Complexadaptivesystemstheory 12613245196525-phpapp02

1 • Complexity Concepts
Complex Adaptive Systems
Theory
An Introduction to the Basic Theory and Concepts
John Cleveland
Innovation Network for Communities
First Produced in March 1994; Revised November, 2005

A Two-Page Intro To
Complex Adaptive Systems
Theory

A Quick Intro to Complex Adaptive Systems
The field of complex adaptive systems theory (also known as “complexity” theory) seeks to understand how order
emerges in complex, non-linear systems such as galaxies, ecologies, markets, social systems and neural networks.
Complexity scientists suggest that living systems migrate to a state of dynamic stability they call the “edge of chaos.”
Mitchell Waldrop provides a description of the edge of chaos in his book, Complexity:
“The balance point -- often called the edge of chaos -- is where the components of a system never quite lock
into place, and yet never quite dissolve into turbulence either. . . The edge of chaos is where life has enough
stability to sustain itself and enough creativity to deserve the name of life. The edge of chaos is where new idea
and innovative genotypes are forever nibbling away at the edges of the status quo, and where even the most
entrenched old guard will eventually be overthrown. The edge of chaos is where centuries of slavery and
segregation suddenly give way to the civil rights movement of the 1950s and 1960s; where seventy years of
Soviet communism suddenly give way to political turmoil and ferment; where eons of evolutionary stability
suddenly give way to wholesale species transformation. The edge is the constantly shifting battle zone between
stagnation and anarchy, the one place where a complex system can be spontaneous, adaptive and alive.”
Systems “on the edge” are notable for a “hunger” for novelty and disequilibrium that distinguishes them from rigidly
ordered systems. At the same time, however, they also possess a deep underlying coherence that provides structure
and continuity, and distinguishes them from chaotic systems. Theorists use words like “integrity,” “identity” “persistent
structure” and “self-reference” to describe this opposite characteristic. Systems that evolve along the edge of chaos
periodically re-integrate into structures with temporary stability, which bear recognizable resemblance to the string of
predecessor structures. They are free enough to change, but stable enough to stay recognizable.
In describing the edge of chaos, complexity scientists have documented and analyzed qualities that humans have
sought in their systems for some time. A vibrant democracy is an “edge of chaos” form of governance; a healthy
market is an “edge of chaos” form of economics; a flexible and adaptive organization is an “edge of chaos” institution;
and a mature, well-developed personality is an “edge of chaos” psyche.
In many of our systems, however, we have created forms of organization that are locked in rigid order and incapable of
adaptable evolution (e.g. bureaucracies, monopolies, dictatorships). These forms of social control were often
responses to situations that were previously too chaotic. In multiple sectors of society, we now see a migration from
both extremes of incoherent chaos and rigid order towards the middle “edge of chaos” where systems have the
capacity to grow, learn and evolve.

Basic Features of Complex Adaptive Systems
Complexity scientists have identified several characteristics that distinguish edge of chaos systems from systems that are
either locked in rigid order, or too chaotic for any stability to emerge. These include:
 Autonomous agents. Like a swarm of bees, a flock of birds, or a healthy market, these systems are made up of
many individual actors who make choices about how to act based on information in their local environment. All the
agents make choices simultaneously (“parallel processing”), both influencing and limiting each other’s actions.
 Networked structure. The agents don’t act randomly. They share some common “rules” about how they decide
what to do next. At the level of matter, these common rules are the laws of nature (gravity, electromagnetism, etc.).
At the level of conscious actors, these are decision-making rules (preferences, interests, desires, etc.). These rules
connect the agents together and allow a global coherence to emerge without any central source of direction -- the
“swarm” has velocity, shape, direction and density that do not reside in any individual agent. The rules used by agents
evolve based on their successfulness in the changing environment. The connections between agents in edge of
chaos systems are “moderately dense” — not so interconnected so the system freezes up, and not so disconnected
that it disintegrates into chaos.
 Profuse experimentation. These edge of chaos systems are full of novelty and experimentation. They have a
quality of dynamic stability that is characterized by occasional rapid and unpredictable shifts in shape and direction.
They can react to small changes in big and surprising ways (rumors fly like lightning; a mob forms; the market
crashes; the hive swarms). Such systems can communicate almost instantaneously, experiment with dozens of
possible responses if they encounter a roadblock, and rapidly exploit solutions when one is found.
The attached materials describe some of the basic concepts of complex adaptive systems theory.

