Recognize it or not, to organizational leaders, science
matters. While names like Galileo, Newton, and
Descartes do not routinely appear on lists of management gurus, scientists such as these have had a
profound effect on management thinking, and thinking in general. Science shapes the way we view the
world; providing metaphors that help us make sense of events, and thereby giving us a framework for
acting to influence the future course of those events.
Since the time of the Renaissance, the predominant metaphor
of science has been that of the machine.
Scientists of the time described the universe as a grand clockwork. The planets spun about the Sun in
predictable orbits and physical bodies moved in trajectories that could be described with the precision
of
mathematics. The goal of science was to reduce the world to its piece parts, understand those parts,
and
then put them back together in new ways to make new things.
This thinking pervades our view of leadership and management.
Organization charts, job descriptions,
corporate policies, detailed strategic and operational plans, and countless other artifacts of modern
organizational life, are deeply rooted in the machine metaphor.1 These are our attempts to specify,
in
increasing detail, the piece parts of organizational systems so that the overall clockwork of the organization
can better produce the outcomes we desire.
Despite our attempts to control the machine of the modern
organization, and despite the numerous,
undeniable successes from the use of these machine-control techniques, it remains our common experience
of the world that "stuff happens." For example, Coca-Cola reduced consumer judgment to its
piece parts,
conducted scientifically sound taste tests, developed a detailed product launch plan, and found the
"New
Coke" surprisingly rejected by the marketplace. Countless merger and acquisition deals have been
thoroughly analyzed and declared "sure winners," only to have the whole thing come unraveled
as the
merged organization never quite learns to work synergistically as one. Reengineering, TQM, and numerous
other improvement approaches that have worked with great success in one organization, fail miserably
when installed in another organization.
True to the machine metaphor, our usual reaction to such
"stuff" is to retrace the analysis, pinpoint where
we went wrong, extract lessons learned, and fix things up for the next round of analysis. The organizational
world is a machine-we think, not unlike the scientists of the Renaissance-and it is only a matter of
time and
technology before we will understand its parts in enough detail to be able to describe it completely
and
harness it totally. The usual result: different "stuff" happens the next time around.
While there are undoubtedly routine aspects of organizational
life where the machine metaphor fits, there
are, just as undoubtedly, aspects where it does not. We need new metaphors to help us understand the
emerging stuff of the modern, complex organization. Fortunately, science has again preceded us.
|
"As a physician, I learned to think from a
biological perspective. When I went into
management, traditional organizational theory
seemed artificial, foreign to my experience. So when
I started studying complexity, I was stunned. Here
was a way of thinking about organizations that
compared them to living things. That makes sense
to me, intuitively."
Richard Weinberg, MD,
Vice President,
Network Development,
Atlantic Health System,
Passaic, New Jersey.
|
New thinking from the relatively new science of complexity
is radically altering our views on the
management of organizations and other human social systems. For the past two years, we have been
working with 30 leaders from VHA health care organizations in applying the lessons from this new science
to the practice of management. Our efforts parallel those of similar groups of leaders outside healthcare
in
forums such as the Santa Fe Institute's Business Network.
Our on-going, practical application work leads us to
describe an emerging set of management principles for
viewing the workings of complex organizations. These emerging principles suggest new directions for
management action; directions that often run counter to our learned instincts based on the machine
metaphor.
What is a Complex Adaptive System (CAS)?
The new thinking to which we refer comes from the study
of complex adaptive systems. Over the past 20
years, this field has attracted leading thinkers-including several Nobel laureates such as Murray Gell-Mann,
Phillip Anderson, Kenneth Arrow, and Ilya Prigogine-from such diverse fields as physics, biology,
chemistry, economics, mathematics, engineering, and computer science. Key work in the field has taken
place at several academic and research centers around the world; most notably the Santa Fe Institute
in
New Mexico. In this section, we will briefly describe some of the key concepts from this work.2 In
subsequent sections we will illustrate these concepts more fully with examples from our work with
organizations.
Definition:
A Complex Adaptive System (CAS) is a system of individual agents, who have the
freedom to act in ways that are not always totally predictable, and whose actions are
interconnected such that one agent's actions changes the context for other agents. Examples
of complex adaptive systems include: the stock market, a colony of termites, the human body
immune system; and just about any collection of humans such as an industry, a business
organization, a department within an organization, a team, a church group, a family, or the
Rotary Club.
In
a CAS, agents operate according to their own internal rules or mental models (the technical term is
"schemata"). In other words, each agent can have its own rules for how it responds to things
in its
environment; each agent can have its own interpretations of events. These rules and interpretations
need not be explicit. They do not even need to be logical when viewed by another agent. These are
clearly characteristics of humans in just about any social system.
Agents
within a CAS can share mental models, or be totally individualistic. Further, agents can change
their mental models. Because agents can both change and share mental models, a CAS can learn; it's
behavior can adapt over time, for better or for worse. Adaptation means that the agents and the
systems in which they are embedded co-evolve. Again, we clearly know that human organizations
change and adapt over time; again, sometimes for better sometimes for worse.
The
behavior of a CAS emerges-and this is a key point-from the interaction among the agents. It is
more than merely the sum of its parts. Further, each agent and each CAS is embedded, or nested, within
other CAS, providing further interactions. For example, a person is a CAS... they are also a member
of
team... the team is embedded in a department... which is nested in an organization... which is part
of an
industry... and so on; there are interactions all up and down the line.
A
CAS can, and usually does, exhibit novel behaviors that stem from these interactions. Because of the
interaction, the behavior of the system is also non- linear; seemingly small changes can result in major
swings in system behavior, while seemingly large changes might have no effect at all. For example, a
change effort in one organization might involve management retreats, employee meetings, memos and
much fanfare, and yet have no discernible effect only a month later. In another organization, a rumor
about a chance comment made by a senior leader in the washroom can touch off a major union
organizing effort that forever changes the landscape of the company. We are usually surprised when
such things happen. However, when we learn to view systems through the lens of CAS, such
unpredictable outcomes are not so surprising.
Because
of this novelty and non-linearity, the detailed behavior of a CAS is fundamentally
unpredictable. It is not a question of better understandings of the agents, better models, or faster
computing; as we have come to believe erroneously, based on the machine metaphor. We simply
cannot reliably predict the detailed behavior of a CAS through analysis. We must let the system run
to
see what happens. The implications of this are that we can never hope to predict the detailed behavior
of a human system. While this seems obvious to say, note how often managers and leaders act as if we
can be sure about how others should act in response to our actions; for example, when we install a
program that worked in another company and then wring our hands and point our fingers when the
predicted success fails to materialize in our own organization.
