**CHAOS AND MANAGEMENT SCIENCE:
CONTROL, PREDICTION AND NONLINEAR DYNAMICS
T. R. Young
Scholar in Residence
Sociology
**Texas Woman's University

Dec, 1994

date

__No. 006 __

Distributed as part of the Red Feather Institute Series on Non-Linear Social Dynamics. The
Red Feather Institute, 8085 Essex, Weidman, Michigan, 48893.

**CHAOS AND MANAGEMENT SCIENCE:
CONTROL, PREDICTION AND NONLINEAR DYNAMICS
**

**INTRODUCTION**

Any of the administrative sciences wishing to guide firms, agencies and governments
through the cycles of uncertainties in an increasingly complex and changing environment
might well consider the findings of the new science of Chaos. Chaos theory deals with the
changing relationship between order and disorder in the behavior of natural or social
systems. Chaos theory is so fundamentally different from previous understandings of social
and natural dynamics that an entirely new paradigm for the knowledge process is required.
The chief elements of the new paradigm include 1), the nonlinearity but self-similarity of
systems dynamics, 2), qualitative transformations to new dynamical states, 3),
progressively more complex outcomes as well as 4), the appearance of new forms of order
out of even the most chaotic regimes.

The experimental tasks energizing such a management science consists of a) generation and
display of data to reveal the hidden patterns of dynamics in phase-space b)identification
of the key parameters which drive a system from one dynamical state to another, c)
reflection on the implications for a firm or an agency which are close to change points at
which entirely new modalities of behavior emerge, d) adoption of flexible strategies of
management as causality opens and closes, e) concern for interacting exigencies inside and
outside the firms which bend upon employees, suppliers and clients and which affect their
dynamics, as well as, f) the quest for new organizational forms in a time of advanced
chaos. Corollary to such work is the need to cultivate a mathematics and a geometry with
which to study such dynamics.

The majority of the paper is devoted to three nonlinear dynamic states and three
transformations from 1-valued causal basins to much more complex fields together with
their challenges to management science. A hypothetical application is provided to help
illuminate its principle ideas. Lastly, a brief exploration of the larger implications of
chaos theory for a "postmodern" management science is offered.

**ELEMENTS OF CHAOS THEORY** Chaos theory is a set of ideas
about the transformation from order to disorder as the generation of new forms of order
from turbulent, nonlinear dynamics. A system exhibiting nonlinear behavior may appear
quite random over time, yet studies of chaotic regimes in phase-space reveal underlying
patterns. Such patterns are called "attractors" since a system appears to be
"pulled" toward a region in an outcome field during its cycles or periods
(Mandelbrot, 1977). Some attractors are called 'strange' attractors since a system behaves
in ways not expected by Newtonian physics, propositional logic, rational numbering systems
or euclidean geometry. **1
**Chaos research maps the geometry of system dynamics
in phase-space.

Management science, as a discipline and profession, must learn how to generate and display the data of systems dynamics in such a way as to reveal the geometry of the deep structures hidden in those data. One of us, L. Douglas Kiel, presents this technique below. Knowledge of those patterns /attractors help one connect individual workers, firms and national economies to key parameters in the larger society the values of which may either stabilize a system or drive it into far from stable equilibrium. While Chaos theory is a science of surprises and transformations, it is also a science of wholeness and connectedness that reunites a given firm with the social and natural environment in which it is found.

Chaos research, as mentioned, tracks the transformations of dynamical systems from one behavioral regime (attractor state) to another. In such transformations, management science has much to learn and much to ponder. As key parameters of systems reach each one of four feigenbaum numbers (F

I.

Routine dynamics inside a factory, an office, a hospital, a school or a prison have the character of a torus; they are familiar but are never the exactly the same from day to day...similarity displaces sameness in the paradigm now before us. Quality control experts such as W. E. Deming (1986) are sensitive to the self similar dynamics found in chaos research. Deming does not argue for "zero defects" in manufacturing output nor does he argue for perfectly replicable products, but instead accepts statistical variation within defined parameters as preferred management policy. Management policy may well work to restrict entropy to the fractal geometry of a torus yet no set of administrative controls can generate that precise behavior of a point attractor which is so much a part of modern science and its preferences for linear dynamics.

