"If you want to make God laugh, tell Him your
plans"
-Ida Davis, late grandmother of the author
Introduction: Planning in the New World of Complex
Systems.
Planning is considered a crucial responsibility for the
leaders of organizations. The common wisdom has it
that the higher-up in the hierarchy a leader is, the greater the time span is supposed to be covered
by
planning. Thus, a CEO is expected to be involved in planning that focuses on several years, even many
years, ahead. The sequence of planning typically goes like this: accurate forecasting; establishing
a vision;
planning for the vision; articulating the vision; implementing the plan; measuring the progress being
made
to achieve the vision; and, correcting the course if necessary. But, what assumptions underlie this
conception of planning and do they remain as pertinent as they once did in the face of the strange new
world of complex and nonlinear systems within which leaders must lead?
Consider the etymology of the word "plan":
it comes from the Latin "planus" meaning flat, as in our words
"plane" (a flat surface) and "plain" (the Plains). A "plan" is a projection
or map of a three dimensional
object (e.g., an airplane) onto a two dimension flat surface (e.g., the airplane s blueprint). The plan
then
offers a way to both survey all at once a dauntingly large or complicated object as well as a means
to peer
into the future by looking ahead on the plan s flat surface. But the flatness and static quality of
the plan
neglect not only the spatial third dimension but the temporal dimension of a system's evolution over
time as
well. This neglect is not a problem if the plan is of a simple, linear system. But, what recent research
into
complexity is showing is that our businesses or institutions are not simple or linear, they are better
thought
of as complex and nonlinear. As a result, the leadership role of planning needs to be rethought in the
light
of complexity research.
-
I propose in this article to reconceive planning in the
light of contemporary research in the
complexity sciences by sketching out three revised roles for planners in the complex, nonlinear,
and nonequilibrium world in which our businesses and institutions exist:
Planners as Nonlinear and Complex Map-makers
- Planners as Nonlinear and Complex Explorers
- Planners as Nonlinear and Complex Tricksters
These three roles are interrelated in the sense that
the planner as Trickster first needs to have Explored the
new terrain of the nonlinear and complex world which, in turn, demands that appropriate maps have been
made of this new geography. So, first we'll look at the new maps, then how to explore the new geography,
and finally, how to proceed within this new geography following these new maps.
Planning and The Geography of Predictability
Traditionally, successful planning was supposed to rest
on two interrelated achievements: accurate
prediction of the future combined with an implementation strategy carefully tailored to these predictions.
For instance, Ackerman (1982) claimed that successful organizational change resulted not only from an
"impact analysis" of how the planned change will specifically effect the organization's functions,
people,
and management systems, but the ability of planners to predict, ahead of time, at what pace this change
will
proceed! And, Zeira and Avedisian (1990) proposed a planning procedure based so primarily on the
accuracy of the initial forecast that success was supposed to altogether hinge on the initial assessment
of
the current status of the organization.
Figure 1: Planning as Cartography
Indeed, this linkage of planning with accurate prediction
runs deep in our classical scientific and
philosophical heritage. For example, Isaac Newton believed he had managed by means of his calculus to
unfailingly predict the future state of a system - all that was needed was an accurate measurement of
initial
conditions and the appropriate equations of motion (Ekeland, 1988). Linking effective planning to this
ideal
of predictability indeed sounds like a commendable endeavor for an organization. The only problem is
that
Newton s promise of predictability was for a world mainly conceived as linear, simple, and stable, whereas
complexity research is revealing a world composed of systems that are nonlinear, complex, and unstable.
In
such a new world, Newton's type of predictability can no longer reign supreme. Of course, this is true
not
only from a mathematical or physical point of view, for who can, in our tumultuous and unstable healthcare
environment, seriously entertain the belief that predicting the future is possible anymore (outside
of trivial
considerations of current trends)? Instead of a stable environment, instability is the name of the game:
shifts in the workforce; the unexpected rise of resistant bacteria and apparently new viruses; changes
in
healthcare financing and insurance; unexpected shifts in governmental regulations; the unprecedented
rise
and fall of for-profit ventures; technological innovations; demographic shifts in the marketplace; and
on
and on.
Furthermore, are prediction and accurate anticipation
really what's so crucial for organizational change
efforts? Dyers (1985), for example, in his studies of the planned change of corporate cultures, points
out
that in many cases significant changes were not planned, but were, instead, precipitated by unanticipated
financial shifts, crises, illnesses, and even deaths of leaders. And, Westley (1990) found that unexpected
changes, spontaneously accompanying planned change efforts, often had more lasting influence on an
organization than the original plans themselves.
A Limit to Unpredictability
Amidst all this talk about unpredictability, however,
an important point needs to be underscored. To be
sure in the wake of complexity research, there has been a great deal of brouhaha surrounding the newly
discovered unpredictability of complex systems which has been having a major impact on how we are now
thinking about our businesses and institutions. Some organizational theorists have even gone so far
as to
claim that such unpredictability obviates entirely the role of planning and visioning (a chief buzzword
of
leadership in the 1980's and early 90's). What's the point of planning if the future is totally uncertain?
All it
can be is to serve as a temporary illusion, something nice to strive for but a striving that is ultimately
in
vain.
To be sure, complex systems are unpredictable in ways
not previously considered. But it is simply not true
that they are not predicable at all. Instead, the world of complex, nonlinear, and nonequilibrium (or
far-from-
equilibrium) systems is a strange brew of anticipated and surprising events, continuous and emergent
phenomena, and stable and unstable features. To say they are totally unpredictable is as simplistic
as to
say they are as predictable as they were once thought to be. Rethinking the role of planning called-for
by
the recognition of organizations as complex systems demands then not only a sufficient grasp of what
makes them unpredictable, it equally requires those involved in corporate planning to understand in
what
ways this unpredictability is itself limited. Complex organizations are indeed predictable but in ways
not
previously considered. Therefore, a nonlinear and complex world requires a nonlinear and complex map,
and, accordingly, leaders as planners must practice a new style of cartography (see the geography of
the
new nonlinear and complex world in Figure 1 above).
Regions of Nonlinear Amplification: Loss of Information
and Unpredictability
Chaos
As is now well appreciated, one of the cornerstones of
the complexity revolution concerns nonlinearity.