Still,
despite this lack of detailed predictability, it is often possible to make generally true, practically
useful statements about the behavior of a CAS. For example, while we cannot predict the exact closing
reading of the Dow Jones Industrial Average tomorrow, we can describe the overall stock market trend
as bullish or bearish and take appropriate investment action. This gives us some hope in understanding
complex human systems, we just need to be careful not to over-estimate our ability to predict what will
happen. Over-estimation is the usual mistake that we all make; if you have ever been surprised by how
something has turned out, you may have fallen into the trap of over-estimating your ability to predict.
Ilya
Prigogine3, Stuart Kauffman4, and others have shown that a CAS is inherently self-organizing.
Order, creativity, and progress can emerge naturally from the interactions within a CAS; it does not
need to be imposed from outside. Further, in a CAS, control is dispersed throughout the interactions
among agents; a central controller is not needed. Consider, for example, the CAS of the lowly termite.
Termite mounds are engineering marvels; the highest structures on the planet, when compared to the
size of its builders. Yet there is no CEO termite, no architect termite, no blueprint, no termite on
a far
away hill viewing the structure in perspective and radioing orders for adjustments as the building
proceeds. Each individual termite acts locally, within a context of other termites who are also acting
locally. The termite mound emerges from a process of self-organization. In contrast, most of our
traditional management theory is about how to establish order and control through the actions of a few
people at the top of an organizational hierarchy. This management instinct, one that we have all
learned, may be the biggest factor holding back innovation and progress in our organizations.
Christopher
Langton5 calls the set of circumstances under which this creative emergence arises "the
edge of chaos." This is a place where there is not enough agreement and certainty to make the choice
of the next step trivial and obvious, but neither is there so much disagreement and uncertainty that
the
system is thrown into complete disorder. We have all been there many times in our lives within
organizations. It is that anxious point in time when the plan has not quite come together yet; when
it
feels like we are on to something but no one is quite sure just what that something is. Our learned
instinct in such moments is to try to achieve concreteness, troubleshoot the issues, and take action
to
fix things; in essence to break down the ambiguity into piece parts so that we can go on assembling
our
plans in a logical manner. The study of complex adaptive systems suggests that we might often be
better off maintaining the anxiety, sustaining the diversity, letting the thing simmer for a while longer
to
see what will happen on its own. This is indeed uncomfortable for leaders schooled in machine thinking.
|
Key
points form the theory of complex adaptive systems:
-
individual agents
- interpretation and action is based on mental models
- agents can have their own or shared mental models
- mental models can change; learning, adaptation, and co-evolution is
possible
- interconnections among agents, and systems embedded within systems
- system behavior emerges from the interaction among agents
- action by one agent changes the context for others
- the system can exhibit novel behavior
- the system is non-linear; small inputs can lead to major outcome swings
- system behavior is fundamentally unpredictable at the detail level
- broad-brush prediction of system behavior is sometimes possible
- order is an inherent property of the system, it need not be imposed
creative emergence has its best chance to appear when
there is a little (but not too
much) disagreement and uncertainty
|
The
Stock Market: An Example of a Complex Adaptive System.
The
new thinking to which we refer comes from the study of complex adaptive systems. Over the past
20 years, this field has attracted leading thinkers-including several Nobel laureates such as Murray
Gell-
Mann, Phillip Anderson, Kenneth Arrow, and Ilya Prigogine-from such diverse fields as physics,
biology, chemistry, economics, mathematics, engineering, and computer science. Key work in the field
has taken place at several academic and research centers around the world; most notably the Santa Fe
Institute in New Mexico. In this section, we will briefly describe some of the key concepts from this
work.2 In subsequent sections we will illustrate these concepts more fully with examples from our work
with organizations.
Definition:
A Complex Adaptive System (CAS) is a system of individual agents, who have the
freedom to act in ways that are not always totally predictable, and whose actions are
interconnected such that one agent's actions changes the context for other agents. Examples
of complex adaptive systems include: the stock market, a colony of termites, the human body
immune system; and just about any collection of humans such as an industry, a business
organization, a department within an organization, a team, a church group, a family, or the
Rotary Club.
In
a CAS, agents operate according to their own internal rules or mental models (the technical term is
"schemata"). In other words, each agent can have its own rules for how it responds to things
in its
environment; each agent can have its own interpretations of events. These rules and interpretations
need not be explicit. They do not even need to be logical when viewed by another agent. These are
clearly characteristics of humans in just about any social system.
Agents
within a CAS can share mental models, or be totally individualistic. Further, agents can change
their mental models. Because agents can both change and share mental models, a CAS can learn; it's
behavior can adapt over time, for better or for worse. Adaptation means that the agents and the
systems in which they are embedded co-evolve. Again, we clearly know that human organizations
change and adapt over time; again, sometimes for better sometimes for worse.
The
behavior of a CAS emerges-and this is a key point-from the interaction among the agents. It is
more than merely the sum of its parts. Further, each agent and each CAS is embedded, or nested, within
other CAS, providing further interactions. For example, a person is a CAS... they are also a member
of
team... the team is embedded in a department... which is nested in an organization... which is part
of an
industry... and so on; there are interactions all up and down the line.
A
CAS can, and usually does, exhibit novel behaviors that stem from these interactions. Because of the
interaction, the behavior of the system is also non- linear; seemingly small changes can result in major
swings in system behavior, while seemingly large changes might have no effect at all. For example, a
change effort in one organization might involve management retreats, employee meetings, memos and
much fanfare, and yet have no discernible effect only a month later. In another organization, a rumor
about a chance comment made by a senior leader in the washroom can touch off a major union
organizing effort that forever changes the landscape of the company. We are usually surprised when
such things happen. However, when we learn to view systems through the lens of CAS, such
unpredictable outcomes are not so surprising.
Because
of this novelty and non-linearity, the detailed behavior of a CAS is fundamentally
unpredictable. It is not a question of better understandings of the agents, better models, or faster
computing; as we have come to believe erroneously, based on the machine metaphor. We simply
cannot reliably predict the detailed behavior of a CAS through analysis. We must let the system run
to
see what happens. The implications of this are that we can never hope to predict the detailed behavior
of a human system. While this seems obvious to say, note how often managers and leaders act as if we
can be sure about how others should act in response to our actions; for example, when we install a
program that worked in another company and then wring our hands and point our fingers when the
predicted success fails to materialize in our own organization.