Figure 1. Three Views of a Torus as Dynamics transform into Structure.

The geometry of a torus is seen in more detail in Figure 1. In reading a torus, the first form in Figure 1 represents the dynamics of one system over one iteration. It might depict the income pattern of a person over a lifetime or the profit behavior of a firm over a year.

The second form in Figure 1 might depict either one system over a dozen cycles or a dozen
firms over one such cycle in terms of some attribute of the system of interest to the
researcher. The near to stable geometry of any given torus emerges after thousands of
iterations of such cycles. At some point between the first iteration and those thousands
of iterations, process transforms into structure. The theoretical point is
momentous...such regularity becomes part of the biome in which it is found; other systems
in the area come to match their own structure and dynamics to this regularity. Causality
opens and closes depending upon the region of phase-space in which we look.

One can see that the system (or systems) do not fully occupy the space available to it.
Indeed, if it were to occupy all the space available, one would never know just where to
look to find it. Dynamics of the system would be fully chaotic; other systems would not be
able to map their own behavior to that of the person, firm, species or society...causality
would disassemble at that point.

Given the semi-stable dynamics of a torus, management policies generally work with
sufficient efficacy to warrant the resources allocated to them. If we want workers or
students or shipments to arrive within a close approximation to the time of last arrival,
then there are policies which will work to effect that close (but never precise)
approximation. The unique aspects of each iteration reinforces contingency based
approaches in management science (Koontz, 1961).

In brief then, self similar systems such as a torus exhibit near to stable dynamics. In
the observations of thousands and millions of iterations of natural and social systems,
the pattern which emerges is a chain of variations with infinite variety, infinite length,
and infinite detail rather than a stable, natural point or pattern. This
similarity-but-not sameness is of considerable interest to management inquiry. It sets
variation at the foundation of the administrative process and decenters precision and
conformity as goals of management.

**Feigenbaum Numbers** When a key parameter of a torus increases in value, one region
inside it can expand to form a tongue; when it exceeds a given value, that tongue expands
to form another wing of the attractor. Figure 2 offers a close up view of an emerging
tongue (arrow) of a torus which might expand with a small increase in an already enlarged
key parameter (e.g., price of raw materials, labor costs, capital costs, taxes or such) to
form a new wing in an outcome basin.

Figure 2. The Birth of a New Attractor

The Feigenbaum number for the transformation of a torus into a
butterfly attractor is 3.0. That is, when a key parameter exceeds 3 times its value in a
previous iteration, it forces a outcome basin to expand into two distinct causal fields.**5** The point is significant. Most researchers
expect systems with similar initial conditions to behave similarly. Chaos theory teaches
otherwise. The fact that there are **two** natural outcomes for a system whose internal
parameters have not changed is foreign to most scientific thinking. Given changes in
external parameters, water drops, gypsy moths, and perchance business firms undergo phase
transitions without changing the nature of water, the physiology of moths or, perchance,
the policies of a firm.

More than that, at the margins of the two outcome basins, it is not possible to predict to
which outcome basin a moth or a firm or, perchance, a customer/client will go. Causality
loosens and solidifies as one samples different regions of a 2n+ outcome field. This fact
has profound implications for generalizability, replicability, and falsifiability of
research findings. It may be the case that, in the Chaos paradigm, there are no samples,
only 'universes;' no statistics, only parameters.

This kind of change, from 1n outcome state to 2n+ outcome states requires a whole new
philosophy of management science. It is the kind of dramatic change which heretofore has
been seen as inimical to good management but a 2n causal field offers choices not found in
limit or torus attractors. For example, advantages accruing from a 2n gender division of
labor are not possible in a unisex society; some societies invent 4n+ gendering patterns
in order to combine certainty and flexibility in labor routines. In industrial societies,
gender free occupational specialties replace gender divisions altogether as a way to
organize the work process since 2n, 4n, or even 8n gendering systems interfere with
flexibility in role allocation; in marketing strategies and in accessibility to unused
talents and knowledges.