According to the physicist J. Bruce West (1985), the success of linear reasoning formed the backbone
of
scientific models well into the mid-twentieth century. This linear perspective assumed a one- way, non-
reciprocal type of causality, a proportion between input and output, a negligible environmental influence
on
a system, and that systems would evolve predictably (as on the flat surface of the plan that was mentioned
above). On the other hand, discoveries of nonlinearity have radically challenged each of these
assumptions. We can see this by taking a look at one of the most startling types of newly discovered
nonlinearity, i.e., chaos.
Chaos presents one of the most startling demonstrations
of unpredictability in complex systems. Since
chaotic systems show a degree of unpredictability more extensive than that found generally in complex
systems, the recognition of bounds even to the unpredictability in chaotic systems will be applicable
even
more so to complex systems in general. The unpredictability of chaotic systems is the result of their
property of sensitive dependence on initial conditions (SIC) which exponentially magnifies small differences
or changes in initial conditions. This is the so-called Butterfly Effect where the tiny air currents
produced
by a butterfly flapping its wings in, say, Sierra Leone, can be hugely amplified leading to a thunderstorm
weeks later in Brazil. If such a tiny event as a butterfly flapping its wings could have such a huge
impact on
a system, and the number of such tiny events happening in a large complex system is so enormous, then
the predictability of future states in a chaotic system must be impossible. Indeed, mathematical theorems
have proven that the unpredictability of a chaotic system will always exceed capacity of the fastest
computer predicting future states of a chaotic system by calculations based on initial conditions (Ford,
1989).
A way to understand chaos' characteristic of SIC is to
first consider what initial conditions are and how
they are measured. An initial condition is simply the current state of a system when it is being assessed
or
measured. Measurements of the initial conditions of the weather, for example, may include air temperature
at
sea level, air temperature at higher elevations, wind speed, humidity, and so on. Of course, any
measurement at some initial point in time will strive to be as precise and accurate as possible. On
a graph,
this hoped-for, ideal precision of measurement of initial conditions would be captured by a clearly
distinct
point (see Figure 1 in Appendix B). But the fact is that every measurement of the initial conditions
of any
system will contain some degree of imprecision or inaccuracy because the measurers are fallible, the
measuring instruments are fallible and the measurement accuracy will always be limited. For example,
measurements of air temperature at sea level will only go as far as some specific decimal point: Fahrenheit
75.0093 degrees. The instrument just cannot go any further. But this means that the measurement when
displayed on a graph will never be an exact point, but will instead always occupy a region around a
point,
this region being equivalent to the amount of inaccuracy of the measurement (again, see Figure 1 in
Appendix A).
Unpredictability as the The Nonlinear Expansion
of Ignorance
Because there can never be a perfectly accurate measurement
or assessment of a system's initial condition,
there will always be something about the system that, at the time it is measured, remains unknown, in
other
words, a degree of ignorance or missing information about the system (Ford, 1989). Now the fact that
we
will always remain ignorant to some degree about a system does not present a problem for predictability
in a
linear system. The reason is that a linear system does not expand the amount of ignorance we have at
any
initial condition but simply keeps this ignorance approximately the same. That is, a small magnitude
of
ignorance or missing information to start-out with will merely stay the same because such systems are
not
sensitive to initial conditions. In other words they do not amplify the initial imprecision. The linearity
of the
system guarantees that the amount of what we don't know about the system will remain pretty much the
same.
However, in a strongly nonlinear system such as found
in chaos, the ignorance or missing information
associated with imprecision of measurement or assessment will be "blown-up" by the system
and to such a
degree that our ignorance of the system will always exceeds our ability to predict future states of
the
system. Chaotic systems, therefore, are intractably unpredictable, at least as far as future states
of the
system are concerned (See Appendix A Figure 2). In chaotic systems, we become more and more ignorant
as we project the current state into the future. That is, our projection of the future will have to
be extremely
general and imprecise. Consequently, trying to predict the future state of a chaotic system based on
measurements of the initial condition is largely an exercise in futility. All it can yield is a very
large and
murky space of possibilities for future states of the system.
From the point of view of a planner trying to prognosticate
the future, each future state of an organization
becomes farther and farther removed from the predictions based on the initial conditions. The point
is not
simply the obvious fact that we can't know everything, instead, it is that chaos exponentially amplifies
every small lack of information at our disposal. In such systems, there can be no exact solution, no
short cut
to tell ahead of time a future state - you just have to watch as the system evolves. According to the
computer scientist Ed Fredkin:
"There is no way to knowing the answer to some question
[a nonlinear one] any faster than what's going
on...(even God) cannot know the answer to the question any faster than doing it" (quoted in Wright,
1990,
p. 68).
Whereas the assumption of linearity in traditional planning
presents a picture of system evolution as if it
were proceeding on a flat plane where there is a proportionality between input and output with no surprises
ahead, in nonlinear amplification like in chaos, a small input is magnified into a very large output.
This
suggests that nonlinearity deforms the surface so much that our line of vision is obscured. In regard
to a
business or institution characterized by some degree of strong nonlinearity, any initial assessment
will not
be of much help in forecasting future states of the system. This holds true for assessments of the
environment as well. No matter how sophisticated the tools for measuring or assessing environmental
variables, if the environment is characterized by strong nonlinearities, the future will remain opaque.
But all this talk about the expansion of our ignorance
and the ensuing unpredictability in strongly nonlinear
systems is not the whole truth being revealed in complexity research. Indeed, there are regions on the
nonlinear and complex geography that are indeed unpredictable, but the good news is that the more we
learn about nonlinear systems the more we know about limits to regions of unpredictability. Let's turn
to
some of the ways nonlinear, complex systems are proving to be predictable after all.
Decreasing Our Ignorance in Nonlinear Systems:
Recognizing the Identity of an Organization
The unpredictability found in nonlinear, complex systems
has a seldom discussed property that can
actually lead to a decrease of our ignorance of them. Exploiting this property on the part of leader/planners
can help facilitate their shift from thinking of planning as a linear to a nonlinear activity. Instead
of being
linear prognosticators, leader/planners can be facilitators of a greater recognition of an organization's
identity, i.e., its core competencies, strengths and limitations, and unique perspective on the goods
or
services it makes or delivers. This property has to do with approaching the ongoing measurement of a
chaotic or complex system in terms of a gain in experimental information (Abraham & Shaw, 1984;
Shaw,
1981). This gain in experimental information, or in other words, decrease in ignorance, derives from
an
ongoing comparison of current measurements with ones conducted in the past, i.e., each new assessment
of initial conditions is compared with previous assessments of past initial conditions.