A Slightly Longer
Introduction To Complex
Adaptive Systems Theory

There is New Information About How Things Work
We at Integral Assets are not scientists or professional scholars. We’re people who live in communities…who are parents
with young ones in school…we’re managers of organizations…we’re taxpayers, piano players, neighbors and
homeowners…In short, we are not theorists, but practitioners with a practical stake in how our world operates.
When familiar practices begin to fail, we need to invent new ones. At that point, even non-theorists can see that having a
theory about what’s going on can help. We’ve discovered that a quiet revolution in many scientific disciplines in the last ten
years offers us a fundamentally new theory about how things work and that can be very practical: The new understandings
(variously known as “complex adaptive system theory” or “general system theory” or “nonlinear dynamics”) do several things
for us.
These ideas:
• suggest profound changes in the way we think about our world.
• go a long way to explain frustrations and failures in the way we’ve gone about trying to change our world.
• offer promising new options about what to do when we care about getting to a very different place from the status
quo.
Information about this new framework is not easy to come by. Emerging ideas about dynamic systems come from a broad
array of disciplines and cross-disciplinary thinking is uncommon. So, thinkers about systems are cautious and tentative
about drawing conclusions. They warn frequently not to over-generalize. Their writing is dense and hard to synthesize and
the field is broad enough that one needs to read hundreds of books before patterns emerge.
Still, the new ideas are there. Popular books with provocative metaphors are beginning to appear and the ideas in them
spark creative possibilities for us as practitioners. They get us “unstuck”. Our clients and colleagues tell us the ideas help
them to imagine cooperating with existing dynamics instead of struggling to control those same dynamics. We find that in
some important ways, thinking like this helps us to relax and acknowledge being a small part of a larger whole.
(Paradoxically) that increases the chance that we can influence the whole.
We think you might find the ideas liberating as well. See what you think.

Disequilibrium Is A Natural Part of Life
When we experience unpredictable turbulence, most of us react with alarm. People managing their lives and
organizations tend to see systems in disequilibrium as equilibrium systems gone haywire. But it turns out that “punctuated
equilibrium” is normal, necessary and hopeful in all lifelike systems. Without patches of disequilibrium, systems stagnate
and die. Disequilibrium creates new arrangements that lead again to (temporary) stabilities. The underlying cycle looks
like this:
• EMERGENCE – New patterns emerge that help solve old problems. American schools, for instance, began
consolidating into the elementary, junior high and high school pattern around 1900 to fit an industrially based
economy.
• EXPLOITATION – The successful pattern spreads quickly; standard expectations get established, applications
expand and bugs are worked out. American education, for example, grew its mass production schools enormously
between 1900 and 1940.
• EQUILIBRIUM – Temporary stability is established as the system meets the needs of its environment in exchange for
available resources. Between 1930 and 1960, for example, American educators presided over relatively predictable,
efficient operation, producing expected results.
• DISEQUILIBRIUM – Eventually the stabilized relationship between the system and its environment (a larger system
in some stage of this cycle) disintegrates as new problems inherent in the arrangement become apparent. Since the
1960’s for instance, disruptive forces in American education have been squeezing educators and changing rules,
demanding different outcomes and straining the system’s design.
• REINTEGRATION or DISINTEGRATION – One of two things happens: either new patterns emerge capable of
stabilizing at a higher level of functional fit with the environment, or the system breaks apart, dispersing its energy
and resources. If the latter, then new “entrepreneurs” eventually take up the loose elements and start a new pattern.
In the 1990’s, some American schools were re-organizing, while others are watching charter schools take over
resources and functions.
The same cycle can be traced in governmental civil service systems, industrial mass production systems, natural forest
management or weather systems, biological virus populations and (in all likelihood) in community and family systems you
care about.

HOW DO YOU INCREASE THE ODDS…
That you’ll catch the updraft
rather than the downdraft?
The Cycle of Change
Emergence
(Pattern
Formation)
Growth Through
Duplication and
Improvement of
the Pattern
Equilibrium
Descent into Chaos
Disequilibrium
Reintegration at a
Higher Level of
Growth and
Complexity
OR

