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A Complexity Science Primer:
What is Complexity Science and Why Should I Learn About It?
Adapted From: Edgeware: Lessons From Complexity Science for Health Care
Leaders, by Brenda Zimmerman, Curt Lindberg, and Paul Plsek, 1998, Dallas,
TX: VHA Inc. (available by calling toll-free 866-822-5571 or through
Amazon.com)
This paper is called a ‘primer’ because it is intended to be a first step in understanding
complexity science. In house painting, the primer or prime coat is not the finished
surface. A room with a primer on the walls often looks worse than before the painting
began. The patchy surface allows us to see some of the old paint but the new paint is not
yet obvious. It is not the completed image we want to create. But it creates the conditions
for a smoother application of the other coats of paint, for a deeper or richer color, and a
more coherent and consistent finish. As you read this primer, keep this image in mind.
This paper is not the finished product. Ideas and concepts are mentioned but only given a
quick brush stroke in this primer. You will need to look to the other resources in this kit
to get a richer color of complexity.
Complexity science reframes our view of many systems which are only partially
understood by traditional scientific insights. Systems as apparently diverse as stock
markets, human bodies, forest ecosystems, manufacturing businesses, immune systems,
termite colonies, and hospitals seem to share some patterns of behavior. These shared
patterns of behavior provide insights into sustainability, viability, health, and innovation.
Leaders and managers in organizations of all types are using complexity science to
discover new ways of working.
Why would leaders
be interested in
complexity science?
In a recent research
project with health
care executives, we
uncovered
two
inter-related reasons
for the interest:
frustration
and
resonance.
“At first learning about complexity science and what it suggested
about leadership was confusing, even stressful. Once I began to
learn it, to understand it, and to discuss it with other
professionals, it began to make sense… I really believe in it… In
complexity science I’m learning that leaders of modern
organizations have got to take on a different roles – especially in
this health care revolution.”
John Kopicki, CEO,
Muhlenberg Regional Medical Center,
Plainfield, NJ.
There is a frustration with some of the traditional clinical and organizational interventions
in health care. The health care leaders in the study said they no longer trusted many of the
methods of management they had been taught and practiced. They didn’t believe in the
strategic plans they wrote because the future was not as predictable as it was depicted in
the plans. They saw intensive processes of information gathering and consensus building
in their organizations where nothing of substance changed. They were working harder
and feeling like much of their hard work had little or no impact. Complexity science
offered an opportunity to explore an alternative world view. Complexity science held a
promise of relief from stress but also suggested options for new interventions or ways of
interacting in a leadership role.
The second “hook” for health care leaders was resonance. Complexity science resonated
with or articulated what they were already doing. It provided the language and models to
explain their intuitive actions. By having a theory to explain what they ‘knew’ already,
they felt they could get better leverage from their intuitive knowledge and use it more
confidently.
Although we are in the early days of deliberately applying complexity science inspired
approaches in organizations, we are gathering evidence of leaders applying the ideas to
general management and leadership, planning, quality improvement, and new service
development. Some of the application projects have generated positive results while
others are still works in progress. Complexity science holds promise to have an important
impact on organizational performance.
Comparing complexity science with traditional science
Complexity science addresses aspects of living systems which are neglected or
understated in traditional approaches. Existing models in economics, management and
physics were built on the foundation of Newtonian scientific principles. The dominant
metaphor in Newtonian science is the machine. The universe and all its subsystems were
seen as giant clocks or inanimate machines. The clocks or machines can be explained
using reductionism – by understanding each part separately. The whole of the machine is
the sum of the parts. The clockware perspective has led to great discoveries by focusing
on the attributes and functioning of the ‘parts’ – whether of a human body or a human
organization. The parts are controlled by a few immutable external forces or laws. The
parts are not seen to have choice or self determination. The ‘machines’ are simple and
predictable – you need only understand the few guiding external rules which determine
how the parts will behave. There are limits to this perspective when understanding living
systems, and in particular human organizations. Clearly humans are not machine parts
without individual choice and so clockware is a necessary but not sufficient way of
understanding complex systems.
