannot solve our problems with the same thinking
we used when we created them."
- Albert Einstein

 

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Complex systems exist on the edge of chaos.  Sometimes they show regular predictable behavior.  But they can undergo massive unpredictable changes in response to emerging new patterns in the world.  That’s what we see happening now.  Complexity science seeks to discover and understand these new patterns.   Novel dialogue processes such as TAP meet the need to provide a way to understand these emerging patterns and draw from them imaginary new solutions to prepare for the unknown future.

Properties of Complex Adaptive Systems Relevant to Social Systems

This new science is a science of collective behavior that is full of surprising dynamics, as human and natural systems co-evolve in a mutual search for renewal and balance. 

Below are a few examples of some of the properties that underlie the theory of Complex Adaptive Systems science that are especially helpful in problem-solving in complex adaptive social systems.

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The Adjacent Possible (TAP) 

The Adjacent Possible is a term that Complexity scientist Stuart Kauffman coined to describe his dramatic insight (and resulting equation), about the historic process of technological innovation and its consequences for human well-being in the near future. He found that fossil-fuel based technology has grown exponentially since the industrial revolution, pushing our living systems to “the edge of chaos” and climate catastrophe.  The Center for Emergent Diplomacy has borrowed the term as a metaphor to describe the global search for a paradigmatic shift to planetary balance and technology appropriate for the future. 

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Self-organization

Resistance movements, like the Black Lives Matter protests and student and faculty encampments to protest the war in Gaza, happen spontaneously when a window opens for long-suppressed social justice imperatives to go through.  One small action, like the killing of a black man by police that is filmed and goes viral on the internet, initiates a spontaneous and leaderless mobilization that can self-organize into a critical mass. A large protest is a nonlinear system that adapts rapidly to the changing conditions inside the gathering and to the reactions outside.

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Radical Uncertainty

The Buddhist principle of not-knowing, the beginners mind, and a tolerance for the unknowable and even the inconceivable captures the excitement for researchers and practitioners who adopt complex systems thinking. The paradigm shift toward planetary balance begins here with the individual.

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Nonlinearity

We are taught linear thinking which examines each property in a system and adds them up to describe the whole. They are then static, and we can describe and predict what happens next (Newton's "Clockwork Universe"). But nonlinear systems, like all social and natural systems, are nonlinear, dynamical, rapidly adapting, and thus unpredictable. We cannot predict the weather or the stock market as two examples, because the nonlinear conditions and dynamics are changing so quickly. This is harder to understand since we are taught the fatal myth that we can control these systems.

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Tipping Points

Complex Adaptive systems can quite often become chaotic.  When we feel that we are standing on the edge of chaos we may be watching a system cross a critical threshold called a tipping point.  When a careless camper leaves a small campfire unattended, that small condition may quickly become the tipping point that explodes into a raging and uncontrollable wildfire.

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Emergence

Complexity science is the science of surprise, the surprises that happen when conditions change suddenly and something completely new and unexpected emerges--sometimes from our best efforts to intervene. When the Adjacent Possible (TAP) Dialogue brings the knowledge that our imagined control is just a distraction to the work of assuring a more balanced and sustainable planet, we open our hearts and minds to the spontaneous, the unpredictable, and the surprise of new strategies for breakthrough.

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