But Why Seek Disequilibrium?
Systems that survive… that stay in the evolutionary game…
that prove to be “sustainable”… that have the lively,
surprising, interesting quality we intuitively associate with
lifelikeness… systems that succeed in shaping their
environment at the same time they are shaped by it… such
systems turn out to have an insatiable hunger for novelty, for
disturbance, for new and puzzling information… yes, they
gravitate toward disequilibrium periodically.
Such systems are highly sensitive to disruptive, anomalous
information; they notice variations from established patters
quickly.
They respond to new information by experimenting
profusely; they throw whole repertoires of possible
responses at new phenomena, and save the ones that work.
The new information leads to adaptive changes…often is
startling and unpredictable leaps to whole different
frameworks capable of re-integrating a better relationship
with their environment.
“The balance point – often called the edge of chaos – is
where the components of a system never quite lock into
place, and yet never quite dissolve into turbulence
either… The edge of chaos is where life has enough
stability to sustain itself and enough creativity to
deserve the name of life. The edge of chaos is where
new ideas and innovative genotypes are forever
nibbling away at the edges of the status quo, and
where even the most entrenched old guard will
eventually be overthrown. The edge of chaos is where
centuries of slavery and segregation suddenly give way
to the civil rights movement of the 1950s and 1960s;
where seventy years of Soviet Communism suddenly
give way to political turmoil and ferment; where eons of
evolutionary stability suddenly give way to wholesale
species transformation. The edge is the constantly
shifting battle zone between stagnation and anarchy,
the one place where a complex system can be
spontaneous, adaptive and alive.” (Mitchell Waldrop,
Complexity)

Evolution Happens At The Edge of Chaos
Systems use the dynamics of “punctuated equilibrium” to transcend themselves and self-organize into something more
compatible with their environment. This is true at all levels of complexity. Scientific disciplines focus on a particular level
of the evolutionary chain. So when we say that these new ideas about systems staying “on the edge” are cross-
disciplinary, we mean that physicists can see that subatomic particles work this way…chemists can see that atoms and
molecules work this way… meteorologists can see that weather systems composed of air molecules work this way…
biologists can see that cell populations work this way… anatomists can see that organisms work this way… neurologists
can see that neuron networks work this way… ecologists see species working this way… economists see markets
working this way… and sociologists see crowds working this way.
The exciting possibility is that when we describe these conditions that keep systems “on the edge” we are beginning to
understand how systems since the universe began have demonstrated adaptive capacity – the ability to self-organize into
new forms that “stay in the game” of evolution. And that understanding those conditions can help us know how to act in
order to accelerate our own learning, evolution and adaptation.
When we try this idea on for size we notice that:
If this is true, the universe has a direction – it is moving toward more order by itself. We don’t have to resist entropy… or
push the river. We just need to learn how to get out of the way and cooperate with the direction.
If this is true, all the messiness and disorder and unpredictability isn’t evidence of an equilibrium system gone awry.
Instead, it’s part of the inevitable movement toward more order. This feels different; we can relax into it a bit.

The Edge Needs Both Structure & Freedom
As we noted earlier, systems “on the edge” are notable for the “hunger” for novelty and disequilibrium. This distinguishes
them from rigidly ordered systems. At the same time, they also possess a deep underlying coherence that provides
structure and continuity, and distinguishes them from chaotic systems. Theorists use words like “integrity”, “identity”,
“persistent structure” and “self-reference” to describe the opposite characteristic. Systems that evolve along the edge of
chaos periodically re-integrate into structures with temporary stability… which bear recognizable resemblance to the
string of predecessor structures. They are free enough to change, but stable enough to stay recognizable.
They’re mad up of autonomous agents: Think of a “swarm” of bees… or a crowd leaving a football stadium… or a flock
of birds. All can be made up of hundreds of actors with the freedom to make any number of choices about where to go
next at a given moment. You can also think of non-animated systems such as weather systems full of air molecules in
this way. They all make simultaneous “choices” simultaneously, influencing and limiting each other’s actions.
But the agents don’t exist in pure chaos – they’re networked together. They don’t go just anywhere from one moment
to the next. They share common purposes, are subject to common laws (gravity, aerodynamics) and take in information
from a common environment using common perception systems (vision, scents). So they are effectively connected and
an observer who “zooms out” to look at the teeming activity from far enough away sees a coherent whole/pattern over
time. The swarm or the crowd has velocity, shape, direction and density that do not reside in any individual agent.
Autonomous agents networked together create non-linear dynamics. The system can react to small changes in the big
and surprising ways (rumors fly like lightning; a mob forms; the hive “swarms”). Such systems can communicate almost
instantaneously, experiment with dozens of possible responses if they encounter a roadblock and rapidly exploit solutions
when one is found.
The capacity of such a system to learn, grow, adapt and invent novel new patterns, when presented with a
disequilibrium-inducing obstacle, is MUCH higher than that of a marching band, for example, or a machine-robot or a top-
down command/control bureaucracy. Associated in this way, the agents have both autonomy/freedom to choose and
also a structure/connectedness that flexibly constrains their options.

