Managing Business Activities To Achieve Results
Managing Business Activities To Achieve Results
see attached file
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
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
In a recent research
project with health
care executives, we
for the interest:
“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,
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
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.
interdisciplinary including biologists,
anthropologists, economists, sociologists,
management theorists and many others in
a quest to answer some fundamental
“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.”
President and CEO
University of Louisville Hospital
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
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
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
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,
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
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
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
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
correlated to the size of the
input. A large push to the
system may not move it at
all. In many non-linear
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.”
Vice President of Patient Care
Hunterdon Medical Center
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
“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
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
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
Jaguar” exemplifies this coming together of the appreciation of the micro and macro
analysis. E.O. Wilson, a renowned biologist, argued that we are seeing the confluence of
the two major foundations of biology: (1) the molecular basis of life, and (2) the
evolutionary basis for human (and ecosystem) behavior. This has profound impacts on
our understanding of organizational health. Some interventions are seen to be context
dependent – we cannot explain the micro functioning without understanding the macro
context. The health of a community or organization impacts the well-being of the
individuals within them. Complexity provides us with the opportunity to look at problems
with multiple perspectives, studying the micro and macro issues and understanding how
they are interdependent.
This section provided some explanations for the complexity science movement in the
physical and natural sciences. But there is an additional explanation for its power in
social systems – the compression of time and space. The next section describes this
seemingly esoteric issue. Some readers may not feel the need to understand the roots of
complexity from this perspective and may skip ahead to the section which addresses the
paradoxes of complexity.
The compression of time and space
One of the unique dimensions of the late 20th century is the apparent compression of
space and time. Why should health care leaders care about something as seemingly
esoteric as the compression of space and time? Most of the models of organization,
methods to improve performance, and measurement concepts which dominate the
management field today were created with the implicit assumption of space and time
lags. In other words, they were designed for a world which in many instances no longer
exists. When these approaches are tried in contexts where there is this space-time
compression, the results are often frustration, stress and lack of improvement. This
section of the paper will demonstrate the compression of space in time using examples
from manufacturing, banking and health care.
Dee Hock, the founding CEO of VISA, refers to the major impact the compression of
time has had in financial markets. In the past, there was an expectation of a time lag (or
‘float’) between the initiation and completion of most financial transactions. For example,
if you purchase an item on credit there is a time lag between when you make the
transaction and when the cash is paid to the supplier. We have elaborate systems
designed to take advantage of this float. This luxury of a time lag (or ‘float’) disappears
with the use of debit cards or equivalent systems of real-time transfer of funds.
Hock argues this same reduction of time lags happens with information today. We used
to have the luxury of a time lag between the discovery of an idea and the application into
practice. This time lag is almost non-existent in many aspects of society today. In health
care, medical research is reported on (often in ‘sound bites’ on the news). The public
access to medical research has often created a push to put the ideas into application
An example of a time lag reduction that has had a remarkable impact on manufacturing
around the world is the idea of ‘just in time’ inventory systems. The idea was a simple
one, eliminate the need for storing, financing and managing inventories by creating realtime order and delivery systems between suppliers and producers. When the concept was
first introduced there were many skeptics. Yet in a very short period of time, this was
standard practice in many (perhaps most) manufacturing industries. Just in time inventory
changed the relationship between suppliers and producers. It was both facilitated by the
improvement in technology and shaped new improvements in technology to get the most
benefit from the concept. Boundaries became blurry between what was “in the
organization” and what was “outside”. Networks were created to minimize the potential
problems if a supplier could not provide the needed goods on time. The definition of
success for a supplier was altered and new skills of flexibility were needed in the
employees and the physical production systems.
Case Study: Time & Space Compression
At a large hospital in Montreal, a change in procedures demonstrates this compression of
time and space. Recently the hospital administrators made a decision to eliminate all
radiology film from the hospital. Instead, x-ray images were stored in computer files and
doctors viewed them on their computer screens. Films which traditionally needed to be
handled, processed and delivered through intermediaries were now directly available
from the radiology department to the surgeons or other direct service providers. After
hearing how quickly and radically this changed the ability of the radiology department to
serve the patient care physicians, several hospitals in Toronto are planning to eliminate
radiology film. In this example, film and all of its associated people and systems were
intermediaries which created both time and space lags between the tests and the reading
or interpretation of the tests.
In terms of compression of space, we can now bypass many of the intermediaries in our
society. Intermediaries play the role of a bridge between organizations or individuals.
When we can access the organization or individual directly rather than through an
intermediary, we are again witnessing a compression of space.
The financial service industry is another case where this compression of time and space
can be demonstrated. Technology has allowed us to bridge huge distances and create
connections which permit simultaneous creation and dissemination of information. We
see this reduction of time lags in banking where the currency float of a few years ago has
shrunk to a point of being virtually non-existent. Money can be transferred instantly
between individuals, organizations and countries. The increased degree of connectedness
aided by technology has eliminated some of the intermediaries in our society. One of the
banks’ prime roles was to be the intermediary between those who had money to loan and
those who had need to borrow money. For a price, the banks would match the players.
Today, this is becoming less significant. When the information of who has money and
who needs money is more widely available, many corporations are bypassing the
intermediary role of the bank. This is not unique to financial services. Due to the
technology which allows increased connectedness, in many industries one can go directly
to the source of the information, product or service.