Still,
despite this lack of detailed predictability, it is often possible to make generally true, practically
useful statements about the behavior of a CAS. For example, while we cannot predict the exact closing
reading of the Dow Jones Industrial Average tomorrow, we can describe the overall stock market trend
as bullish or bearish and take appropriate investment action. This gives us some hope in understanding
complex human systems, we just need to be careful not to over-estimate our ability to predict what will
happen. Over-estimation is the usual mistake that we all make; if you have ever been surprised by how
something has turned out, you may have fallen into the trap of over-estimating your ability to predict.
Ilya
Prigogine,3 Stuart Kauffman,4 and others have shown that a CAS is inherently self-organizing.
Order, creativity, and progress can emerge naturally from the interactions within a CAS; it does not
need to be imposed from outside. Further, in a CAS, control is dispersed throughout the interactions
among agents; a central controller is not needed. Consider, for example, the CAS of the lowly termite.
Termite mounds are engineering marvels; the highest structures on the planet, when compared to the
size of its builders. Yet there is no CEO termite, no architect termite, no blueprint, no termite on
a far
away hill viewing the structure in perspective and radioing orders for adjustments as the building
proceeds. Each individual termite acts locally, within a context of other termites who are also acting
locally. The termite mound emerges from a process of self-organization. In contrast, most of our
traditional management theory is about how to establish order and control through the actions of a few
people at the top of an organizational hierarchy. This management instinct, one that we have all
learned, may be the biggest factor holding back innovation and progress in our organizations.
Christopher
Langton5 calls the set of circumstances under which this creative emergence arises "the
edge of chaos." This is a place where there is not enough agreement and certainty to make the choice
of the next step trivial and obvious, but neither is there so much disagreement and uncertainty that
the
system is thrown into complete disorder. We have all been there many times in our lives within
organizations. It is that anxious point in time when the plan has not quite come together yet; when
it
feels like we are on to something but no one is quite sure just what that something is. Our learned
instinct in such moments is to try to achieve concreteness, troubleshoot the issues, and take action
to
fix things; in essence to break down the ambiguity into piece parts so that we can go on assembling
our
plans in a logical manner. The study of complex adaptive systems suggests that we might often be
better off maintaining the anxiety, sustaining the diversity, letting the thing simmer for a while longer
to
see what will happen on its own. This is indeed uncomfortable for leaders schooled in machine thinking.
|
Key points form the theory of complex adaptive systems:
-
individual agents
- interpretation and action is based on mental models
- agents can have their own or shared mental models
- mental models can change; learning, adaptation, and co-evolution is
possible
- interconnections among agents, and systems embedded within systems
- system behavior emerges from the interaction among agents
- action by one agent changes the context for others
- the system can exhibit novel behavior
- the system is non-linear; small inputs can lead to major outcome swings
- system behavior is fundamentally unpredictable at the detail level
- broad-brush prediction of system behavior is sometimes possible
- order is an inherent property of the system, it need not be imposed
creative emergence has its best chance to appear when
there is a little (but not too
much) disagreement and uncertainty
|
The Stock Market: An Example of a Complex Adaptive
System.
The stock market is a good illustration of these properties
of a CAS. Buyers, sellers, companies, and
regulators each have their own mental models and are free to take many different actions. The specific
actions of each agent are somewhat unpredictable, and can often be construed as illogical by other agents
observing the action. Logical or not, each action changes the environment that others within the system
face. These others may take their own actions, which in turn further changes the environment. The detailed
movements of the system (whether the market is up or down today and by how much) is fundamentally
unpredictable. Furthermore, relatively small things, like the off-hand remarks of the Federal Reserve
Chairman, can have a large impact on the market; there is non-linearity in the system. However, despite
what seems to be total chaos, there is an underlying order that allows us to make generally true statements
about the system (this is the basis of both the fundamentals and technical analysis approaches to the
stock
market). Finally, no one "controls" the stock market. Rather, the stock market "happens;"
it creates its own
unique behavior every day.
Most organizational systems are a CAS. Substituting terms
such as employees, co- workers, bosses,
outcomes, performance, and so on into the stock market illustration above yields a pretty good description
of what goes on every day in most organizations. Try this substitution yourself and see if it doesn't
resonate with your experience in organizations. This is referred to as "sense-making;" when
the emerging
understanding of complex adaptive systems helps people make sense of what in the past has seemed a
sometimes chaotic and nonsensical world.6
Some Emerging Principles of Complexity Management
Our study of the science of complex adaptive systems
and our work with organizations has led us to
propose some principles of management that are consistent with an understanding of organizations as
CAS
(see figure 1). In the spirit of the subject matter, there is nothing sacred or permanent about this
list.
However, these principles do begin to give us a new way of thinking about and approaching our roles
as
leaders in organizations.
We are not the first to propose such a list.7 Our intent
here is to capture practical principles that emerge
from the science of complexity in language that resonates with management issues. Furthermore, astute
readers will also observe that our list of principles, and CAS theory itself, has much in common with
general
systems thinking, the learning organization, total quality, empowerment, gestalt theory, organizational
development and other approaches. It has much in common with these, but it is not any of these. CAS
theory clarifies and pulls together lots of good thinking from the past. An understanding of CAS is
an
understanding of how things work in the real world. That others in the past have also understood these
things and put them into various contextual frames should not be surprising. An understanding of CAS
simply provides a broader, more fundamental, potentially unifying framework for these ideas.
Figure 1: Nine, Emerging, and Connected Organizational
and Leadership Principles from the Study of
Complex Adaptive Systems
|
Principle
(shorthand)
|
Full statement
of principle
|
Further explanation or
contrast to the
traditional approach
|
|
1. Complexity lens
|
View your system through
the lens of complexity...
|
rather than the metaphor of
a machine or a military
organization.
|
|
2. Good enough vision
|
Build a good enough
vision and provide
minimum specifications...
|
rather than trying to plan
out every little detail.
|
|
3. Clockware/ swarmware
|
When life is far from
certain, lead from the
edge, with clockware and
swarmware in tandem...
|
that is, balance data and
intuition, planning and
acting, safety and risk,
giving due honor to each.
|
|
4. Tune to the edge
|
Tune your place to the
edge by fostering the
"right" degree of:
information flow, diversity
and difference,
connections inside and
outside the organization,
power differential, and
anxiety...
|
instead of controlling
information, forcing
agreement, dealing
separately with
contentious groups,
working systematically
down all the layers of the
hierarchy in sequence, and
seeking comfort.
|
|
5. Paradox
|
Uncover and work
paradox and tension...
|
rather than shying away
from them as if they were
unnatural.
|
|
6. Multiple actions
|
Go for multiple actions at
the fringes, let direction
arise...
|
rather than believing that
you must be "sure' before
you proceed with anything.