It’s A Spiral Dance, Not A Sharp Line
Notice, however, that it would be a mistake to let the term “edge” imply that adaptive systems find magical “right” balance
of structure and freedom and then walk that fine line consistently. “Punctuated equilibrium” means, instead, that such
systems alternate between order and chaos during the course of their profuse experimentation. The
growth/learning/adaptation looks more like a spiral that enters each state, but stays close enough to the other that
crossing back over into the other state is likely.
Science speaks of the dynamics of “phase transitions” like the ones from ice water or from water to stream. In this
condition, small changes (in heat energy) push the system over to the other state. The same changes would make no
difference at all if applies at other temperatures.
A system on the “ edge of chaos” is sensitively dependent on initial conditions. Some people call this the butterfly
effect; a butterfly flapping its wings in Brazil can set in motion a chain of effects that results in a tornado in Texas hours or
days later because the weather systems are poised on the “edge of chaos”. A few air molecules moving in different
direction can have snowballing effects on their surrounding molecules, with whole different weather patterns resulting. In
stable systems small changes in inputs have small effects, while large shifts have large effects. In systems “on the edge”,
tiny shifts result in huge, rippling phase changes and the same shift in different places products wildly different results.
In other words, at the Edge of Chaos, not all “twitching” leads to turbulence. In truly chaotic systems, every little change
sets the system on a new path, and the result is a “boiling” mixture, with no stable patterns. Systems on the “edge” of
chaos are not actually chaotic – most of the small fluctuations are “dampened” by negative feedback and don’t lead to
nonlinear behaviors. To “upset the apple cart”, a disturbance needs to be strategically positioned in a niche where it can
set off a chain reaction of events linked by positive feedback. Systems on the edge of chaos have just enough of these
niches to stay alive and vibrant, but not enough to disintegrate into chaos.
The point in time when a small disturbance leads to a large change in a system’s behavior is referred to as a “bifurcation
point”. At the point of bifurcation, a system has almost infinite “degrees of freedom” – choices to make about its future.
Biological systems maintain their capacity for creativity by evolving what Briggs and Peat call “bifurcation sensitivity” –
some areas of behavior which are sensitive to small changes. Other areas may dampen change in order to stay stable.
The system’s history teaches it where it’s important to be poised on the Edge and where it matters less.

Not Everybody Makes It…
When we look back across the chain of progressively more complex systems we see the trail of survivors. Nobody charts
or remembers the millions of experiments which have fallen by the wayside because they weren’t adaptive. But when we
set out to emulate the conditions of systems on the edge of chaos (because of their liveliness and adaptability) it
behooves us to remember that these systems are not careful, efficient or controlled.
On the contrary, these systems are profligate experiments, most of whose experiments fail. They spin off huge repertoires
of options and try them all out simultaneously. It takes them many “no’s” to find the “yeses”. Most of us (who have
matured during a period of relative stability) are more use to the careful accountability for resources, the predictability and
control more characteristic of equilibrium systems. The idea of losing swarms of agents to experiment, knowing that some
of them will fail, while others will stumble onto brilliant breakthroughs, feels irresponsible.
Nature’s lesson from these lifelike systems is to remind us that – while the danger of many failures is real and acute –
still, in the long run, the results of NOT staying on the edge of chaos are equally deadly. Systems, which lock into order,
stagnate, repress information and possibilities and converge to monopolistic, rigid solutions. And ultimately – because the
spiral dance is inevitable – they will nevertheless plunge into disintegrating chaos, perhaps more catastrophic because the
rigidly structured order has held off gradual incorporation of new ideas.
The other end of the spectrum is equally fatal: systems which opt for too much unstructured chaos diverge so that
eventually their agents simply don’t influence each other – they cease to be a system and their resources and agents are
available to be attracted into other systems.
“We live in a universe that is alive, creative, and experimenting all the time to discover
what’s possible. This is my favorite realization. We see this at all levels of scale,
whether we’re looking at the smallest microbes or looking out into the galaxies. We live
in a world which in constantly exploring what’s possible, finding new combinations – not
struggling to survive, but playing, tinkering, to find what’s possible.”
Margaret Wheatley “Three Images” Noetic Science Review (Spring 96)