In our organizations, intermediaries are often layers of management or supervision. Part
of their job is to bridge the gap between the providers of service or front-line workers and
upper management. Bridging the gap creates time lags in our organizations. These lags
provide the information float and hence the luxury (and sometimes the frustration) of
time delays. But these intermediary positions are being eliminated in many industries
through downsizing. If the positions are eliminated but the role of intermediation and the
expectation of float still exist as old mental models, we will simply see over-worked
employees trying to fulfill the same roles but with less resources and less success.
“The tendency of people in positions of
power is to believe that they can control
and they believe in the power of ‘let us
figure it out.’ ‘Let’s hire the experts, let
us sit in a room, figure it out and then
it’ll happen.’ That is a common theme
and it’s one that I just don’t believe in.”
President and CEO
University of Louisville Hospital
Intermediaries also imply external
‘designers’ of a system. The designers
are distanced from the deliverers of the
service. This is a separation of thought
and action in both space and time. The
planners plan and others implement – a
separation in space. The plans are
created first and predetermine the
action steps to take – a separation in
time. Complex adaptive systems have
the capacity to adapt and evolve
without an external designer. They
self-organize without either external or
In highly interconnected contexts, where there is a compression of time and space, the
assumptions of float, intermediaries and external designers are problematic. Many
management models, such as traditional strategic planning processes, are built on the
assumptions of float, intermediaries and external designers. When these assumptions
hold, the models are relevant and useful. They can improve effectiveness and efficiency
in organizations. When the assumptions are invalid, these models can lead to an illusion
of control but an actual loss of effectiveness and adaptability.
Some of the paradoxes of complexity
Complexity science is highly paradoxical. As you study the world through a complexity
lens you will be continually confronted with ‘both-and’ rather than ‘either-or’ thinking.
The paradoxes of complexity are that both sides of many apparent contradictions are true.
The first of these paradoxes is that the systemic nature of a CAS implies interdependence
yet each of the elements which are interdependent are able to act independently.
Interdependence and independence co-exist.
Another paradox in complexity is that
simple patterns of interaction can create
huge numbers of potential outcomes.
Simplicity leads to complexity. CAS
operate in a context that is frequently
unpredictable; not merely unknown but
unknowable. Yet it is the agents’
propensity to predict based on schema of
local conditions that allow them to act in
an apparently coherent manner.
Complexity science is the study of living
systems but living systems die. As a
metaphor associated with life, it needs to
encompass all aspects of the life cycle.
Death is part of this cycle. The traditional
management literature’s depiction of the
life cycle begins at birth and ends at
decline. Complexity also includes the
study of death and renewal.
“As a physician, I learned to think
from a biological perspective. When
I went into management, traditional
artificial, foreign to my experience.
So when I started studying
complexity, I was stunned. Here was
organizations hat compared them to
living things. That makes sense to
Richard Weinberg, MD
Atlantic Health System
Passaic, New Jersey
Complexity is a metaphor
A recent article in a popular magazine argued that we needed to distinguish between
complexity researchers who were using the ‘theory’ from those who were using the
‘metaphor’. What that statement missed is that all science is metaphor, as Gareth Morgan
argues. It is metaphor which shapes our logic and perspective. Metaphor influences the
questions we ask and hence the answers we find. A powerful metaphor becomes deeply
rooted in our ways of understanding and is often implicit rather than explicit. In
biological terms, a metaphor is the schema by which we make sense of our situation.
Complexity science presents a contrast to the dominant scientific and organizational
metaphor and thereby challenges us to see what other questions we can ask about the
systems we are studying or living within. The metaphor of systems as mechanical or
‘machines’ has shaped our studies in physics, biology, economics, medicine and
organizations. Complexity is about reframing our understanding of many systems by
using a metaphor associated with life and living systems rather than machines or
mechanical systems. Viewing the world through a complexity lens means understanding
the world from biological concepts.
The inquiry continues
It is normal to finish a paper with a conclusion – to end with a summary of the key points
and implications. Yet consistent with both the science of complexity and the state of its
development, it seems more appropriate to end with questions. The questions can be
viewed from five levels of analysis:
institution or organization
division, department or work group
Some of the questions below are aimed at one of the levels but most can be used for any
level. We invite you to participate with us in this inquiry as it applies to Your
organization or sector health care. The overall question is, how can complexity science
improve management and the health of organizations?
Some of the other questions to ponder are:
How does co-evolution impact the role of a leader? If everything is changing and
I am part of that change, how do I plan?
If a CAS self-organizes, what is the job of manager or leader of a CAS?
Can we use ideas of self-organization to unleash the full potential of our staff?
Can we create the conditions for emergence as two or more organizations are
coming together in a merger?
What do we have to change to improve the quality of our services and reduce
costs? Can complexity science provide us with any insights to this question?
If an organization is a CAS, what does this imply about strategic planning?
Can we use insights from complexity to improve the health of communities?
If the edge of chaos is the area of greatest innovation, how do we stay on the edge
of chaos? What are the risks of staying on the edge?
What organizational structures, designs, processes etc. are consistent with a
complexity science perspective? How would implementing these ‘complex’ ideas
improve organizations and the services they offer?
How can we ensure complexity science enhances and complements proven
management approaches? Where and when does complexity science add most
value? Where are “traditional” approaches more appropriate?