|
|
7. Shadow system
|
Listen to the shadow
system..
|
that is, realize that informal
relationships, gossip,
rumor, and hallway
conversations, contribute
significantly to agents'
mental models and
subsequent actions.
|
|
8. Chunking
|
Grow complex systems by
chunking...
|
that is, allow complex
systems to emerge out of
the links among simple
systems that work well and
are capable of operating
independently.
|
|
9. Tit-for-tat
|
Nice, forgiving, tough,
and clear people finish
first...
|
so, balance cooperation
and competition via the tit-
for-tat strategy.
|
1. View your system through the lens of complexity
(rather than the metaphor of a machine or a military
organization). As we have pointed out, the predominant metaphor in use in organizations today is
that of a
machine. Almost equally popular is the metaphor of a military operation. If an organization is a machine,
then we just need to specify the parts well, and make sure that each part does its part. If an organization
is a
military operation, then command, control, and communication needs to be hierarchical; survival is key;
and
sacrificial heroes are desired (although no one really wants to be one themselves). Most of today's
organizational artifacts- job descriptions, "rank and file" employees, turf battles, strategic
plans and so on-
emerge from these largely unexpressed and undiscussed metaphors. If you buy into these metaphors, then
the traditional actions of management make sense and should work.
The basic problem with these metaphors when applied to
a CAS is that they ignore the individuality of
agents and the interaction effects among agents. Or worse, they simply assume that all this can be tightly
controlled through better (read: more) specification. While there are many situations where the machine
and
military metaphors might be useful-for example, routine surgical processes in the health care organizations
we worked with-there are also many situations where these metaphors are grossly inadequate. When we
"view our system through the lens of complexity" we are taking on a new metaphor-that of a
CAS-and,
therefore, are using a different model to determine what makes sense to do as leaders.
Viewing the world through the complexity lens has been
a marvelously stress- reducing experience for the
health care leaders that we have worked with over the past few years. Many have come to see that the
massive sea-changes that they have experienced and agonized over recently-for example, the failed Clinton
health care reform plan, the rise of managed care, the AIDS epidemic-are natural phenomena in a complex
adaptive system. Such things will happen again, each will leave its mark on the health care system,
predicting when and where the next one will come is futile, learning to be flexible and adaptable is
the only
sustainable leadership strategy.
The view through the complexity lens need not only be
of very large scale systems. For example,
Muhlenberg Regional Medical Center knew that its biggest community relations problem was in its
Emergency Room (ER). Hospital CEO John Kopicki and VP Mary Anne Keyes knew that the traditional
approach was to develop a plan (they also toyed with the idea of launching a reengineering effort),
and
then use their organizational weight to see to it that everyone followed the plan. The complexity lens
suggested, however, that a "good enough vision" and "minimum specifications" (described
in principle two
below); along with interaction among the ER staff and the willingness to hold the creative anxiety of
not
being able to say exactly what ought to be done (described in principle four below) might lead to better
results than the traditional management or reengineering approaches. "The idea that the ER staff
could
determine for themselves what they would do generated a burst of enthusiasm," notes Kopicki. Starting
without a master plan, "...they tried a variety of innovations, kept what worked, and threw out
what didn't.
Within six months, they had improved customer satisfaction scores by 67 percent. That's unheard of.
No
one ever created that level of improvement in only six months. With that kind of success under our belts,
I've been leading the hospital towards a culture where this kind of self-organization is the way we
do things.
We see more examples of it working all the time."
|
Sidebar: Put On The Lens Of Complexity at Your Next
Meeting
The use of "team metaphors" is a quick way
to try on the lens of complexity at the
next meeting of your management group.
"Team" is an over-used and under-defined work
in current organizational jargon.
Everyone is forming teams and everyone knows them need to be a good "team
player" in order to be successful. But there are many, diverse images (mental
models) of a good team and how it operates. successful team behavior is very
different when one is on a basketball team (where fluid flow is valued) , versus a
baseball team (where roles are very clearly defined), versus a community theater
group (where all roles are important but some get more visibility than others),
versus the NASA space shuttle team (where technical expertise and detailed
planning are key). In general, it is not a good assumption to imagine that everyone
in a complex adaptive system (CAS) has the same mental picture of how they
should interact on a "team." Explicit discussion is very valuable.
After about 10 minutes, call time and ask each pair to
post and describe their
images. keep the discussion rich and safe for everyone. Their is no need to come
to consensus, there is no "right" answer. The point is simply to notice the
different mental models and to come to a deeper appreciation for how those
models might impact the evolving CAS of your team.
|
2. Build a good enough vision and provide minimum
specifications (rather than trying to plan out every
little detail). Since the behavior of a CAS emerges from the interaction among the agents, and since
the
detailed behavior of the system is fundamentally unpredictable, it does little good to spend all the
time that
most organizations spend in detailed planning. Most organizational leaders have had the experience of
participating in very detailed planning, only to find that assumptions and inputs must be changed almost
immediately after the plan is finalized. Complexity science suggests that we would be better off with
minimum specifications and general senses of direction, and then allow appropriate autonomy for
individuals to self-organize and adapt as time goes by.
The science behind this principle traces it roots back
to the "Boids" computer simulation, developed in 1987
by Craig Reynolds (and available on many Internet software bulletin boards).8 The simulation consists
of a
collection of autonomous agents-the boids-placed in a environment with obstacles. In addition to the
basic
laws of physics, each agent follows three simple rules: (1) try to maintain a minimum distance from
all other
boids and objects, (2) try to match speed with neighboring boids, and (3) try to move toward the center
of
mass of the boids in your neighborhood. Remarkably, when the simulation is run, the boids exhibit the
very
life-like behavior of flying in flocks around the objects on the screen. They "flock," a complex
behavior
pattern, even though there is no rule explicitly telling them to do so.9 While this does not prove that
real
birds use these simple rules, it does show that simple rules-minimum specifications-can lead to complex
behaviors. These complex behaviors emerge from the interactions among agents, rather than being imposed
upon the CAS by an outside agent or an explicit, detailed description.
In contrast, we often over-specify things when designing
or planning new activities in our organizations.
This follows from the paradigm of "organization as a machine." If you are designing a machine,
you had
better think of everything, because the machine cannot think for itself. Of course, in some cases,
organizations do act enough like machines to justify selected use of this metaphor. For example, if
I am
having my gall bladder removed, I would like the surgical team to operate like a precision machine;
save that
emerging, creative behavior for another time! Maximum specifications and the elimination of variation
might
be appropriate in such situations.