Autonomous Agents Use Rules To Balance Structure and Freedom
One source of communion among agents in a system (on the Edge or not) is
a shared set of “rules”. This kind of rule serves as a sort of instruction to
ourselves that guides our own (agent-like) choices. William Roetzheim (in
Enter The Complexity Lab) offers an algorithmic template for a rule: If you’re
an X, and Y happens, do Z”. For instance, “If you’re an ant and you see a
food crumb straight ahead, dance like this to tell your fellow ants where it is”.
Or, “If you’re on a bicycle and it tips to the left, lean right”. Or, “If you’re a
student and class begins, stop your own thinking and try to understand and
mimic the teacher’s thinking”. You get the idea.
And notice how profound the impact if the rule changes to something like, “If
you’re a student and class begins, take charge of your own inquiry”. If all the
agents were using that rule, a completely different shape would emerge from
the swarm of activity.
Roetzheim programs a compelling illustration into a disk that accompanies his
book so you can play with this on a computer and get the feel of it yourself.
He calls the computer simulation “Boids”. In it, computer simulated birds (Get
it, boids?) are placed random on a screen and set in motion at the same time.
They are autonomous agents, networked together by a rule-based
relationship. Each boid is given three rules by which it “chooses” where to
move in each round: Roetzheim’s three rules are: 1) move toward the center
mass, 2) keep a minimum distance from the others, and 3) match velocity of
the nearest other boids. Set in motion, the boids wander around randomly for
a while, but soon begin to converge into a recognizable flock, which can not
only stay together, but navigate as a whole around obstacles placed in its
path. No “boss boid” is necessary; no manual of job description lays out
procedures for dealing with barriers. However, the flock moves as one and
adapts to its environment as a whole. As Mitchell Waldrop observes on the
opposite page about other phenomena, it is from a few simple rules
governing the interaction of the components that a stable system emerges.
Human Rules
The rules that seem to govern many
human systems are of three basic
kinds: we have instructions to
ourselves about:
• how to detect information: we
become aware of different
information from our environment,
depending on our “rules”.
• how to interpret information: we
have “rules” for what’s important and
what it means.
• how to act in response: our “rules”
prioritize possible responses by
predicting for us how successful
courses of action will be.

Certain Kinds of Rules Lead to the “Edge”
One way these “Edge of Chaos” patterns have been uncovered is by carefully examining very simple systems (on the
theory that we can see the underlying dynamics better there than when we’re looking at complex phenomena). Mitchell
Waldrop describes Chris Langton’s work with one such system in his book Complexity. In this case, cells in a grid were
treated like autonomous agents. Their big choice? Turn yourself ON or OFF. When these cells were given different
kinds of rules governing their relationships, one of four patterns emerged:
CLASS 1 RULES led to frozen static states: eventually, all the cells were either ON or OFF, and no more change
was happening. They “died” or they “converged” on one choice. We’ve been calling this outcome rigid ORDER or
EQUILIBRIUM.
CLASS 2 RULES led to rhythmic oscillation: eventually, groups of cells fell into a repeating patters. Some
numbers of changes eventually led back to the original state, from which the same rules kicked off the same cycle
again. This too is ORDER/EQUILIBRIUM; a limited number of patterns repeat themselves.
CLASS 3 RULES led to randomness: no discernable pattern ever emerged. This is what we’ve been calling
DISEQUILIBRIUM or CHAOS.
But (and this was a surprise to the scientists), there was a final class of rules:
CLASS 4 RULES led to flow of non-repeating emergent patterns: one pattern would pass over the grid, but retain
enough novelty not to get “stuck” in that pattern. Instead, the pattern itself would generate disturbances in its own
regularity… which grew into a new pattern… and another… and another. The system retained its capacity for
coherence/memory and yet novelty/change. It cycled through order and disorder. This is what we’ve been calling
the EDGE OF CHAOS.
Notice that the only difference between the four results is the KIND OF RULE used by the autonomous agents to govern
their interactions with each other.
Our question, of course, is: What would be the human equivalent of a new cellular automation’s CLASS 4 RULES. That
is, what would be the “few simple rules” which – if a swarm of agents used them – would lead to the kind of flexible,
experimental order characterized by natural systems at the Edge of Chaos?