The Newtonian perspective assumes that all can be explained by the careful examination
of the parts. Yet that does not work for many aspects of human behavior. We have all
experienced situations in which the whole is not the sum of the parts – where we cannot
explain the outcomes of a situation by studying the individual elements. For example,
when a natural disaster strikes a community, we have seen spontaneous organization
where there is no obvious leader, controller or designer. In these contexts, we find groups
of people create outcomes and have impacts which are far greater than would have been
predicted by summing up the resources and skills available within the group. In these
cases, there is self-organization in which outcomes emerge which are highly dependent
on the relationships and context rather than merely the parts. Stuart Kauffman calls this
“order for free” and Kevin Kelly refers to it as “creating something out of nothing.”
Complexity science is not a single theory.
It is the study of complex adaptive systems
– the patterns of relationships within them,
how they are sustained, how they selforganize and how outcomes emerge.
Within the science there are many theories
and concepts. The science encompasses
more than one theoretical framework.
Complexity
science
is
highly
interdisciplinary including biologists,
anthropologists, economists, sociologists,
management theorists and many others in
a quest to answer some fundamental
questions
about
living,
adaptable,
changeable systems.
“I found a lot of what we did [in
management] was really dumb. It was
very impersonal. We treated people as if
they were one-dimensional. If you figure
them out, give them strict rules, put
money in front of them, they will perform
better…it was very linear.”
James Taylor
President and CEO
University of Louisville Hospital
Louisville, Kentucky
From physics envy to biology envy
There has been an implicit hierarchy of sciences with physics as the most respectable and
biology as the conceptually poor cousin. Physics is enviable because of its rigor and
immutable laws. Biology on the other hand is rooted in the messiness of real life and
therefore did not create as many elegantly simple equations, models or predictable
solutions to problems. Even within biology there was a hierarchy of studies. Mapping the
genome was more elegant, precise and physics -like, hence respectable, whereas
evolutionary biology was “softer,” dealing with interactions, context and other
dimensions which made prediction less precise. Physics envy was not only evident in the
physical and natural sciences but also in the social sciences. Economics and management
theory borrowed concepts from physics and created organizational structures and forms
which tried (at some level at least) to follow the laws of physics. These were clearly
limited in their application and “exceptions to the rules” had to be made constantly. In
spite of the limitations, an implicit physics envy permeated management and organization
theories.
Recently, we have seen physics envy replaced with biology envy. Physicists are looking
to biological models for insight and explanation. Biological metaphors are being used to
understand everything from urban planning, organization design, and technologically
advanced computer systems. Technology is now mimicking life – or biology – in its
design. The poor cousin in science has now become highly respectable and central to
many disciplines. Complexity science is a key area where we witness this bridging of the
disciplines with the study of life (or biology) as the connecting glue or area of common
interest.
For organizational leaders and managers, the shift from physics envy to biology envy
provides an opportunity to build systems which are sustainable because of their capacity
to “live”. Living organizations, living computer systems, living communities and living
health care systems are important because of our interest in sustainability and
adaptability. Where better to learn lessons about sustainability and adaptability than from
life itself.
Complexity questions
The questions asked by complexity scientists in the physical, natural and social sciences
are not little questions. They are deep questions about how life happens and how it
evolves. The questions are not new. Indeed, some of the ‘answers’ proposed by
complexity science are not new. But in many contexts, these ‘answers’ were not
explainable by theory . They were the intuitive responses that were known by many but
appeared illogical or at least idiosyncratic when viewed through out traditional scientific
theories. Complexity science provides the language, the metaphors, the conceptual
frameworks, the models and the theories which help make the idiosyncrasies nonidiosyncratic and the illogical logical. For some leaders who are studying complexity, the
science is counterintuitive because of the stark contrast with what they had been taught
about how organizations should operate. Complexity science describes how systems
actually behave rather than how they should behave.