Most of the time, however, organizations are not machines;
they are complex adaptive systems. The key
learning from the simulations is that in the case of a CAS, minimum specifications and purposeful variation
are the way to go.
This principle would suggest, for example, that intricate
strategic plans be replaced by simple documents
that describe the general direction that the organization is pursuing and a few basic principles for
how the
organization should get there. The rest is left to the flexibility, adaptability, and creativity of
the system as
the context continually changes. This, of course, is a frightening thought for leaders classically trained
in
the machine and military metaphors. But the key questions are: Are these traditional metaphors working
for
us today? Are we able today to lay out detailed plans and then 'just do it' with a guaranteed outcome?
If
not, do we really think that planning harder will be any better?
The quintessential organizational example of this principle
of good enough vision and minimum
specifications is the credit-card company, VISA International. Despite its $1 trillion annual sales
volume and
roughly half a billion clients, few people could tell you where it is headquartered or how it is governed.
It's
founding CEO, Dee Hock describes it as a nonstock, for-profit membership corporation in which members
(typically, banks that issue the VISA cards) cooperate intensely "in a narrow band of activity
essential to
the success of the whole" (for example, the graphic layout of the card and common clearinghouse
operations), while competing fiercely and innovatively in all else (including going after each other's
customers!).10 This blend of minimum specifications in the essential areas of cooperation, and complete
freedom for creative energy in all else, has allowed VISA to grow 10,000% since 1970 despite the incredibly
complex worldwide system of different currencies, customs, legal systems and the like. "It was
beyond the
power of reason to design an organization to deal with such complexity," Hock explains. "The
organization
had to be based on biological concepts to evolve, in effect, to invent and organize itself."
Health care organizations are traditionally quite rule
bound. Because there are many legitimate industry
regulations that govern who can do what and how, many staff members in health care organizations assume
that everything must be done the way it has been done in order to satisfy legal requirements. So the
concepts of good enough vision and minimum specifications are both freeing and scary to the health care
leaders we worked with. The results for the risk takers, however, have been good. For example, Mary
Anne
Keyes (the Muhlenberg Medical Center VP we met in an earlier example) assembled a "little group
of
doctors and nurses" to simplify the hospital's admission process and gave them just one simple
specification: "all admission work must be done within an hour of the patient coming to the hospital."
All
other previously sacred cows were open to the group's creativity. The group created the Express
Admission process that is such a hit with patients and doctors that 400 hospitals from around the country
have asked to come to learn about it.
In a similar vein, Linda Rusch, a VP at Hunterdon Medical
Center, asked two nurse mangers to work with
the staff nurses to transform their units into "humanistic healing environments." "That's
all," Rusch tells us,
"I'm convinced that they will create two units that are both very very customer-service oriented
and good
places to heal." In another aspect of the hospital's mission, community health, Rusch explains
that after she
laid out a few minimum specifications regarding partnerships and the community, "the next thing
I know, I
hear about these nursing units that are collaborating in all these different projects with the outside
public."
In most health care institutions, true to the classic military organization metaphor, it is someone's
job to
coordinate community affairs. In many cases that person spends a great deal of time trying to "motivate"
staff to get involved. This does not seem to be a problem anymore at Hunterdon Medical Center; nor at
the
other organizations in the VHA group who have made similar progress.11
Good enough vision and minimum specifications are also
powerful concepts in regard to strategic planning.
For example, the Institute for Healthcare Improvement, a non-profit organization in Boston, by-passed
the
classic MBA approach in its efforts to build its international activities. Instead, the organization's
board
adopted 8 simple principles such as: "we should only work in countries where there is a clear aim
to
improve" and "our international collaborations must always be a two-way street of learning."
These
minimum specifications, along with diverse efforts at building information flow (see principle number
four in
a later section), comprise the organization's ever emerging "plan" for international activities.
Because of this
flexibility, the organization was able to respond quickly to requests from local leaders in Sweden to
begin a
series of improvement efforts spurred by the recent Dagmar Agreement in that country's parliament that
mandates reductions in waiting lists in the health service. Such a development might never have been
predicted had the organization used a more traditional approach to strategic planning; the opportunity
would have been missed.
3. When life is far from certain, lead from the edge,
with clockware and swarmware in tandem (that is,
balance data and intuition, planning and acting, safety and risk, giving due honor to each). "Clockware"
is
a term that describes the management processes we all know that involve operating the core production
processes of the organization in a manner which is rational, planned, standardized, repeatable, controlled,
and measured. In contrast, "swarmware" are management processes that explore new possibilities
through
experimentation, trials, autonomy, freedom, intuition, and working at the edge of knowledge and experience.
Good enough vision, minimum specifications, and metaphor are examples of swarmware that we have
already seen. The idea is to say just enough to paint a picture or describe the absolute boundaries,
and
then let the people in the CAS become active in trying whatever they think might work.
In an informed approach to complexity, it is not a question
of saying that one is good and the other is bad.
The issue is about finding an appropriate mix for a given situation. Where the world is certain and
their is a
high level of agreement among agents (for example, the need for consistent variable names and
programming language syntax in a large software system, or the activities in the operating room during
a
routine surgery) clockware is appropriate. In a clockware situation, agents give up some of their freedom
and mental models in order to accomplish something they have agreed upon collectively. The CAS displays
less emergent, creative behavior, and begins to act more like a machine. There is nothing wrong with
this.
However, where the world is far from certainty and agreement
("near the edge of chaos") swarmware is
needed with its adaptability, openness to new learning, and flexibility. Swarmware is also needed in
situations where the old clockware processes are no longer adequate for accomplishing the purpose, or
in
situations where the purpose has changed, or in situations where creativity is desirable for its own
sake.
Linda Rusch at the Hunterdon Medical Center is working
with her staff to move fluidly between clockware
routines and swarmware activities as the level of agreement and certainty varies in the situation. She
laughs, "My staff go around saying, "we're swarming now!'"
James Taylor, the new CEO at the University of Louisville
Hospital, convinced his board to save the
$500,000 they were going to spend on consultants and various analyses to develop a strategic plan.
Instead, he argued, lets "just get on with addressing the strategic issues themselves." He
astutely points
out that there is a strong tendency in most organizations to "get some experts, plan it, and avoid
talking
about what the real issues are." Taylor sums up the essence of the swarming we have seen in the
organizations we work with, "It's a more pragmatic, action orientation that says here are the strategic
issues
so let's address them the best we can. Let's keep our ideas open... Let's create an organizational
environment where we can learn from our actions." While this might sound like an abdication of
leadership
to those steeped in the organization-as-machine metaphor, the new science suggests that it is the very
essence of leadership in complex adaptive systems.