Management policies which had been adequate, even highly successful in a 1n causal basin
now no longer cope with the variety found in the new basin. Innovation, flexibility, and
spontaneity become assets in such a causal basin replacing stability, uniformity and
formality as preferred management policy.

Figure 3. Two Views of a Butterfly Attractor

**The Butterfly Attractor** Strange attractors with more than one loose but
predictable outcome basins are called butterfly attractors. Such a form is considered an
attractor since, while a system may vary markedly from one iteration to the next, still it
is very probable that it will wind up in one of two fairly stable if very different
outcome regions. Figure 3 offers two idealized views of the same butterfly attractor; one
view is presented in the well known time series format and the second view in a
topological display which reveals its changing causal geometry in phase-space. The first
such attractor was identified by Edward Lorenz in his meteorology simulations in the early
1960s (Lorenz, 1963).

And, as we see in the anatomy of a butterfly attractor, when a parameter of the larger
environment changes beyond a given value, a system (or set of systems) can fluctuate
between two outcome basins. Should a new wing develop from a torus, one might see one set
of firms which succeed by virtue of internal policies and one set of firms which
inevitably fail even if they adopt the same policies. There are now two outcome basins in
that causal field. In between the two basins is a region most interesting to management
science since, in that area, linearity is lost. There will be some firms which skirt the
margins of both basins and may be drawn into one or the other; there is no way to predict.

It is important to note that it may not be the internal dynamics of the business that
leads it toward one or the other wings of an outcome basin, but rather a small change in
external parameters. The logic of this point is that, in the future, administration of a
given firm must be open to policies which consider the stability of the whole system. When
one adds connectivity to uncertainty, the best strategy is to reduce uncertainties for
every connected system in the field rather than maximizing the short term advantages of
one's own firm.

When a causal field has some fraction of its basin occupied by two or more attractors and
some fraction in which great disorder is found, administrative policies which presume one
and only one outcome field lose efficacy; indeed, causality, prediction, and control
become casualties to new dynamics. For example, if external conditions change to open up
new attractors in the labor market, workers would have more than one choice about the
conditions of employment. Policies which had worked to attract and/or motivate workers
lose efficacy in such a complex field. The difficulty of developing policy as to
reasonable labor relations is resolved by a commitment to contingency theory (Koontz,
1961).

Figure 4. Labor Effort Attractor: a Federal Agency

Most dynamics are not so tidy as those in Figure 3. Figure 4
displays the kind of nonlinear order that exists in most actual nonlinear systems. **6** In Figure 4, data points are mapped on a
cartesian graph which represents the phase space of work activity. In this phase space
map, the current week's percentage of labor required to meet outside requests in a public
agency (t), is mapped on the y axis (vertical), while the previous week's percentage of
total labor required (t-1), is mapped on the x axis (horizontal). This attractor thus
reveals the dynamic structure of the system as a function of the relationship between
successive adjoining time periods.

In Figure 4, one can see two areas to which most time demands tend to cluster (bottom left
and center right). Then, periodically, the agency labor demands loop from low labor time
needs to much higher as requests from outside agencies arrive. The 'erratic' behavior in
Figure 4 is entirely natural to nonlinear systems. If one were to try to control the
twisting, turning, looping dynamics in Figure 4 to more closely approximate those in
Figure 1, one would have to control more and more of the environment in which the system
moves. Such efforts are unwise for two reasons; first, the costs of control efforts would
affect profits (or wages if it were a worker owned and operated firm).

Secondly, as management piles layer upon layer of controls, control tends to displace
other goals. Taking the U.S. economy as a whole, investments in control apparatus eat up
ever increasing chunks of federal, state, local budgets as well as that of private firms.
Both higher prices and higher taxes are required thus reducing competitive advantages and
neglecting essential common social needs. Private security forces now outnumber public
police and these are increasing each year. Add to that the various layers of control
activity within and outside a firm and one can appreciate the need to adopt new goals in
the management of nonlinearity rather than more controls.