The gain in experimental information comes about by continually
remeasuring the system - we conduct the
same assessment at a later time (see Appendix A, Figure 3). We then compare the new measurement at the
new time with what believed to be the future state of the system based on projections from our previous
measurement at the initial time. But remember that our projection into the future based on the initial
measurement had to be extremely general and unable to pinpoint future states of the system since SIC
in the
chaotic system "blew-up" the small ignorance or missing information we had at the initial
measurement. But
notice that the ignorance of our new measurement or its missing information has not yet "blown-up"
and is,
therefore, much more precise than the projection based on the past measurements. This means that the
new
measurement has decreased the ignorance expressed in our earlier projection into the future. That is,
we
know more about the system at this current time than was available at the earlier time when we projected
into the future.
We can then take this current decrease in ignorance or
gain in experimental information flowing it
backwards to the earlier imprecision, ignorance, or missing information (see Appendix A, Figure 4).
This
backward flow, in turn, shrinks the earlier imprecision, the degree of our earlier ignorance about the
future
by increasing the amount of the amount of information available to the system even at the earlier time.
What's going on here is that by an ongoing measurement process and the comparison of these ongoing
measurements with earlier ones, the system is yielding more knowledge or information about itself, no
matter how much the nonlinear amplification in the system is making future states unpredictable. In
such a
way, a system, its observers and planners, can know more about itself, in terms of where it was before
than
it could have possibly known at the earlier time. Accordingly, a more precise knowledge of where it
is now,
i.e., its identity, yields potential greater knowledge of where it is heading.
A planner by conducting ongoing present assessments and
comparing them with earlier projections of the
future gains information and decreases ignorance about what the system really is at its core, i.e.,
its core
competencies (what specific operations, tendencies, propensities, and directions, practices, and skills
form
the essential identity and capacity of the organization). This shifts the role of planning, though,
into a
process of map-making, comparing temporal regions of a company's evolution to engender greater
knowledge of the geography of an organization's identity. This role for planning is different than merely
searching for trends since the focus is not on looking for trends occurring now and continuing into
the
future as much as it is in gaining information about where the organization was, and then continues
to be,
and will continue to be into the future. The planner here makes maps that connect past and present in
feedback loops of information, opening up vistas into the future. It may be that this gain of information
on a
chaotic attractor is one of the bases for the "intuitive" insights that leaders use to guide
a business or
institution into the uncharted regions of the future. This gain of information about an organization
s
identity is related to another feature of predictability of nonlinear systems to which we now turn.
Attractors: Nonlinear Geographies with
Unpredictable States but Predictable Structures
One of the most fascinating findings of complexity theory
is that the evolution of nonlinear, complex
systems are marked by a series of phases, each of which is under the governance of an attractor(s)
dominating the system at that time. These attractors, arising out of the internal nonlinear dynamics
plus the
influence of environmental factors on the system, act to permit and constrain the range of possible
behaviors in the system. Moreover, when attractors change, behavior in the system concomitantly changes
as well because it is now operating under the different set of governing rules represented by the newly
emergent attractor(s).
In fact, it is often possible to determine a great deal
about the behavior in a complex systems through an
exploration of the qualitative properties of its attractors even when the specific equations modeling
the
dynamics of the system haven't been solved (Glass & Mackey, 1988). Since an attractor represents
the
"shape" of a nonlinear system, a "shape" determining its behavior, knowledge of
these "shapes" provides
some degree of ability to predict the system's behavior. This is the case even for chaotic systems which,
as
we saw above, are marked by the the presence of sensitive dependence on initial conditions rendering
the
future states of such systems unpredictable. Chaotic systems have chaotic attractors whose "shape"
determines the possible behaviors in the system; see Figure 2.
We can see from this figure that even aperiodic and unpredictable
chaos has attractors. If chaos were a
totally random system, time series data (i.e., measurements of the system at discrete time steps) would
simply completely fill out the coordinate plane within which the attractor is graphed. Instead we see
a
particular structure that delimits the coordinate plane. This structure is the chaotic attractor which
acts as
an enduring geometric shape (in phase space) for the system. The presence of this attractor is a structure
within which future states of the system must fall. In other words, not anything goes concerning the
future
evolution of a chaotic system - it must stay within its structure (Goertzel, 1993). So whereas the particular
future states of the system may be unpredictable, the fact that they will fall within the attractor
is definitely
predictable. In this case, the attractor acts as a system's structure that remains the same or is predictable,
whereas specific points on the attractor represent the system's states which are unpredictable.
For example, consider the weather: particular states
of the weather would be the temperature or humidity at
any particular time ("Today, October 29 is sunny, 54 degrees, with a humidity of 53% with a Southwest
wind at 10 mph"). But then there is the climate (Mid-Atlantic, Autumn) which acts as a structure
within
which particular states of the weather are constrained. In a Mid-Atlantic region during late October,
one can
predict with a fair amount of certainty that the day-time temperature will be between 45 and 62. (Of
course
this all becomes more complicated due to the fact that climates change as well Ñ but because climates
don't
change as fast as the state of the weather, they remain good candidates for predicting the range within
which future weather states will occur.) In other words, the climate as structure acts as an attractor
for the
states of the weather. The nonlinear dynamical psychologist Fred Abraham (1991) has termed this structural
predictability of complex systems "insensitivity to initial conditions" to contrast it with
the sensitive
dependence on initial conditions causing future states to be so unpredictability.
Because chaos is aperiodic, each new state of the system
will be novel, not an exact repeat of a previous
state. Indeed, deprivation in prediction turns out to be one of the preconditions of novelty in complex
systems. Yet, even though novelty and uncertainty are being generated in complex, nonlinear systems,
simultaneously, order and redundancy are also being maintained because of the bounded and patterned
arena of the chaotic attractor acting as a structure ordering the apparently random.