4. Tune your place to the edge by fostering the right
degree of: information flow, diversity and difference,
connections inside and outside the organization, power differential, and anxiety (instead of controlling
information, forcing agreement, dealing separately with contentious groups, working systematically down
all the layers of the hierarchy in sequence, and seeking comfort). Theoretical studies of complex
adaptive
systems suggest that creative self-organization occurs when there is just enough information flow,
diversity, connectivity, power differential, and anxiety among the agents. Too much of any of these
can
lead to chaotic system behavior; too little and the system remains stuck in a pattern of behavior.12
Complexity researcher Stuart Kauffman provides a simple,
visual illustration that gives some insight into the
science here.13 Consider a collection of a hundred or more buttons spread out on a table surface. Now,
select two buttons at random and tie them together. Continue this selection and tying process, each
time
lifting the thread after you have made the tie to see how long a string of buttons you can pick up.
For a
while, additional connectivity does not lead to creative self-organization; each time you pick up the
newly
tied thread there are only two or three buttons attached. At some point of additional connectivity,
however,
something seemingly magical happens. You pick up the thread and 5, 8, or 10 buttons are attached in
an
intricate pattern among the sea of buttons on the table.14 This creative self- organization phenomena
continues for a while until we pass the edge of chaos and get into chaos itself. With too much connectivity
among the buttons, the thread gets all tangled up. You can no longer see a pattern among the sea of
buttons; all you see is a mess of thread.
Of course, the trick in a human CAS lies in gauging the
"right" amount of information flow, diversity,
connectivity, power differential, and anxiety among the agents. Since the predominant metaphors of
organizational life are those of a machine and military operation, most organizations today have too
little
information flow and diversity, and too much power differential. The degree of connectivity and anxiety
can
go either way. This is a general observation which, of course, could be different in any specific context.
If
you are in a CAS, you will have your own mental model about such things, as will the other agents in
the
system.
Richard Weinberg, VP of Network Development at Atlantic
Health Systems, has used this concept of
"tuning to the edge," along with good enough vision and minimum specifications, in his work
with
physicians who can often become embroiled in turf battles. For example, recent technological advances
have made it possible for radiologists and cardiologists to reshape damaged arteries, something that
used
to require the skills of a vascular surgeon. In most places, a senior hospital administrator would be
put in
the unenviable position of representing the hospital's interests, while serving as negotiator and referee
among these powerful constituencies. Weinberg's approach instead involves "convening a group with
representatives of all three specialties" (increasing diversity, connections among agents, and
anxiety);
giving them honest information about the hospital's resources and requirements (increasing information
flow and tuning the power differential); "asking them to develop a plan" (in the end, decreasing
diversity
and power differential); and "telling them that the hospital won't invest in the procedure until
they have
come up with such a plan" (increasing power differential and anxiety). Weinberg cites this approach
as
leading to many, creative, successful, collaborative relationships with physician groups at a time when
many health care organizations report nothing but contention.
The international strategic plan for the Institute for
Healthcare Improvement that we mentioned earlier is
another example of the use of this "tuning" principle. The Institute has firm financial goals
but no firm
operational plans for its international efforts (increasing anxiety). Because of this anxiety, it constantly
solicits information inputs from its contacts in healthcare organizations around the world, using innovative
approaches involving the Internet (increasing connections, information flow, and diversity). At the
same
time, it makes known the various methods for improvement that it has available to offer (tuning the
power
differential; where here, knowledge is power). However, the Institute does not push its way onto the
international health care scene; preferring instead to wait to be invited to help by local leaders in
a given
country (tuning the power differential; where here, control is power).
A third example of "tuning" involves forming
what Lane and Maxfield call "generative relationships."15 A
generative relationship is one that generates outcomes that are greater than the simple sum of the individual
efforts of the parties working alone. Lane and Maxfield suggest that generative relationships are necessary
to deal with a world characterized by "cascades of rapid change, perpetual novelty, and ambiguity."
Jim Dwyer, VP for Medical Affairs at Memorial Hospital
of Burlington County, provides an illustration of
this. "In the past," Dwyer says, "if I were trying to develop a partnership with another
physician group, I'd
try to bring people around to the right way-that is, my way-of seeing things. With generative relationships,
on the other hand, I begin by showing them what we could be doing together. Then we define what we are
both comfortable with and let the relationship grow from there. Our relationship doesn't have to appear
all at
once. It's a lot more comfortable for everyone if we let it emerge, let it generate itself." In
a health care
environment where size and cash position seem to be temporarily dominating the scene, Dwyer's approach
is to "serve the community by creating relationships that allow partnering organizations to benefit
mutually,
yet retain their identities."
Since the detailed behavior of a CAS is fundamentally
unpredictable, there is no way to analyze your way to
an answer about the proper amount of information flow, diversity, connections inside and outside the
organization, power differential, and anxiety to sustain among the agents. You can have more or less
correct
intuitions, and some sense of general direction, but that's inherently the best you can do. You'll just
have to
try tuning up or down the various factors and reflect on what happens.
Reflection is, therefore, a key skill for anyone in a
CAS. Good "leaders" in a CAS lead not by telling people
what to do; rather they lead by being open to experimentation with the above factors, followed-up by
thoughtful and honest reflection on what happens. For example, James Taylor, the University of Louisville
Hospital CEO in our learning group, is practicing reflection when he advises acting on strategic issues,
and
creating an organizational environment where we can learn from those actions.
5. Uncover and work paradox and tension (rather than
shying away from them as if they were unnatural).
Because the behavior of a CAS emerges from the interaction among agents and because of non-linear
effects, "weird" stuff seems to happen. Of course, it is only weird because we do not yet
have a way to
understand it
.
In a CAS, creativity and innovation have the best chance
to emerge precisely at the point of greatest
tension and apparent irreconcilable differences. Rather than smoothing over these differences-the typical
leadership intuition from the machine and military metaphors-we should focus on them and seek a new
way
forward. So, for example, one group wants to hold on to the status quo while another wants radical change.
Mix them into a single group and take on the challenge of finding a "radical way to hold on to
the status
quo." This is a statement of a paradox; it makes no sense according to the prevailing mental models.
However, working on it sincerely places the group at the "edge of chaos" where creativity
is a heightened
possibility.
Zimmerman,16 Goldstein,17 and Morgan18 are three leading
complexity management theorists who each
provide specific techniques and metaphors for getting at these points of paradox and tension in
organizations. For example, Zimmerman describes "wicked questions" at the Canadian metals
distributor
Fedmet. At a strategy planning retreat, the senior management team spent most of the day openly
discussing questions of paradox such as, "Are we really ready to put responsibility for the work
on the
shoulders of the people who do the work?'' and "Do our body language and our everyday actions reflect
what we write in our vision and values statements?" We have all been there before and we all know
what
the "right" public answer is to such questions: "Well, of course, don't be silly."