If the experience of the former Soviet Union has taught us
anything, it has taught us of the futility of trying to plan all activity of a productive
system. If one considers the fact that all private and public systems now operate in a
global economy, the task of controlling all key parameters in order to obtain stability of
behavior of a firm (or a nation) becomes daunting. Even the most powerful nation cannot
control all other nations and firms in the global economy now emerging. There are,
however, solutions to the problem of order. **7**
We discuss the management of Chaos below.

**III. Exploding Attractors** There is a third kind of change of great interest to
those in management science. Chaos theory offers an explanation and description of the
transformation of strange attractors from quasi-stability toward far-from-stable chaos.
Feigenbaum (1978), discovered that a system with eight outcome basins is stable but one
with 16 possible outcome basins quickly tumbles into deep chaos. The remarkable thing is
that this precipitous rush into an infinite number of outcome basins is universal over all
natural systems so far studied. The Feigenbaum constant gives us the possibility of making
a prediction of the onset of full chaos when the first few period doubling parameters are
known. It defines a key point in postmodern management strategy.

Figure 5. Bifurcation Points from Order to Deep Chaos

Feigenbaum taught us that when the parameters of a causal field change from previous
values, an outcome field expands with elegant regularity even though there may be great
disorder in the whole field. Figure 5 shows the 'cascade' toward full chaos. As one can
see in Figure 5, after one bifurcation (or forking), a system has two outcome basins in
which it might be found (Region A). After two bifurcations, there are four possible
destinies which any normal system may take (Region B). After three, eight outcome basins
which are available (Region C). After four bifurcations, the number of attractors possible
for similar members of a set explode to fill any phase-space available to it (Region D).

The Feigenbaum numbers for phase transitions begin with 3.0 discussed above which produces
a 2n outcome field. The second bifurcation point (3.4495) produces a 4n field, the third
bifurcation (3.56) produces an 8n outcome basin. An outcome field with 16 attractor basins
develop at F_{4} (3.596) to which any given firm, customer, supplier, or worker
could move. After F_{4}, firms are in a situation where a very small change can
produce, not just one or eight new outcome basins but a veritable flood of entirely new
and unexpected, unpredictable end-states toward which a person, a group, a business or a
society might go (Rasmussen and Mosekilde, 1988). That happens when a key parameter
reaches 3.56999 a previous value at which it was semi-stable.

**From Chaos to Order** One of the more interesting implications of Chaos theory for
management science is found in Region D above. One will note that there are several
regions of order in Region D as a whole. The arrows next to E, upper right, point to those
windows of order. Those regions denote the emergence of entirely new organizational forms.
In biology, such forms may be new species giving one a nonlinear theory of evolution. In
sociology, a qualitative increase in food in the early 1700s may have kicked off the
industrial revolution by permitting bifurcations in the division of labor due to the new
food reserves. James Burke offers an excellent PBS documentary (27 Oct., 1991) on that
process (without reference to Chaos theory). Given a division of labor made possible by
food reserves and freed from feudal bondage, new technologies were developed.

Ilya Prigogine took a Nobel prize in 1977 for his work on the emergence of order from
disorder. Such work resolves the contradiction that, while the second Law of
Thermodynamics predicts an increase in disorder, the opposite has occurred in the past
four billion years. The tendency is toward more complex forms. Chaos theory and
Prigogine's explanation (along with Isabelle Stengers, 1984) offers much food for thought
for management science. We return to some of the implications later. Right now we would
like to pause along the route to chaos to offer a mock application of such phase
transitions for heuristic purposes.

**APPLICATIONS** Chaos theory would suggest that, as key parameters in the external
environment change, then people, firms or societies move to occupy differing
regions/attractors in an outcome field. In order to illustrate some of the implications of
Chaos theory for management science, we can take a common problem for university
administration: parking policy.