“It is a curious thing… at least for me it has been. It is both mind expanding
because of new notions but it also seems like it is affirming of stuff you already
know. It is quite paradoxical.”
James Roberts, MD,
Senior Vice-President,
VHA Inc., Irving, Texas
Complexity science provides more than just explanations for some of our intuitive
understandings. It also provides a rigorous approach to study some of the key dimensions
of organizational life. How does change happen? What are the conditions for innovation?
What allows some things to be sustained even when they are no longer viable? What
creates adaptability? What is leadership in systems where there is no direct authority or
control?
What does strategic planning mean in highly turbulent times? How do creativity and
potential get released? How do they get trapped? Traditional management theories have
focused on the predictable and controllable dimensions of management. Although these
dimensions are critical in organizations, they provide only a partial explanation of the
reality of organizations. Complexity science invites us to examine the unpredictable,
disorderly and unstable aspects of organizations. Complexity complements our traditional
understanding of organizations to provide us with a more complete picture.
That is the good news about complexity science. There is also some bad news.
Complexity science is in its infancy. It is an emerging field of study. There are few
proven theories in the field. It has not yet stood the test of time. But it has become a
movement. Unlike some other movements in the management arena, the complexity
science movement spans almost every discipline in the physical, natural and social
sciences. There is often a huge schism between those who study the world using
quantitative approaches and those who use qualitative methods.
“Out of nothing, nature makes
something. How do you make
something from nothing? Although
nature knows this trick, we haven’t
learned much just by watching…
[Life’s] reign of constant evolution,
perpetual novelty, and an agenda out
of our control… is far more rewarding
than a world of clocks, gears, and
predictable simplicity.”
Kevin Kelly
Out of Control
Complexity has created a bridge or a
merger of quantitative and qualitative
explanations of life. It has attracted
some of the greatest thinkers in the
world including some of the most
highly respected organization theorists
and Nobel prize winners in physics,
mathematics and economics. It has
also attracted poets, artists and
theologians who see the optimism
implicit in the science. By examining
how life happens from a complexity
perspective, we seem to have increased
our reverence for life – the more we
understand, the more we are amazed.
Definition of Complex Adaptive System
The next two sections of the paper need a “warning to reader” label. They are filled with
the new jargon of complexity science. Each new term is a quick brush stroke in this
primer but is explained in greater detail in other sections of this resource kit. For the
reader new to the field of complexity, read the next two sections to get the overall sense
of complexity science. You do not need to understand every term at the outset to start the
journey into understanding complexity.
Complex adaptive systems are ubiquitous. Stock markets, human bodies, forest
ecosystems, manufacturing businesses, immune systems and hospitals are all examples of
CAS. What is a complex adaptive system (CAS)? The three words in the name are each
significant in the definition. ‘Complex’ implies diversity – a great number of connections
between 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. The ‘things’ in a CAS are independent agents. An agent may be a person, a
molecule, a species, or an organization among many others. These agents act based on
local knowledge and conditions. Their individual moves are not controlled by a central
body, master neuron or CEO. A CAS has a densely connected web of interacting agents
each operating from their own schema or local knowledge. In human systems, schemata
are the mental models which an individual uses to make sense of their world.
Description of complex adaptive systems
CAS have a number of linked attributes or properties. Because the attributes are all
linked, it is impossible to identify the starting point for the list of attributes. Each attribute
can be seen to be both a cause and effect of the other attributes. The attributes listed are
all in stark contrast to the implicit assumptions underlying traditional management and
Newtonian science.
CAS are embedded or nested in other CAS. Each individual agent in a CAS is itself a
CAS. In an ecosystem, a tree in a forest is a CAS and is also an agent in the CAS of the
forest which is an agent in the larger ecosystem of the island and so forth. In health care,
a doctor is a CAS and also an agent in the department which is a CAS and an agent in the
hospital which is a CAS and an agent in health care which is a CAS and an agent in
society. The agents co-evolve with the CAS of which they are a part. The cause and
effect is mutual rather than one-way. In the health care system, we see how the system is
co-evolving with the health care organizations and practitioners which make up the
whole. The entire system is emerging from a dense pattern of interactions.