This understanding of attractor as predictable structure
can be related to what the complexity influenced
planning theorist Mike McMaster (1996) has said about foresight into the structure of the future because
the future is currently manifested in the structure of the present. Instead of emphasizing prediction
per se,
McMaster argues for foresight based on an understanding of the unfolding patterns in an organization.
Again, this is similar to the earlier point about how comparing present with past assessments can aid
leaders in discovering more and more about an organization's identity and using these discoveries to
facilitate a greater unfolding of this identity. Planners can enable greater insight into an organization's
"identity" by exploiting the idea of attractors as predictable structures. Also relevant here
is Gareth
Morgan's (1997) point about areas of paradox in an organization being precisely the points where insights
into a system's behavior may be accessible. Structure has paradoxical regions (e.g., how chaotic attractors
show tendencies toward both divergence and convergence, i.e., the so-called "stretching and folding"
of
chaos.) Moreover, related to a point made above, if structural predictability can be used to characterize
chaotic systems with their extreme form of nonlinear amplified unpredictability, then, structural
predictability is even more employable when it comes to systems characterized by a lesser degree of
nonlinearity.
Charting the Strange Realm of Nonlinear Resonance
As we have seen, nonlinearity and complexity can lead
not only to greater unpredictability in a system,
paradoxically, they can also yield predictable behaviors. One reason for this strange blend is the way
components and subsystems of complex systems become coupled with one another in feedback types of
relationships. Sometimes this coupling leads to the kind of nonlinear amplification seen, e.g., in chaotic
systems, and other times nonlinear coupling can produce phases of more stability, and hence, greater
predictability. Therefore, planners as cartographers of the complex world need to be familiar with various
kinds of regions of nonlinear predictability.
Consider, for example, the curious behavior that takes
place when pendulum-driven clocks are hung on a
wall already containing similar clocks: the new clocks become in-phase with the clocks already hanging
there, i.e., the periodic swings of the pendulums lock-into the same frequency. As a result, before
a clock is
hung on the wall, if the phase of the clocks already hung is known, then one can predict the eventual
phase
of the new clock Ñ it will be the same as the clocks already hanging. This phenomena of frequency-locking
called "entrainment" is one of the strange features of complex systems.
A similar frequency-locking phenomenon can be seen in
the case of large- scale weather patterns such as
the now notorious El Nino, the seemingly erratic warming of the equatorial surface waters extending
west
into the Pacific Ocean off the coast of South America, a phenomenon now known to deleteriously effect
global weather patterns. This year El Nino has been blamed for Hurricane Linda, the most powerful Eastern
Pacific Hurricane on record. The name "El Nino" comes from the Spanish for "the Christ
Child" because this
weather pattern has tended to occur around Christmas time.
El Nino is a very nonlinear complex system due to the
pervasive feedback loops between oceanic
phenomena (e.g., water temperature both on the surface as well as deeper as well as current speeds and
extension) interacting with atmospheric phenomena (e.g., air circulation and temperatures) ( Jin, F.F.,
Neelin,
J.D., & Ghil, M., 1994; and, Tziperman, Stone, Cane, & Jarosh, 1994). The nonlinearity of El
Nino is even
heightened when the seasonal cycle is added to the picture (See Appendix B). Yet, instead of this additional
nonlinearity making the system more unpredictable, it can, under some conditions, actually serve to
make El
Nino more predictable through engendering both a new kind of stability in the system, i.e., the way
the El
Nino cycle can become entrained (like the pendulum-clocks above) with the seasonal cycle, as well as
putting the mathematics of the El Nino nonlinearity within the known dynamics of the so- called routes
to
chaos. The type of emergent stability of entertainment or frequency-locking can lead to greater
predictability since the system can be temporarily "stuck" at these particular phases. Through
the on-going
intensified exploration of such nonlinear phenomena, the predictability of complex systems will only
increase. Again this is an area of nonlinear dynamics which leader/planners will need to know how to
get
around in.
Improvement in predictability, though, doesn't translate
into prophetic powers. Just this year, El Nino
popped up unexpectedly. Moreover, these remarks on the sophisticated mathematical patterns of El Nino
are not offered here as a suggestion that organizational planners should become mathematicians. Rather,
the point is that not all hope for prediction is lost when it comes to nonlinear, complex systems and
that
organizational planners will need to recognize that nonlinearity will prove to be more and more navigable,
but in a way that will defy common sense derived from outdated models of organizations as linear systems.
Fitness Landscapes: Exploring the Possibility Space
of Adaptations
Planning within the new context of the new nonlinear
and complex world will make headway to the degree
that planners will be able to actually explore the geographies of which they first needed to make new
maps.
Because this new world is so distinct from the old, without the new maps the explorers will surely get
lost.
Yet, the constructs according to which the new maps are being drawn are so diverse in nature and lean
so
often toward the arcane terminology and conceptualizations of sophisticated mathematics and highly
specialized sciences, learning how to explore this new terrain by using these maps as guides is not
something that can be learned over night. Nevertheless, there is an accessible key underlying these
maps of
complexity: it is the concept of adaptation as it has been developed in evolutionary biology. Adaptation
is
the ongoing process by which a species becomes "fit" to a changing environment by way of modifications
in structure, form, and functioning occurring among the individual members of the species. These
modifications result from both random mutations and recombination of genetic materials (e.g., found
in
sexual reproduction). Then, through the mechanism of natural selection, those modifications that prove
helpful to a species' survival are maintained. Complexity sciences, though, add another crucial ingredient
to
the process of adaptation: there is an order arising out of the nonlinear dynamics itself of the system
of
genetic components (what Kauffman, 1995, calls "order for free") upon which natural selection
operates.
Hence, we can say that adaptation has four components: the utilization of random events; some kind of
combinatorial process; emergent order; and natural selection. In terms of a business or institution,
adaptation would also consist of random events in the sense of taking advantage of serendipity (more
on
that later), as well as new combinations such as connecting previously disconnected parts of the
organization, ongoing experiments with new organizational processes and structures, i.e., multiple,
simultaneous activities that may appear to be going in cross directions.
With adaptation as a framework, therefore, planning as
exploration becomes a matter of helping businesses
and institutions explore the "possibility space" of the adaptive value of various modifications
of structure,
form, and process. The complexity influenced organizational theorist Steve Maquire (1997) refers to
this
exploration of the possibility space of adaptation as a design problem, in that the diverse options
in
business strategy are designed according to their potential adaptive value. A particular strategy and
its
modifications enable a business or institution to be more or less "fit" in relation to its
environment. As the
environment changes, the strategies will need to change to sustain the business fitness.