Human Rules Are Complex
Since we are NOT cellular automata, our human rules rarely get formulated as simply as “If you’re and X and you
encounter Y, do Z”. Instead, our ability to detect, interpret and act on information is managed by our brains. Brains
themselves, of course, are also complex systems of neural cells and clusters functioning “on the edge” – in swarm-like
fashion each cluster detects and interprets information (electrical signals) from neighboring cells and acts to send (or not
send) signals itself. The totality of all this parallel-processed neuronal activity results in a global “whole” which governs our
senses, our motor control and our consciousness. The human brain is triune: it developed over eons of evolutionary time
in three stages. Physical evidence of those stages still exists: present day brains still have three parts which are layered
over each other, or “nested”, as many systems which developed through Edge of Chaos dynamics.
We share a part of our brain called the brain stem with reptiles – it controls autonomic functions, instincts, and adrenalin.
It is the source of taboos and territoriality, flocking behavior, mating rituals, and the “fight or flight” instinct. Some of our
“rules” generate from this part of our heritage. Understandably, they are difficult to change; one of our colleagues says they
require a conversion experience.
Enclosing the brain stem is the limbic system, which we share with mammals. Caine and Caine, in their excellent book,
Making Connections;
Teaching and the Human Brain, note that both the amygdala and the hippocampus are located here, and that they
influence the association of events with emotion and the storage of experiences in context-rich “locale” memory that
enable us to map connections, understand narrative lines, be social and manage relationships. Because they have limbic
systems, for instance, mammals care for their young, as reptiles don’t. The “rules” that come from this part of our mental
equipment are somewhat easier to change, although anyone who’s ever tried to change his/her culture or nutritional habits
will agree that these, too, require some sort of shock, deep shift in motivation or “significant emotional experience” if they
are to re-organize.
The third section of our brain, the cerebral cortex, surrounds both the brain stem and the limbic brain and is the seat of
conceptual, logical and formal operational thinking. It permits language, including speech and writing, and makes up 5/6
of our brain’s mass. “Rules” formulated here do not seem easy to change to any of us who have attempted to change a
mental model, a theory or a law. But these rules are relatively flexible: what is required here is simple information. It’s
hard because limbic emotions and feelings are deeply intwined with cerebral concepts and thoughts.

Some Rules For Getting To The Edge
Indisputably, it’s no small challenge to find the human equivalents of the “few simple rules” which keep cellular automata,
turbulent hydrodynamics, virus populations or neuron cluster “on the edge”. But our reading of scientists’ observations of
turbulent-yet-coherent phenomena like these suggests some common themes that we DO believe have human analogs.
We’ve reproduced some of the most fascination science “stories” in the next booklet in this series and listed books where
we read about them in the bibliography. You’re heartily encouraged to dig in and see what patterns stand out for YOU.
But here is OUR mental model:
BREAK IT UP:
Let Autonomous Agents Follow
Simple Rules
Systems on the edge always look
like swarms. Lots of actors are
buzzing around taking autonomous,
but interdependent actions at the
same time. They have the
“authority” to direct themselves and
notable freedom to wander in any
direction. At the same time, they
share a few common “rules” or a
governing sense of themselves as a
“part” that keeps their buzzing
activity close to the “whole”. The
operating unit – the “agent” capable
of autonomous action – is very
small, much smaller than we’re used
to in command and control
organizations. The sense of control
is much more light-handed… or
perhaps it’s deeper-seated, allowing
much more “chaos” into the mix.
WIRE IT TOGETHER:
Create Moderately Dense Connections
Systems on the edge always look like jungles.
The agents buzzing around are in constant,
sometimes invisible communication with each
other. Their choices influence each other;
they depend on each other’s success. Their
connections are messy, overlapping, webbed,
redundant, and fluid. The connections are
chosen rather than designed: connections
that prove useful are deepened, while ones
that have become inactive are shed. There
are often nested levels to the resulting
structure – it’s not true that there is no
hierarchy in this jungle. Some agents “work”
on the system itself (or system-wide aspects)
while others “work” on sub-tasks and the
connections cross such level-boundaries
freely. Information is passed freely to agents
who find it useful, but there is no requirement
to communicate with “everybody”, choking the
system with unrequested information.
EXPERIMENT PROFUSELY:
Feed The Fringe and Nurture
Innovation
Systems on the edge look like
mad scientist’s laboratory.
Agents try hundreds of potential
solutions appropriate to local
conditions. As information
about options that work is
passed through the network,
less promising possibilities are
discarded left and right.
Feedback is rigorous, and
instantaneous so the
experimenter knows what to
refine further. Experiments are
the source of life-giving novelty
and new information.