Diversity is necessary for the sustainability of a CAS. Diversity is a source of information
or novelty. As John Holland argues, the diversity of a CAS is the result of progressive
adaptations. Diversity which is the result of adaptation also becomes the source of future
adaptations. A decrease in diversity reduces the potential for future adaptations. It is for
this reason that biologist E.O. Wilson argues that the rain forest is so critical to our
planet. It has significantly more diversity – more potential for adaptation – than any other
part of the planet. The planet needs this source of information and potential for long-term
survival. In organizations, diversity is becoming seen as a key source of sustainability.
Psychological profiles which identify individuals’ dominant thinking styles have become
popular management tools to ensure there is a sufficient level of diversity, at least in
terms of thinking approaches, within teams in organizations. Diversity is seen as a key to
innovation and long term viability.
Many of us were taught that biological innovation was due in large part to genetic
random mutations. When these random mutations fit the environment better than their
predecessor they had a higher chance of being retained in the gene pool. Adaptation or
innovation by random mutation of genes explains only a small fraction of the biological
diversity we experience today. Crossover of genetic material is a million times more
common than mutation in nature according to John Holland. In essence, crossover
suggests a mixing together of the same building blocks or genetic material into different
combinations. Understanding this can lead to profound insights about CAS. The concept
of genetic algorithms is paradoxical in that building blocks, genes or other raw elements
which are recombined in a wide variety of ways are the key to sustainability. Yet the
process of manipulating these blocks only occurs when they are in relationship to each
other. In genetic terms, this means the whole string on a chromosome. Holland argues
that “evolution remembers combinations of building blocks that increase fitness.” It is the
relationship between the building blocks which is significant rather than the building
blocks themselves. The focus is on the inter-relationships.
In organizational terms, this suggests that it is not the individual that is most critical but
the relationships between individuals. We see this frequently in team sports. The team
with the best individual players can lose to a team of poorer players. The second team
cannot rely on one or two stars but instead has to focus on creating outcomes which are
beyond the talents of any one individual. They create outcomes based on the
interrelationships between the players. This is not to dismiss individual excellence. It
does suggest that individual abilities is not a complete explanation of success or failure.
In management terms, it shifts the attention to focus on the patterns of interrelationships
and on the context of the issue, individual or group.
CAS have distributed control rather than centralized control. Rather than having a
command center which directs all of the agents, control is distributed throughout the
system. In a school of fish, there is no ‘boss’ which directs the other fishes’ behavior. The
independent agents (or fish) have the capacity to learn new strategies and adaptive
techniques. The coherence of a CAS’ behavior relates to the interrelationships between
the agents. You cannot explain the outcomes or behavior of a CAS from a thorough
understanding of all of the individual parts or agents. The school of fish reacts to a
stimulus, for example the threat of a predator, faster than any individual fish can react.
The school has capacities and attributes which are not explainable by the capacities and
attributes of the individual agents. There is not one fish which is smarter than the others
who is directing the school. If there was a smart ‘boss’ fish, this form of centralized
control would result in a school of fish reacting at least as slow as the fastest fish could
respond. Centralized control would slow down the school’s capacity to react and adapt.
Distributed control means that the outcomes of a complex adaptive system emerge from a
process of self-organization rather than being designed and controlled externally or by a
centralized body. The emergence is a result of the patterns of interrelationships between
the agents. Emergence suggests unpredictability – an inability to state precisely how a
system will evolve.