**Chaos and Parking Basins** If some parking places are reserved for faculty and staff
and some for students, there are two outcome basins to which drivers could go. If places
are reserved for handicapped students, there are three outcome basins to which any given
driver could park.' If a fourth area is reserved for visitors, a fifth for seniors and
graduate students; another for occupational therapists and/or engineering students and
another for maintenance vehicles, the outcome basin available to all drivers is very
complex. Chaos theory would suggest that a parking regime with more than 6 or 8 such
basins would be close to a bifurcation point at which 'illegal' parking would begin to
increase qualitatively.

When key parameters are stable, the system might work--i.e., knowledgeable driver might go
directly to the 'proper' parking area. There would, in the normal course of events, be
some small and random violations of such policy. Chaos teaches us that uncertainty
accumulates with small changes in key parameters. Three examples serve to make the point.

A. Let's assume that most drivers can handle 2 or 3 inches of snow. Three or four inches
of snow would delay some students and force a nonlinear increase in violations as
students, staff and faculty scrambled to get where they were going in time. Two more
inches might greatly increase the number of parking violations; then even 1/2 inch more
might throw the whole system into full blown chaos.

B. Most universities allow ten or fifteen minutes between classes. A large change in the
time allowed between classes might not change the number of parking violators greatly. If,
for example, there were 15 minutes between classes, students and faculty in one class
period could get to their car and vacate a place before another set of arriving students
began to look for places. If the time between classes were reduced by, say two minutes,
there might be a small increase in parking violations. If three minutes, another
relatively small increase might be seen. If one more minute were removed, an otherwise
adequate parking policy might fail and students might begin parking everywhere; on the
sidewalk, double parked in the street, blocking entrances to reserved lots and so forth.
Full chaos would have developed.

C. A parking policy might work on a campus with tightly clustered buildings but after a
few years of expansion, increase in time it takes to walk from car to class has the same
effect as reducing the time between classes. As the sum of the square of each distance
between all classrooms increases, one can expect the onset of chaos in both parking policy
and classroom attendance.

Again, notice that nothing has changed in the design of the car, the parking lot or the
genetics or physiology of the students. The only thing that has changed is a parameter
outside control of students and faculty and a compelling need to park and get to class. In
such a population of students, there will be some who are inconsiderate; they don't care
whether they block others or not. There will be some rich enough to pay for any number of
tickets. These will be the first to ignore policy, but these and all other students who
are otherwise well socialized and distressed about 'illegal' parking will ignore policy.
Taken as a group, all violators will be self-similar to the student body the day before
when each set of parkers went to the 'proper' parking lot.

**POSTMODERN MANAGEMENT SCIENCE** Chaos theory represents a theoretical paradigm which
serves both to critique and supplement Newtonian models of management founded on
linearity, coherence, uniformity, prediction and hierarchical control. The assumptions of
modern science, i.e., assumptions which have been central to the knowledge process for the
past 300 years are greatly modified by new techniques and new insights. In brief, the
linear ontology upon which modern science was grounded has been replaced by a nonlinear
ontology subsumed by the Chaos concept. Chaos theory undermines the logic of tight control
and stability of existing routines that traditional management tends to prefer.

Chaos theory offers management science insight about where and when management control is
reasonable or possible and at what scale of organization such control efforts are best
directed. In short, the dialectics between freedom and necessity vary across causal
fields; given wisdom and judgment, semi-stability can be maintained if key parameters are
modulated such that they do not destroy the connections which hold the system together or
overwhelm the carrying capacity of the environment.

Chaos theory thus adds understanding and support to the logic of many new management
ideas. Current concerns aimed at democratizing decision-making inside the work place find
considerable support in the logics of Chaos theory; no one plan or policy is adequate to
cover all exigencies for all time. Postmodern management science must yield up space to
spontaneous and timely response by workers on the spot. The interest in decentralizing
operations and decision-making also fits within the theoretic envelope of Chaos theory. Up
to a point, an increase in flexibility adds to the overall rationality of a political
economy. 2n, 4n, 8n and 16n outcome basins offer options as well as creative interactions.
After 16n outcome basins, order and prediction are so difficult that even the best
designed systems fail.