Leading on The Edge of Chaos
Little of this thinking matches traditional notions of leadership. We know how to be (and expect OUT leaders to be) good
at defining and assigning tasks, designing and ensuring coordination for the whole, making course corrections, ensuring
that there are “no surprises on THIS watch” and incidentally, assuming responsibility for the success and the stability of
the whole. Peter Block’s books The Empowered Manager and Stewardship expose this old-paradigm model of
leadership, which he calls the “Patriarchal Contract” as a fantasy. He offers an alternative “Entrepreneurial Contract”
which we think probably forms the internal mental model of most humans “on the edge”.
But what does the leader “on the edge” do? The leadership design injunctions which we began sketching on the previous
page are (in a sense) instructions to each agent in a swarm about how to step up to leadership. This takes “personal
work” deep in the interior of each person, because it is about unilaterally abdicating what Block calls the Patriarchal
Contract and assuming swarm-like autonomy (and responsibility) for developing a new contract with oneself and with
one’s whole. So what the leader of a swarm does (mostly) is to create conditions under which a critical mass of the
agents in the (potential) swarm can and do make that internal shift to thinking of themselves as autonomous, but
networked experimenting agents.
We’ve found the image of the fern extremely helpful: In James Gleick’s book Chaos, he describes the development of
fractal algorithms which allow a computer to use a “few simple rules” to iterate over and over the placement of a dot (or
other shape). At first, the dots appear at random, and there is no way of knowing (short or following each computer
calculation) where the next dot will appear. But as more and more dots appear, an outline takes place and as dots
continue to be added (by autonomous agents, each following locally appropriate rules) the shape becomes more and
more distinct.
This has come to be our image for what the leader of a system “on the edge” owes the agents who are buzzing around
placing dots: a clear outline of the shape intended to emerge (probably accompanied by some “few simple rules” about
dot placement). We no longer help build strategic plans that enumerate actions; rather we help identify for agents the
crucial sorting mechanisms that would tell any individual agent whether a particular dot (s)he’s considering falls inside or
outside the “fern” being constructed. Then the leader can genuinely let go of control and encourage agents to
opportunistically seize possibilities as they occur locally. Without such a “fern” inside the organization’s DNA, we find most
organizations are incapable of giving up their control-based patriarchal contracts. Such organizations gravitate back to
rigid order regardless of a leader’s intent.

Leaders Balance Agency & Communion
One way our practice with human systems and organizations has changed is that our first diagnostic cut at any situation
is to ask ourselves whether it calls for more agency or more communion, that is, more autonomy for the agents? Or
more belongingness?
In other words, where on the spiral are we? To get to the Edge, do we have to move from order to more chaos? (If so,
introduce more autonomy) Or is it time to organize some of the experimental novelty into a new pattern/structure? (If so,
work on belongingness: identify, share rules)
If conditions are right, learning and growth will EMERGE: cycles of freedom and structure... chaos and order…
disequilibrium. The leader’s task is tending to the CONDITIONS and the helping people navigate the resulting
turbulence.
HOW exactly do you adjust the mixture? HOW does a leader (or an entrepreneurial agent) tilt a system in one direction
or the other? Our experience suggests that these decisions are very opportunistic and situational. There seems to be a
repertoire of possible strategies, from which different leaders at different times in different situations pull different options.
So to explore further what these new ideas mean in any particular arena involves getting specific about that particular
arena: how close to the Edge is it currently? What forces are pushing toward order? Toward Chaos? What would
“reintegrating at a higher level of fitness with its environment” mean for this system? Who has how much autonomy
here? Who MIGHT be loosed to be autonomous? What holds autonomous agents together? Is there a “fern” in
people’s minds?