But we also all know that
these questions and others like them carry embedded in them the seeds of paradox that often bring
organizational progress to a grinding and surprising halt (only surprising to those who hold the machine
and military metaphors).
6. Go for multiple actions at the fringes, let direction
arise (rather than believing that you must be "sure"
before you proceed with anything). As we have already noted, in a CAS it does little good to plan
the
details. You can never know exactly what will happen until you do it. So, allowing the flexibility of
multiple
approaches is a very reasonable thing to do. Of course, such a flexible approach is unreasonable when
we
view the situation through the metaphor of a machine or military organization. A machine can only work
one
way, and an old-style military organization must follow procedures and regulations.
The science that supports this principle of CAS behavior
comes primarily from the study of gene pools in
evolutionary biology. Ackley points outs that "Researchers have shown clearly and unequivocally
how
populations of organisms that are learning (that is, exploring their fitness possibilities by changing
behavior) evolve faster than populations that are not learning."19 We do not think it strains the
metaphor
here to suggest that our managerial instincts to drive for organizational consensus around a single
option
might be equivalent to inbreeding in a gene pool. And we all know the kinds of dysfunction that inbreeding
in nature can spawn. We are personally struck by the fact that even though the words "organization"
and
"organism" have a common root, we have learned to think about them in such remarkably different
ways.
The "fringes" that we are referring to here
are those issues that are far from the zone of certainty and
agreement. Recall that we pointed out that it was not a question of the machine metaphor being wrong
and
the CAS metaphor being right, nor is it about throwing out clockware and replacing it with swarmware.
Neither approach is inherently right or wrong; but either approach can be inappropriate and ineffective
in a
given context. The leadership skill lies in the intuition to know which approach is needed in the context
one
is in. The degree of certainty and agreement is good guide.
However, when we do find ourselves in situations far
from certainty and agreement, the management advice
contained in this principle is to quit agonizing over it, quit trying to analyze it to certainty. Try
several small
experiments, reflect carefully on what happens, and gradually shift time and attention toward those
things
that seem to be working the best (that is, "let direction arise"). These multiple actions
at the fringes also
serve the purpose of providing us with additional insights about the larger systems that every system
is
inevitably buried within.
A concrete example of this principle is the healthcare
organization that is trying to come up with a new
financial incentive plan for associated physicians. There are many options and there are success and
failure
stories in the industry for each one. Therefore, we are far from certainty and agreement. Rather than
meeting
endlessly over it trying to pick the "right" approach, experiment with several approaches.
See what
happens, see what seems to work and in what context. Over time, you may find a "right" way
for you, or
you may find several "right" ways.
7. Listen to the shadow system (that is, realize that
informal relationships, gossip, rumor, and hallway
conversations contribute significantly to agents' mental models and subsequent actions). Complexity
theorist Ralph Stacey points out that every organization actually consists of two organizations: the
legitimate and shadow systems; and that everyone in the organization is part of both.20 The legitimate
system consists of the formal hierarchy, rules, and communications patterns in the organization. The
shadow organization lies behind the scenes. It consists of hallway conversation, the "grapevine,"
the
"rumor mill," and the informal procedures for getting things done. Most traditional management
theory
either ignores the shadow system, or speaks of it as something leaders must battle against (as in,
"overcome resistance to change;" it's that military metaphor again).
Stacey further points out that because the shadow system
harbors such diversity of thought and approach,
it is often the place where much of the creativity resides within the organization. While the legitimate
system is often focused on procedures, routines, and the like, the shadow system has few rules and
constraints. The diversity, tension, and paradox of these two organizations that co-exist within one
can be a
great source of innovation if leaders could just learn to listen to rather than battle against the shadow.
When we see our organizations as CAS, we realize that
the shadow system is just a natural part of the larger
system. It is simply more interconnections among agents; often stronger interconnections than those
in the
legitimate system. Leaders who lead from an understanding of CAS, will not have a need to discredit,
agonize over, or combat the shadow systems in their organizations. Rather, they will recognize and listen
to
the shadow organization, using the interconnections it represents as another avenue for tuning information
flow, diversity of opinion, anxiety, and power differential (see principle four).
Jim Dwyer at Memorial Hospital of Burlington County learned
from the shadow system associated with his
organization's formal quality improvement efforts. "[In order to screen out projects of low benefit]
We had a
formal mechanism for approving quality improvement projects," Dwyer notes, but "the process
became so
difficult that people were losing enthusiasm over worthwhile projects." Dwyer goes on to tell how
he
became involved in an ad-hoc improvement project on the process of delivering anti-coagulants; a project
that was cooked up by a group of doctors and nurses talking in the cafeteria one day. As a result of
the
success of this effort outside the formal improvement structure, Dwyer and other senior leaders "basically
decided to turn the structure upside-down. We created lots of opportunities for people to generate
projects," Dwyer explains, "and restructured our quality program to support them." He
concludes, "We
expect we'll see a lot more important projects because we have found a way to tap the shadow system."
We believe that Dwyer's experience is typical of many
experiences associated with formal improvement
structures in many industries. Recognizing that the shadow system exists, giving up some control, and
learning to tap the energy in the shadow are key recommendations we would make to leaders in any
industry who believe that their organization's improvement efforts are floundering.
8. Grow complex systems by chunking (that is, allow
complex systems to emerge out of the links among
simple systems that work well and are capable of operating independently). Complex systems are...
well,
complex. They are not easily understood nor built in detail from the ground up. "Chunking"
simply means
that a good approach to building complex systems is to start small. Experiment to get pieces that work,
and
then link the pieces together. Of course, when you make the links, be aware that new interconnections
may
bring about unpredicted, emerging behaviors.
This principle is the basis upon which genetic evolution
proceeds.21 Building blocks of organism
functionality (for example, webbed feet on a bird) develop and are combined through cross-over of genetic
material with other bits of functionality (for example, an oversized bill suitable for easily scooping
fish out of
the water) to form increasingly complex organisms (a pelican). The "good enough" genetic combinations
may survive and are then available as building blocks for future combinations. The UNIX computer
operating system is another good example of an ever-evolving complex system that was built up from
chunks. The basic-and at the time it was introduced, revolutionary-principle behind the UNIX system
is that
software functions should be small, simple, stand-alone bits of code that do only one thing well, embedded
in an environment that makes it very easy for each such function to pass its output on to another function
for further processing.