**Management Policy and Nonlinear Feedback** The source of semi-stability in any
attractor is to be found in nonlinear feedback. The curious thing is that, if any
management group wishes to fit a firm or agency to the inevitable patterns of change, it
must couple linear change with nonlinear response. Linear amplification causes a torus to
"...blow apart...' to fill the space available to it (Briggs and Peat, 1989:37). One
operative question emerges for management science here. What constitutes a nonlinear
response with which to obtain the quasi-periodic behavior essential to stability in
preferred attractor(s)?

When the environment is fairly stable, then low-level nonlinear feedback suffices. Within
the system, flexible management policies in which rules are moderated by wisdom and
judgment, in which exceptions are made, variations tolerated and competing principles
weighed against each other; all this constitutes low-level nonlinearity. It is the heart
of the kind of quality control which the Deming people encourage.

Objective, uniform and impersonal applications of policies seem necessary in a management
science which privileges linearity and coherency. They also appear to be reasonable in a
society oriented to democratic values. However if such policies amplify deviancy beyond a
Feigenbaum point, nasty surprises may await. Chaos theory implies that differential and
situational policies may promote both stability and democratic values if wisdom and
judgment supplement reason and rationality.

**Managing Chaos** Low level Chaos is not to be managed but to be savored. Postmodern
management science brings into question traditional efforts oriented to tight management
control tactics. Low level chaotic behavior may simply be evidence of a system's adaptive
response to its environment. Low level Chaos implies learning as a system continuously
reaches new points without retracing previous steps. Such behavior is compatible with the
notion of a "learning organization". Conversely, typical repetitive linearized
organizational processes inhibit learning by negative feedback which promotes an extreme
stability and, in the same moment, inhibits the generation of fluctuations which generate
new modes of work or production. One might argue that traditional management practices
hinder, if not eliminate, learning in organizations.

However valuable some low level chaos might be for any system, there comes a time when
chaos interferes with both production and social life. The question of controllability
presents itself with compelling force. At first glance, it seems as if Chaos, by
definition is uncontrollable. Yet recent work suggests that, if we can accept a different
standard for controllability, then human agency is possible (Young, 1992).

**Only Chaos Can Cope with Chaos** In conventional systems theory, open systems can
maintain stability through the incorporation of order from their environment (Bertalanffy,
1968; Buckley, 1968). When a system is mismatched to its environment, there are three
generic solutions by which rematch can be obtained: 1) change the system, 2) change the
environment or 3) recourse to a third system by which to bridge the two (Young, 1969,
1977). As we will see, in the Chaos paradigm, the solutions are a bit more complex but
still depend upon change. H. Ross Ashby (1968:135) had posited that only variety can cope
with variety but what is added to Ashby's Law of Requisite Variety by Chaos theory is the
need to know when and how much variety is needed. The kind and timing of such variety
depends on the characteristic set of complex cycles exhibited by a system. Hübler, (1992)
calls this set the dynamical key to management of chaos.

A major point of interest for administrative science is that, while
system dynamics may, at times, be understood best in terms of the characteristics of
individual persons or firms, qualitative change from one organizational form or process to
another depends upon the ways in which the whole system interacts with its environment. In
brief, there are times when administrative efforts are best oriented to motivate and to
control individual behavior and times when such efforts are best directed at system
parameters. Chaos theory helps illuminate the regions in phase-space in which those very
different management concerns are best focussed. **8
Formal control** Formal control tactics take two forms. First there are
tactical efforts such as the Hübler protocol (1992:18) as well as that of Stephen
Guastello, below. Hübler says that nonlinear physical systems respond to a special class
of aperiodic driving forces. In his words:

The method of dynamical control enables us now to handle some of the most sophisticated, unpredictable systems, including some systems exhibiting chaos, turbulence and catastrophes and to use them for scientific and industrial purposes.

In the Hübler protocol, one identifies the **dynamical key** of a chaotic system,
calculates the difference between that key and semi-stable frequencies or oscillations of
such a system and then adds such values to the key parameters which drive the system. Such
work has been successful in creating solitons efficiently (29). The very sophisticated
mathematics and rationale for such technical control tactics are given in the Hübler
paper. One should note that whatever controllability of chaotic systems is now in place,
it is for physical systems: liquids, lasers, metals and such. Control of nonlinear social
systems may surpass the ingenuity of science since the key parameters are so global and
the world increasingly so interconnected.