A helpful way to expedite this more inclusive notion
of planning is to utilize the tool of fitness landscapes
which graphically depict the adaptive value of particular modifications. But, before we explore this
vital
concept in greater depth, let's contrast it with a very different type of "landscape" that
is unfortunately far
too prevalent in our businesses and institutions: the Sisyphean landscape originating from the Greek
myth
of Sisyphus who was eternally condemned to every day push a huge boulder up a steep hill, only to have
the boulder fall back to the bottom of the hill at the end of the day. In the organizational counterpart
to the
myth of Sisyphus, the task of management is seen to be a matter of pushing work uphill everyday against
an landscape of resistant employees, recalcitrant boards, and organizational inertia.
This Sisyphian landscape depicts the work of management
as primarily a fight against what are thought to
be the given, natural tendencies of the workplace.
But this picture of bull-headed hill-climbing is not
what is connoted in complexity science's hill- climbing on
fitness landscapes. Here, adaptation is indeed hill- climbing but instead of proceeding against natural
forces, it follows along the contours of the fitness landscape which are manifesting these natural forces
-
contours shaped by nonlinear dynamics, randomness, and recombination occurring as a potent amalgam of
self-organizing processes. It's not that there is no effort involved in this exploratory hill-climbing,
but it is a
different, non-futile kind of effort, more like a dance with the natural forces than a struggle against
them
(after all, sexual activities leading to genetic recombination require some amount of effort, but -
need I go
on?).
Getting back to fitness landscapes per se, the fitness
or adaptive value of various possible modifications in
the characteristics of a species are portrayed as a "landscape" with different hills, peaks,
plains, and valleys
(Kauffman, 1995), see Figure 4.
The height of a peak represents the fitness value of
specific modifications, with nearby or neighboring
peaks representing closely related modifications. For example, consider the fitness value of five fingered
hands for primate survival. Such a characteristic may be represented by a fairly high peak, with neighboring
peaks that are slightly shorter representing four or six fingered hands, or hands with two opposable
thumbs
and so forth. The point is that fairly close modifications are close by geographically with higher or
lower
peaks depending on their fitness value. In a business or institution, nearby peaks may represent closely
related marketing or promotional strategies. Adaptation can be thought of as "climbing hills"
on the
landscape toward "peaks" of higher fitness, and natural selection can be conceived as how
adapting
populations are "pulled" up the peaks (Kauffman, 1995). A fitness landscape provides an indication
of the
degree to which various modifications add to or detract from the system's survivability or sustainability.
Kauffman has pointed out that an important implication
arising from the study of fitness landscapes is that
there may be many "local" peaks or "okay" (i.e., "good enough") solutions
instead of one, perfect, optimal
solution. This is related to the point made by Kauffman that since an evolving species does not have
a
God's eye view of its fitness landscape (cannot see the contours of the landscape within which it is
embedded), evolution proceeds by the population sending out "feelers" or random mutations
that sample
the fitness value of the hills and valleys. In other words, there are experiments in modification of
an
organism closer or farther way from the local peak. This is the accepted, gradualist Darwinian assumption,
similar to a gradual method for problem-solving by a piecemeal, trial and error searching for solutions
in a
problem "space" (Kauffman, 1995). In this blind view of a fitness landscape, there are no
"clues" that can
guide the species toward modifications that will be more helpful for survival. So, adaptation becomes
a
matter of a random search. But this is only true for a totally random or maximally rugged landscape.
In these
totally random landscapes, for every step taken uphill, the number of directions leading to a higher
landscape is cut by a constant fraction, so that it gets harder and harder to to keep improving. As
a result,
the rate of improvement slows exponentially. Such considerations shift the role of planners as explorers
in a
major way: instead of having to find the optimal strategy, one can adopt a more trial and error kind
of
process, seeking "good enough" local peaks, resting there, and then continuing to search either
on nearby
or far away peaks depending on organizational conditions.
Exploring Fitness Landscapes Using the N/K Model
Yet, adaptation need not take place on a purely random
landscape. To envision nonrandom fitness
landscapes whose contours reflect the underlying nonlinear and complex dynamics among the components
in a system or ecosystem, Kauffman has developed a N/K model of adaptation. In this N/K model, N =
number of traits (such as bowed or straight legs, webbed or separate toes, long or short feet) and K
= the
number of inputs from other genes (which is a measure of the dependence of traits on one another, i.e.,
the
nonlinear coupling or feedback among the traits). Kauffman adds this K parameter since the contribution
of
a single trait to adaptability may depends on other traits (e.g., the contribution of bowed legs to
adaptive
fitness may simultaneously involve whether the feet are long or short e.g., if N=3 and K=2, the genome
has
three genes each of which is effected by two others). Using this model, one can alter K as if twisting
a
control knob and observe what happens as the landscape deforms. As K increases, the more
interconnected the traits or modifications are, so there are more conflicting constraints and, thereby,
the
landscape becomes more rugged with more local peaks.
Unlike a landscape with one large mountain representing
a very high value of adaptiveness, in this more
rugged landscape, there are a large number of modest compromise solutions rather than a perfect one.
In
organizational planning, an analogy can be found in the Boston Consulting Group (BCG) portfolio analysis
of products or business units. In the BCG portfolio grid, business units or products are grouped into
four
sectors which are really another way of talking about their adaptive value: stars; cash cows; dogs;
and
question marks. All four may represent compromise solutions, even stars and cash cows because it is
undecidable from the grid alone whether the star or cash cow represents a high optimal peak or is trapped
at
a local peak. Most planners get stuck at that point, whereas the nonlinear fitness landscapes promises
a
way to envision the adaptive value of even currently highly productive products or business units.
Adaptation becomes more difficult as K increases to its
maximum value, N-1, where every gene affecting
every other so the fitness landscape becomes completely random. In such a random fitness landscape,
an
adapting organism gets trapped at very low peaks, and the rate of improvement slows; thus, adaptation
to
highest peak becomes virtually impossible. This can be seen in biological as well as technological evolution
since they are processes that attempt to optimize systems riddled with conflicting constraints (Kauffman
and Macready, 1995). In such a situation, foolish adaptation, i.e., moving down a fitness slope, may
be
paradoxically advantageous since it frees up those modifications trapped on lower valued short peaks.