Applying this principle to team-building in a mid-sized
organization, for example, would suggests that
leaders should look for and support small natural teams. We might provide coaching and training for
these
teams. Then, when these teams are functioning well, look for ways to get the teams to work together
and
involve others. These new links may result in weird behavior; with a CAS, this is to be expected. The
leaders should be open to doing some adapting of their own. Rather than insisting on pressing forward
with
the training, groundrules, or procedures that worked so well in the first teams, the leaders should
understand that the interconnections among teams has resulted in a fundamentally new system that may
need new approaches.
Continual reflection and learning are key in building
complex systems. You cannot reflect on anything until
you do something. So start small, but do start.
We have already seen several examples of this principle.
James Taylor is using chunking at the University
of Louisville Hospital when he focuses the organization on getting started working on strategic issues
as
they come up, rather than trying to figure out the whole system in a grand strategic plan. Hunterdon
Medical Center and Chilton Memorial Hospital are also using the concept of chunking in their community
health efforts. Instead of developing an overall community health program, they provide opportunities
for
small groups of hospital staff and community members to come together where mutual interest lies (that
is,
in generative relationships). The senior leaders then actively nurture these small efforts, and link
them
flexibly in with other such efforts. The Institute for Healthcare Improvement has similarly chosen a
chunking approach in its international work. After starting up a successful effort in Sweden, it now
appears
that it may be possible to start related efforts in other Scandinavian countries. Each of these efforts
will
necessarily have unique features; but as these new efforts come on line, establishing links across countries
may lead to further possibilities (increasing the information flow and diversity, while decreasing the
power
differential).
9. Nice, forgiving, tough, and clear people finish
first (so, balance cooperation and competition via the tit-
for-tat strategy). Throughout this list of principles we have seen the theme of balance as a key
to
successful outcomes in a CAS. Here in this principle, we are talking about the balance between cooperation
and competition among agents.
The basis for this principle comes primarily from the
work of political scientist Robert Axelrod in his studies
of the famous "prisoner's dilemma" in a branch of mathematics called game theory.22 The dilemma
involves
two prisoners being held separately for interrogation by police for a crime they jointly committed.
Each
prisoner is offered a choice: he can turn on his partner and become an informant, or remain silent.
If both
remain silent (that is, they cooperate with one another), they can both go free because the police do
not
have enough evidence to get a conviction without a confession. The police, however, cleverly offer an
incentive. If one of them becomes an informant (that is, he competes with his partner), that prisoner
will be
granted immunity from prosecution and will be given a very nice reward to live out his days in comfort.
The
partner will get the maximum sentence and be assessed a fine. Of course, if both prisoners turn informant
(that is, both choose to compete), then both will get the maximum sentence and neither gets a reward.
The
dilemma is a classic struggle between the virtues of cooperation and competition in an environment of
imperfect information. This "game" is played out for real in organizations in various forms
that we call:
negotiation, partnering, collaborating, forming strategic alliances, and so on.
In the 1970s, Axelrod had the idea to study various strategies
for approaching the Prisoner's Dilemma
through a computerized tournament. Strategies would be paired up in many different combinations and
would play out the game, not once, but 200 times. This is a more realistic simulation of what goes on
in real
relationships as the programs would have the chance to react to each other's strategies, and to learn
as they
went along. Fourteen programs were submitted, but astonishingly to Axelrod and his colleagues, the
simplest strategy of all took the prize in this complex contest. University of Toronto psychologist
Anatol
Rapoport's "Tit for Tat" program started out by cooperating on the first move, and then simply
did exactly
what the other program had done on the move before. The program was "nice" in the sense that
it would
never defect first. It was "tough" in the sense that it would punish uncooperative behavior
by competing
on the next move. It was "forgiving" in that it returned to cooperation once the other party
demonstrated
cooperation. And it was "clear" in the sense that it was very easy for the opposing programs
to figure out
exactly what it would do next. The morale: Nice, tough, forgiving, and clear people can finish first
in
cooperation-competition trade-off situations.
In his 1984 book, The Evolution of Cooperation, Axelrod
showed the profound nature of this simple
strategy in its application to all sorts of complex adaptive systems-trench warfare in WW1, politics,
and
fungus growth on rocks.23 Commenting on this strategy, Waldrop (1992) says "Consider the magical
fact
that competition can produce a very strong incentive for cooperation, as certain players forge alliances
and
symbiotic relationships with each other for mutual support. It happens at every level of and in every
kind of
complex adaptive system, from biology, to economics, to politics."24
From the complexity perspective then, a good leader would
be one who knows how to, and prefers to,
cooperate; but is also a very skillful competitor when provoked to competition (that is, a nice, forgiving,
tough, and clear person). Note that this strategy rejects both extremes as a singular strategy. While
much is
said these days about the importance of being cooperative and positive-thinking in business dealings,
the
always-cooperative leader may find his or her proverbial lunch is being eaten by others. Similarly,
while
sports and warrior metaphors are also popular in some leadership circles, the always-competitive leader
may
find himself or herself on the outside looking in as alliances are formed.
Conclusion
Our existing principles of leadership and management
in organizations are largely based on metaphors from
science that are hundreds of years old. It is time that we realized that science itself has largely
replaced
these metaphors with more accurate descriptions of what really happens in the world. Science is replacing
its old metaphors not so much because they were wrong, but because they only described simplistic
situations that progress has now moved us well beyond. Similarly, our organizations today are not the
simple machines that they were envisioned to be in the Industrial Revolution that saw the birth of scientific
management. Further, people today are no longer the compliant "cogs in the machine" that we
once
thought them to be. We have intuitively known these things for many years. Management innovations
such as learning organizations, total quality, empowerment, and so on were introduced to overcome the
increasingly visible failures of the simple organization-as-machine metaphor. Still, as we have pointed
out,
the metaphor remains and is strong.
The emerging study of complex adaptive systems gives
us a new lens through which we can now begin to
see a new type of "scientific management." This new scientific management resonates well with
more
modern, intuitive notions about what we must do to manage increasingly complex organizations today.
More importantly, the new thinking in science provides a consistent framework to pull together these
heretofore intuitive notions. Now, for example, advocates of open communications and empowerment can
claim the same firmness of ground that advocates of structure and control have been claiming exclusively.
Science can now say rather clearly that structure and control are great for simple, machine-like situations;
but things like open communication, diversity, and so on are needed in complex adaptive systems-like
those
in modern organizations. The new scientific management will, no doubt, revolutionize organizations in
the
coming decades much as the old scientific management changed the world in the early decades of this
century.
1. Thoughts