Rather than trying to predict the specific outcome of emergence, Stuart Kauffman
suggests we think about fitness landscapes for CAS. A CAS or population of CAS are
seen to be higher on the fitness landscape when they have learned better strategies to
adapt and co-evolve with their environment. Being on a peak in a fitness landscape
indicates greater success. However, the fitness landscape itself is not fixed – it is shifting
and evolving. Hence a CAS needs to be continuously learning new strategies. The pattern
one is trying to master is the adaptive walk or capacity of a CAS to move on fitness
landscapes towards higher, more secure positions.
The co-evolution of a CAS
and its environment is
difficult to map because it is
non-linear. Linearity implies
that the size of the change is
correlated with the magnitude
of the input to the system. A
small input will have a small
effect and a large input will
have a large effect in a linear
system. A CAS is a nonlinear system. The size of the
outcome
may
not
be
correlated to the size of the
input. A large push to the
system may not move it at
all. In many non-linear
systems,
you
cannot
accurately predict the effect
of the change by the size of
the input to the system.
“Some people really want to stop
controlling, but are afraid. Everywhere
things are changing, creating high degrees
of uncertainty and anxiety. And the more
anxious you are, the more in control you
need to be. Making all this even worse,
we’ve bought into the myth that leaders
have all the answers. Managers who
accept this myth have their levels of
anxiety ratcheted up again. …If complexity
theory can begin freeing managers from
this myth of control, I think you’ll see
people a whole lot more comfortable.”
Linda Rusch
Vice President of Patient Care
Hunterdon Medical Center
New Jersey
Weather systems are often cited as examples of this phenomenon of nonlinearity. The
butterfly effect, a term coined by meteorologist Edward Lorenz, is created, in part, by the
huge number of non-linear interactions in weather. The butterfly effect suggests that
sometimes a seemingly insignificant difference can make a huge impact. Lorenz found
that in simulated weather forecasting, two almost identical simulations could result in
radically different weather patterns. A very tiny change to the initial variables,
metaphorically something as small as a butterfly flapping its wings, can radically alter the
outcome. The weather system is very sensitive to the initial conditions or to its history.
An example in an organizational setting of non-linearity is the huge effort put into a staff
retreat or strategic planning exercise where everything stays the same after the ‘big push’.
In contrast, there are many examples of one small whisper of gossip – one small push which creates a radical and rapid change in organizations.
Non-linearity, distributed control and independent agents create conditions for perpetual
novelty and innovation. CAS learn new strategies from experience. Their unique history
helps shape the path they take. Newtonian science is ahistorical – the resting point or
attractor of the system is independent of its history. This is the basis of neo-classical
economics and is the antithesis of complexity.
Complex adaptive systems are history dependent. They are shaped and influenced by
where they have been. This may seem obvious and trivial. But much of our traditional
science and management theory ignore this point. What is good in one context, makes
sense in all contexts. Marketers talk about rolling out programs that were effective in one
place and hence should be effective in all. In traditional neo-classical economics, there is
an assumption of equifinality – it does not matter where the system has come from, it will
head towards the equilibrium point. Outliers or minor differences in the starting point or
history of the system are ignored. The outlier or difference from the normal pattern is
assumed to be dampened and hence a ‘blip’ is not important. Brian Arthur’s work in
economics has radically altered this viewpoint. For example, he cites evidence of small
differences fundamentally altering the shape of an industry. The differences are not
always dampened but may indeed grow to reshape the whole. Lorenz referred to this in
meteorology as sensitive dependence to initial conditions which was discussed earlier as
the butterfly effect. In economics, in nature, in weather and in human organizations, we
see many examples where understanding history is key to understanding the current
position and potential movement of a CAS.
CAS are naturally drawn to attractors. In Newtonian science, an attractor can be the
resting point for a pendulum. Unlike traditional attractors in Newtonian science which are
a fixed point or repeated rhythm, the attractors for a CAS may be strange because they
may have an overall shape and boundaries but one cannot predict exactly how or where
the shape will form. They are formed in part by non-linear interactions. The attractor is a
pattern or area that draws the energy of the system to it. It is a boundary of behavior for
the system. The system will operate within this boundary, but at a local level – we cannot
predict where the system will be within this overall attractor.