(We
will come back to this idea of foolish adaptation later to see how planners may be able to exploit it.)
In a
moderate degree of ruggedness, the highest peaks can be scaled from the greatest number of initial
positions, so an adaptive walk is more likely to climb to a high peak than a low one (i.e., the basins
of
attraction for the high peak as attractors are larger than for lower peaks).
Planning as Adaptive Exploration of Organizational
Strategies
Organizations evolve on correlated but rugged landscapes
(Kauffman, 1995). Maguire (1997) understands
the choice of a specific strategy, e.g., its choice of which products or services to make or offer,
as correlated
with a specific fitness landscape. For example, an increase in the heterogeneity of the market is equivalent
to an increase in "ruggedness" on the landscape, which, in turn, means an increase in the
complexity of the
strategy as a design problem. The point is to envision strategy in terms of how the various combinations
of
organizational processes and structures which make up a strategy add to or diminish the adaptive value
of
specific strategies. But notice here that planning is not so much prediction, as exploration of possible
scenarios. In this sense planning can be reconceptualized as exploratory searches through the "space"
of
modifications of a strategy. Here, the use of fitness landscapes can be applied to gain insight into
which
innovative organizational designs, processes, or strategies promise greater potential.
Maguire has provided a kind of grid which suggests the
quality, quantity and foolishness of different
exploration strategies. For example, how constrained or coupled is the environment (an organizational
analogue to the N/K Model). He can use this grid to classify the appropriateness of a particular business
strategy, e.g., Mintzberg's (1988) strategy of quality differentiation is a relatively local search
on a short
distance while design differentiation strategy is a farther away search in the adaptation landscape.
Furthermore, Maguire has identified exploration or search parameters: exploration rate (search activity
per
unit time, number of sample units per unit time); exploration distance (search distance across landscape);
and exploration direction (which variables on a string to flip; or, constraining the search to a specific
direction).
In the new nonlinear and complex geography of organizations,
therefore, leaders as planners face a two-fold
challenge: drawing useful maps of the new terrain and exploring this new terrain through the
encouragement of strategies that tend toward higher fit. However, designing strategies with better fit
does
not always consist of climbing straight-up adaptive hills. Sometimes random searches are what's called-for,
sometimes what is required is the seemingly foolish move of going down a hill, and there are still other
seemingly counterproductive practices. Thus, in the nonlinear geography of complex systems, the planner
also needs a bag of unusual tricks, so now we turn to the role of planner as Trickster.
Planners as Nonlinear and Complex Tricksters
So far we have examined planning in complex systems in
terms of both map-making and exploration of the
new nonlinear geography. Both of these planning roles assume it is a rational process, consciously utilizing
new constructs to better map and explore the new terrain that is being revealed. But the new geography
emerging from complexity research is, in many respects, so unlike the predictable, linear, simple, and
equilibrium-based world of classical science, that rationality itself is in need of revision. The point
being
made here is not a call to act irrationally, but, instead, it is to place attention on how reason itself
has been
shaped to conform to the linearity and simplicity of the classical world. In an environment that is,
in
important respects, unpredictable, unstable, and vulnerable to random events, then the rationality of
planning must include new outlooks and practices congruent with the new world being discovered. Here,
the appropriate image for planners may not so much be the rational designer as that mischievous figure
from mythology: the Trickster. Found in diverse cultures throughout the world, the Trickster breaks
taboos
and flouts, traditional mores and norms, constantly investigating, improvising, and devising new ways
(Harding, 1963). The pranks of Trickster figures are legendary and surprisingly similar to the characteristics
of complex, nonlinear systems: unpredictable; bizarre; disproportionate; random; mixing-thing up; stirring
the pot; upsetting the apple-cart. These qualities are certainly a long way from the image of planning
as
precise forecasting, conscious design, and careful implementation of strategy. Yet, it may be that it
is these
tricks of the Trickster that organizations desperately need to navigate through these tumultuous times.
In this section, we will be looking at planning according
to three Trickster roles:
-
"Noise Makers"
- "Foolish Trekkers"
- "Odd Matchmakers"
Planners as "Noise Makers"
The Utilization of Random Events in Complex Systems
Besides its crucial role in adaptation, randomness has
been understood as a powerful source of the new
structures (e.g., dissipative structures) emerging during the process of self-organization (Nicolis,
1989).
Examples of such emergent structures are the hexagonal cells arising in the Benard liquid when a critical
temperature is reached, or the life-like patterns emerging in cellular automata and random boolean networks.
Random events are unpredictable, unplanned occurrences that a system, under a far-from- equilibrium
condition or unstable state (i.e., near bifurcation), will notice, respond to, and amplify as a major
component
of the new emergent structures. For example, the hexagonal convection cells emerging in the Benard system
are partially the result of the amplification of random currents in the liquid so that the specific
directionality
of the emerging convection cells is unpredictable. According to Prigogine and Nicolis (1989), nothing
in the
experimental set-up permits a prediction beforehand of the state that will eventually ensue: "Only
chance, in
the form of the particular perturbation that may have prevailed at the moment of the experiment, will
decide..." (p. 14).
It is crucial to note that chance elements only become
an important factor when the system is unstable,
because that is when the nonlinear dynamics in the system have the capacity for amplifying the effect
of a
chance occurrence. A stable system will dampen random movements away from the prevailing attractor,
whereas, in an unstable system random events can kick the system away from its attractor - see Figure
5
where stability and instability are portrayed as a ball trapped inside a bowl or perching precariously
on top
of an overturned bowl.
The planner as Trickster would act to first turn the
bowl inside out by challenging the assumptions of the
current attractor, and, then, stand on top of the peaked bowl on the right to facilitate the influence
of
random events in pushing the system away from its current attractor.
If a system is open to the effect of random events to
the point where it can undergo modification of key
aspects of its processes and structures, then the system may be able be more adaptive to the environment
as it changes. Indeed, random-inspired reorganizations may represent an evolutionary response of the
system to changes in the environment but only if the system is in vital contact with its environments
(Allen,
1988). This vital contact is what enables the system to try out its new modifications in the changed
environment. Moreover, Allen and McGlade (1985) state that in order to learn about the world around
them,
it may be the random departures of systems from norm-seeking, average behavior which are decisive.