People We Learned From
John Briggs and David Peat
These two writers have co-authored a number of books, including The
Looking Glass Universe and Turbulent Mirror. We found Turbulent
Mirror to be a highly understandable introduction to the mathematics
of nonlinear systems, intelligible even to those of us who are “math –
challenged”. Briggs book, Fractals, is a deliciously visual exploration
of the theory of fractal geometry that made many of the difficult
concepts understandable.
Murray Gell-Mann
The Quark and the Jaquar is the Nobel Prize-winning physicist’s
framework for thinking about these new ideas. He prefers the images
of simplicity (symbolized by the quark he helped discover) and
complexity (the jaguar and its jungle ecology) to “edge of chaos”
language, but the ideas with which he unifies the two will reflect the
ideas we’ve been talking about here. He’s also a player in Waldrop’s
book.
Stuart Kauffman
One of the seminal thinkers about complexity, Kauffman is an
evolutionary biologist and one of the pillars of the Santa Fe Institute.
His dauntingly technical tome, The Origins of Order, was thankfully
transformed into a reader-friendly form in his more recent book, At
Home in the Universe.
Kauffman is purported to have been the originator of the concepts of
self-organization with traditional ideas about natural selection. He
works to forge a new synthesis of evolutionary theory that takes into
account unlikely sudden leaps into new order, as well as incremental
change. At Home in the Universe should be on the short reading list
of any serious edge of chaos explorer.
Kevin Kelly
Kelly’s book, Out of Control, is the source for the powerful metaphor
of a “swarm” which we use often. Kelly, the executive editor of Wired
magazine, is also clear convincing about the fact that swarm-like
systems have disadvantages as well as the powerful advantages
which are pulling our machine-culture to re-discover them.
Uri Merry
We got introduced to Uri Merry through the Internet, and have been
enamored of his work ever since. His book, Coping with Uncertainty,
is one of the best introductory works to the ideas of self-organization,
chaos and complexity we have encountered. It is easy to read, and
helps integrate some of the practical implications of these often
esoteric-sounding theories. We continue to correspond with Uri and
have joined with him and several others in an on-line exploration of
the links between complexity theory and organizational management.
William Roetzheim
For people who learn best by playing around with their hands and
minds, Enter the Complexity Lab is a must. It names several of the
key new ideas being uncovered by complexity science in clear
language. It also gives you examples on a disk you can play with on a
computer to get your own feel for non-linear dynamics.
Mitchell Waldrop
His Complexity makes the scientific ideas accessible through people’s
stories. He follows the Nobel-caliber scientists in many disconnected
specialists who discovered in the 1980s that there were common
threads running through their disciplines. These pioneers of
Chaos/Complexity science came together at the beginning of the
Santa Fe Institute. You’ll come to the new science ideas through the
eyes and hearts of their discoverers.

More People We Learned From
Margaret Wheatley
Leadership and the New Science was the first book which opened up
to us the possibility of thinking in these new ways. She has a
wonderful appreciation of the mystery and awe embedded in these
ideas, and the joy of exploring them. Her experience is with
organizations and their frustrations and stuck places. She continues
the search for a better way to work together in her new book, A
Simpler Way.
Erich Jantsch
Along with Ervin Lasio, Jantsch is one of the patron saints of self-
organizing systems theory. His work, The Self-Organizing Universe,
is a brilliant synthesis of systems theory, cybernetics, self-organization
and evolutionary theory in a broad framework that provides some of
the key foundational concepts for Ken Wilber’s theories on the
evolution of the planet. It’s sometimes a bit heavy slogging, but
always worth the effort.
John Brockman
Brockman is a long-time commentator on trends in scientific thought.
He edited The Third Culture, a collection of essays by most of the
great thinkers in this field, including Stuart Kauffman, Brian Goodman,
Roger Schank, Francisco Varela, Paul Davis, Murray Gell-Mann,
Chris Langton, and Doyne Farmer. Each of the twenty-three essays is
followed by commentary and critique by two or three of the other
essayists. This provides a unique insight into the way these thinkers
disagree with each other and differ in their approaches. It at least
made us feel good that we weren’t the only ones who were confused!
Ken Wilber
In Sex, Ecology and Spirituality, this author firmly grabbed us by the
scruff of the neck and forced us to recognize that as long as we only
think of all these new ideas through the admittedly exciting lens of
science, we are what he calls “flatlanders”. We miss the 3-
dimensional picture that emerges when one realizes that these
principles of self-organization (he calls them “transcendence” and
“inclusion”) operate not only in science’s exterior objective world, but
also as interior phenomena in individual and collective
consciousness. He expanded our horizons to show us an elegant and
literally awesome framework for how everything works. Don’t miss his
work. The shorter version is A Brief History of Everything, but we
think you’d really like the longer Sex, Ecology and Spirituality, which
documents sources for his extraordinary claims. To encourage you to
tackle the real thing, we’ll send our “Cartoon Guide” to anyone who
writes to tell us you’ve made it though!!

Complexadaptivesystemstheory 12613245196525-phpapp02

Recommended

Recommended

More Related Content

What's hot

What's hot (19)

Viewers also liked

Viewers also liked (7)

Similar to Complexadaptivesystemstheory 12613245196525-phpapp02

Similar to Complexadaptivesystemstheory 12613245196525-phpapp02 (20)

More from Zenobia Beukes

More from Zenobia Beukes (7)

Recently uploaded

Recently uploaded (20)

Complexadaptivesystemstheory 12613245196525-phpapp02