A dominant theme in the change management literature is how to overcome resistance to
change. Using the concept of attractors, the idea of change is flipped to look at sources of
attraction. In other words, to use the natural energy of the system rather than to fight
against it. The non-linearity property of a CAS means that attractors may not be the
biggest most obvious issues. Looking for the subtle attractors becomes a new challenge
for managers.
“In the past, when managers have tried to implement change, they’d find themselves wasting
energy fighting off resistors who felt threatened. Complexity science suggests that we can
create small, non-threatening changes that attract people, instead of implementing large-scale
change that excites resistance. We work with the attractors.”
Mary Anne Keyes, R.N.
Vice President, Patient Care
Muhlenberg Regional Mediacal Center
Plainfield, NJ
CAS thrive in an area of bounded instability on the border or edge of chaos. In this
region, there is not enough stability to have repetition or prediction, but not enough
instability to create anarchy or to disperse the system. Life for a CAS is a dance on the
border between death by equilibrium or death by dissipation. In organizational settings,
this is a region of highly creative energy.
Why is complexity science relevant now?
The seeds for complexity science have been around for a long time. The founding parents
of complexity science were often far ahead of their time. Why is now the right time for
complexity science? More specifically, why is this the time for complexity science
studies of human organizations? Turbulence, change, adaptability and connectedness are
not new to the late 20th century. There are at least four reasons why now is the time for
complexity science:
1.
2.
3.
4.
the limit to the machine metaphor
the coming together of biology and technology
the connections between studies of “micro” and “macro” phenomena,
the apparent compressions of space and time.
The first three reasons will be outlined briefly in this section. The last reason, the
compression of space and time, will be described in the next section.
Complexity science is a direct challenge to the dominance of the machine metaphor.
Since Newton, the machine metaphor has been used as the lens to make sense of our
physical and social worlds, including human organizations. The machine metaphor has
been a powerful force in creating manufacturing, medical and organizational advances.
However, its limits are now becoming more obvious. It is as if we have collectively
learned all we can from the machine metaphor and will continue to use that knowledge
where appropriate. But we have more and more instances where the machine metaphor is
simply not helpful. For example, it does not explain the emergent aspects of an
organization’s strategy or the evolution of an industry. Complexity science, with its focus
on emergence, self-organization, inter-dependencies, unpredictability and nonlinearity
provides a useful alternative to the machine metaphor.
In addition to changing the metaphor to interpret events, complexity science is gaining
momentum because of the coming together of biology and technology. Biologists are
using technology to understand biology, for example, in biotechnology. Computer
technologists are using biology to create computer software which has some life-like
characteristics. Without the technological advancements, due in part from the machine
metaphor, we would not be able to replicate nature’s fractal forms, or understand the
implicit process rules that allow flocks of birds to move as one, or explain the chaotic
heart rates of healthy humans. Complexity science is understandable to us now because
of both the advances in technology and the increased respect for biological lessons.
Complexity science brings together the two solitudes of micro-studies and macroanalysis. For example, the micro studies of the human genome and the macro studies of
evolutionary biology are coming together with complexity science. The lessons from the
micro studies are informing the macro analysis and the lessons from the macro studies are
informing the micro. This second learning – the macro informing the micro – has been
underplayed in our search for applying Newtonian scientific thinking to life. A
Newtonian perspective suggests that the parts can explain the whole. Therefore, the quest
is to study the parts in greater and greater detail. Complexity science suggests that the
whole is not the sum of the parts. Emergent properties of the whole are inexplicable by
the parts. In complexity, studies of natural and human systems are explained by both
kinds of analysis – micro (or analysis of the parts) and macro (or holistic analysis).
Murray Gell-Mann, a Nobel Prize winner, discovered and named the quark – clearly a
study of micro parts. But his journey of discovery into the tiniest parts led him to a path
of holistic understanding and an appreciation for ecology. His book “The Quark and the
Jagua