Nicolis (1989) has evidence that permanent and rigid structures or processes in a system which is
interacting with an unpredictable environment will bring the system to a less than optimal condition.
Whereas, a system which has a high rate of unpredictable explorations (i.e., influenceable by random
occurrences of its unpredictable environment) can develop temporary structures or processes suitable
for
any occasion that may arise.
Furthermore, chaos and complexity, according to the physicist
Robert Shaw, turn out to be a generators par
excellence of information which can be understood as a potent mixture of randomness and redundancy
(Shaw, 1981). Shaw interprets the source of this new information as a matter of the transfer of information
from a micro-to a macro-scale. The chaotic attractor magnifies the random occurrences on the microscale
upwards into novel information available to the system on a macroscale. According to the physicist Joseph
Ford (1989): "chaos is dynamics freed from the shackles of order and predictability. It permits
systems to
randomly explore their every dynamical possibility" (p. 354).
In fact, randomness permits the emergence of real novelty
in a complex system because by its very nature a
random event is unpredictable and not the result of a pre-set plan (for then it wouldn't be random).
Consequently, randomness seems to be a necessary component at some stage in the process of
organization innovation. For if innovations are truly novel they must be unpredictable and what better
source of unpredictability is there besides randomness? Similarly, in an interesting parallel, it has
been
repeatedly pointed out that unplanned events (i.e., random) have often played a crucial role in scientific
discoveries (Austin, 1977). Examples are numerous: the discovery of penicillin, radioactivity, Teflon,
and so
on. Perhaps, the process of scientific discovery can be understood along the lines of self-organizing
systems. In both cases, that of organizational innovation and scientific discovery, randomness can function
serendipitously in the formation of new, possibly more adaptive modifications of pre-existing patterns.
But
of course, the organization or the scientist must be open to and ready to make use of the random event.
As
Pasteur once said, "Chance favors the prepared mind" Ñ therefore, the organization must
be primed to take
advantage of the random event, and such priming is one of the roles of the leader/planner as a Trickster.
Tricksters help make a system unstable in order for innovation to emerge. This is certainly a far cry
from the
traditional role of leaders as organization stabilizers. Certainly, there is a time for stability, but
there is also a
time for instability, and when organizations find themselves in an unpredictable environment, it is
likely a
time for instability and here is where the Trickster can play a major role.
Planning and Serendipitous "Noise" Making
Random events in organizations are what Ciborra (et.
al., 1984) call organizational "noise", i.e., phenomena
occurring in or around the organization that are usually ignored and whose effects are presumed to be
restrained by organizational control mechanisms. But, in unstable conditions "organizational noise"
may
assume a critical role in the evolution of the system through nonlinear amplification and self-organizational
processes (Goldstein, 1994). But, of course, because emergent patterns result from random effects, they
cannot be predicted, nor can it be established ahead of time just what particular "organizational
noise" will
have a transformative rather than disorganizing effect. The role of planners, then, could be that of
facilitating an organization's experimentation with noise. Figuratively speaking, planners would be
acting
like Trickster-inspired "noise makers" (e.g., children and adults on New Year's Eve making
a lot of noise, the
louder and more cacophonous, the better). This means that leader/planners as Tricksters would aid an
organization in exploring its "noisy" elements, events that spontaneously depart from the
norm, and instead
of the normal attempt to dampen the effects of such noisy elements, actually amplify these effects.
But this means that planners would simultaneously need
to facilitate those unstable conditions that allow
noise to have an impact. Again, this is a Trickster role in upsetting the apple cart. The author of
this article
(Goldstein, 1994) has discussed such methods for generating instability under the term, borrowed from
Prigogine, far- from-equilibrium conditions. Examples of such Trickster noise-making would included
methods that highlight the differing ideas and attitudes existing among people in a work group (not
generating conflict but admitting it is there and utilizing its tremendous energy), or that challenge
currently
held deep beliefs about what an organization is and how it should function, or that upset the apple-cart
by
the facilitation of what seem absurd or foolish activities (again see Goldstein, 1994, Chapter 10).
Along the same lines, following Shaw's lead about the
transfer of information from micro- to macro- scales,
planners can expedite processes in a business for magnifying the creative endeavors of its individual
members and incorporating these creative ideas and actions into the macro-scale of how the organization
does its business. Included in such Trickster tricks is also the technique of Wicked Questions suggested
by the organizational complexity researcher Brenda Zimmerman (see Zimmerman in this volume).
Planners as Odd Matchmakers
Recombination
One way that biological organisms explore their adaptive
"space" is through sexual reproduction where
recombinations of parental genetic material afford the opportunity for modifications that may prove
more fit
for the species. The computer scientist and complexity pioneer John Holland (1992) has created adaptive
computer programs called genetic algorithms based on sexual reproduction as a paradigm of "crossing-
over" or the mixing of genetic material (which become bit strings in his programs). The programs
evolve by
both sexual-like recombination as well as through random mutations; each new modification that is closer
to
the solution is given a heavier weight. Then the program over many generations converges to a solution.
The computer scientists Gerhardt Bruderer and Martin Maiers along with the complexity management
consultant and theorist Glenda Eoyang (Maiers and Eoyang, 1997) have been designing a genetic algorithm
as a decision support tool for managers. This program can easily be modified for decision-making in
planning as well. But again, such a usage is dependent on planners revising their view of what their
main
roles are to be.
Recombination also comes up in Kauffman s N/K model.
For Kauffman, sexual mating or reproduction
allows a kind of "God's Eye" peek at the peaks (Kauffman, 1995). The genetic recombinations
that result
from sex between organisms at different locations on a landscape allows the adapting "population"
to "look
at" the regions between the parental genotypes. In this way recombination allows the adapting population
to make use of large scale features of the landscapes to find high peaks. In fact, Kauffman found in
his N/K
landscapes that populations using mutation and recombination as well as selection improve far more rapidly
than those using only mutation and selection (Kauffman, 1995).
If the fitness landscape looks like the Alps, then the
peaks carry mutual information about where to find
high peaks: they are nearby! Moreover, if parents are high up on the peaks, then the kids will have
a greater
chance to start out higher. Yet, recombination can actually be harmful on a totally random landscape
since if
parents are at local peaks, recombination can lead progeny to be "dropped off" in a place
with lower fitness.
Furthermore, when a search is merely random with no clues about upward trends, the only way to find
the
highest pinnacle is to search the whole space.
Recombination is going on in organizations in an unprecedented
manner with the accelerating pace of
mergers and acquisitions. Previously competitive organizations are now joined and the frequent issue
concerns how these previously separate, even hostile entities can possible work together. Whereas the
traditional approach might be to impose a new structure or plan or working procedures on the newly merged
system, an approach informed by genetic algorithms or the N/K model would see this recombination and
the potential conflict it might engender as a great opportunity for the emergence of new organizational
practices and directions (see Goldstein, "Leadership and Emergence" in the File Cabinet section).
Then the
intervention would not be to dampen differences of opinion but to highlight them, amplify them and allow
a
more adaptive organizational structure to emerge as a result of the merger.
Matchmaking for Strange Couples
Organizational planners, then, might conceive of themselves
as organizational "matchmakers" bringing
together diverse organizational "genetic material" and mixing it up and seeing what ensues.
But these
should be strange matches, bringing together what was previously thought of as incompatible elements
or
components or subsystems. For example, bringing together janitorial staff with product designers,
customers with suppliers, finance executives with secretaries on nursing floors. In fact, the more seemingly
incompatible the elements, the more they probably need to be brought together. The emphasis, here, of
course, is on experimentation and the allowance of emergent patterns that are unanticipated and have
unexpected outcomes. Again, one cannot know the correct solution ahead of time, so one needs to work
with whatever emerges through recombination. Such organizational matchmaking links to Lane's and
Maxfield's (1996) idea of generative relationships which are connections among people which generate
new
organizational forms, directions and strategies. A generative relationship is based on heterogeneity,
it leads
to greater action possibilities, it promotes the sharing of information (hence, the flow of innovation),
and it
sets up the conditions for more novel relationships. Organization leader/planners as Tricksters make
and
engender more matches and thus build into a feedback cycle of expanding networks. This is emergence
and
self- organization at its best.
Conclusion: From Planning the Future to Preparing
for the Future
Unlimited possibilities for a company are, of course,
not possible. Possibilities for strategies are limited by
the past history of the organization, by the constraints of the marketplace, and by the identity of
the
organization, i.e., its set of core competencies. Complexity sciences can provide better maps for
organizational strategy design that follow these constraints than traditional organizational tools or
constructs. We live in a complex, interdependent world where the business and institutional environment
is
undergoing unprecedented change, even turbulence. Whereas planners whose main function was to
accurately predict the future had some reason to congratulate themselves when organizations were in
a
more stable environment; today the whole claim of linear predictability is being seriously undermined.
Therefore, the role of leader/planners must shift to take advantage of what we are learning about the
dynamics of complex, interactive, nonlinear, nonequilibrium systems. This shift includes transforming
planning into:
-
A set of better means for organizations to get to know
who they are, what they do well, and what
their innate tendencies are. Planning becomes preparation for the future through greater insight
into what one does better right now.
- A set of processes for facilitating organizational experimentation through using
whatever
happens, anticipated or unanticipated. This makes planning into a way to prepare for or adapt to,
not predict the future.
- A set of tools taken and modified from nonlinear mathematics and sciences to
help an organization
navigate through the newly discovered, intriguing terrain opened up by the exploration of complex
systems.
Whereas, at first sight, it might have appeared that
the unpredictability of complex systems foredoomed all
attempts at planning, there are important ways with which the nonlinear dynamical accounting for this
unpredictability can be exploited for a revised conception of organizational leading/planning.
Planners as Foolish Trekkers
In his N/K Models, Kauffman identified situations in
fitness landscapes where low values of the K
parameter (representing coupling among traits) lead to adaptive modifications getting trapped in local
minima and thereby never arriving at peaks with adequate fitness. This is analogous to organizations
or
work groups getting stuck in equilibrium attractors which Goldstein (1994) blames on the presence of
self-
fulfilling prophecies which link organizational attitudes, expectations, behaviors, and results in vicious
circles. For example, a self-fulfilling prophecy may link an organization's sense of identity and its
market
with actions congruent with those "prophecies" and which lead to results which confirm the
original
expectation. Self-fulfilling prophecies, though, can trap the organization or work group on very suboptimal
short peaks.
To free adaptive processes from their entrapment in local
peaks, Kauffman has suggested a certain amount
of "foolish adaptation" or "going the wrong way" referring to going down instead
of up peaks. That is, to
get to a peak with a higher adaptive value, first there must be a descent from a lower peak. As Maguire
puts
it, an escape from suboptimal peaks opens up the possibility of a uphill path to higher fitness peaks
(Maguire, p. 13). Kauffman points to simulated annealing in models in condensed matter physics which
is a
kind of thermal bath which loosens up this kind of entrapment process. Again, the analogy is to far-from-
equilibrium conditions in organizations which serve to interrupt those self-fulfilling prophecies which
trap
organizational functioning in suboptimal routines.
Hence, organizational planning can include the encouragement
of a type of foolish adaptive walks. Here,
planners in their Trickster role would facilitate a work group to "go the wrong way", do things
unexpected
and out of the ordinary even though these activities seem to be counterproductive to achieving the
organization's goals. From a linear and simple perspective, this sounds like sheer idiocy, even dangerous
to
an organization's success. Yet, "going the wrong way" is precisely what creativity specialists
often call-for.
For example, participants in creativity seminars are often encouraged to go on excursions away from,
even
in opposite directions, to what they think they should be doing (Gordon, 1961). These foolish excursions
or
treks tend to loosen the grip of familiar and comfortable walks in creativity space.
In terms of organizational planning, such foolish treks
could consist of conducing meetings where, instead
of good ideas, foolish notions for strategies could be entertained. (This after all is what "brainstorming"
is
supposed to facilitate but often doesn't because of strong pressure for group conformity). But foolish
notions need not only be entertained in fantasy, planners as Tricksters need to try out some of these
foolish directions. Again, because we don't have a God's Eye view of the future, complex systems need
to
experiment a great deal, and sometimes, with modifications of existing practices that at first sight
seem
foolish.
1. Thoughts