**Reinventing Rationality** In a very creative paper, Stephen
Guastello (1992) showed that a specially derived chaotic tactic was more successful in
controlling hiring rates in the construction industry than was a reactive tactic based
upon actual labor needs. Guastello studied 90 hiring 'generations' of a contractor in the
building industry. When the contractor used rules for hiring based upon fixed reactive
rules, his workforce ended up with an unstable and insufficient number of workers.
Guastello developed a nonlinear predictor, N_{t} = Bn_{t-1}e^{(-aN)}.
**9** Guastello calculated the
variations of the workforce with both a linear reactive tactic (actually used) and a
nonlinear tactic based upon his nonlinear predictor. That predictor, in turn, is based
upon the natural variability of the industry modeled by the values indicated. He found
statistical advantages to the nonlinear tactic. In both cases, the chaotic predictor of
employment needs was superior (for reactive management, R^{2}=.77 for the chaotic
predictor function and .57 for the linear comparison model. For long term planning, R^{2
}=.32 for the chaotic function and .14 for the linear comparison).

The implications of the Guastello tactic are profound. If his predictor can be polished
and generalized, it is possible to reinvent rationality in such a way as to obtain better
results in medium term management practices. Add to that, the Hübler work on control of
disorder in deep chaos. If we go back to Figure 5, we will see shadows which cut across
field C. These shadows define the points at which a very careful touch can secure stable
dynamics in even deep Chaos. The ability to re-rationalize management science depends upon
a very different view of rationality than comes from newtonian, euclidean, aristotlean
understandings of rationality. In brief, rational behavior in a chaotic world is loose,
holistic and changeable. Each region of an outcome field requires variable tactics for
whatever intervention is possible for human beings.

**Strategic Control** A second approach for the management of chaos is much more
strategic. It involves a quest for an optimal array of dynamical states available to
workers, firms and economies. This means that one scans the data of performance workers,
firms and whole economies to map out the deep structural **patterns** hidden there.
With this knowledge, one can identify key parameters which push a person, a firm or an
economy into creative or into destabilizing change. The task of the postmodern manager is
to expedite bifurcations which produce desirable attractors and, at the same time, to
control key parameters to stabilize such outcome states for a firm while framing
organizational policy in such a way as to not destabilize the larger political economy.

If we can begin to see, that rationality is fractal and is located as much in the settings
of key parameters of a society or a firm as it is in the minds and motives, genes or
genomes of particular persons, then we come to an understanding that efforts to bring
order, stability, prediction and pattern into the work lives of human beings depends as
much upon the system acting sensibly as it depends upon individual socialization and
training. In such a paradigm, reward or punishment have limited utility; firms succeed or
fail in spite of good efforts or bad. Or, as some quality experts espouse (Deming, 1986),
employees will inevitably fail if the processes that guide their work are flawed.

Management scientists, working out of the chaos paradigm, will want to give some attention
to exigent uncertainties faced by employees. An employee may be able to handle one or two
uncertainties in their social space but the interactive effects of three or more
uncertainties will destabilize the life of even the best, most dependable employee (or
client or supplier). A worker might be able to juggle the uncertainties of family and work
but, given a third uncertainty, say health, interaction between them might escalate into
full chaos for that worker. If such uncertainties are common in the labor force,
uncertainty becomes unmanageable for the administration of the entire workforce taken firm
by firm. Since workers are also customers, clients and suppliers, such collective
uncertainties tend to destabilize the whole system. As national and global economies
consolidate, such interconnections increase.

In the larger society, affirmative action policies, nonlinear in terms of seniority rules,
merit, or equal opportunity may help prevent the amplification of deviancy of the whole
system and thus fractal increase in percentage risk for any given system. Adverse effects
of income and status inequality for the whole system have been well documented in social
research (Currie, Dunn, and Fogarty, 1980). Special educational opportunities for young
Black males, on the face of it, nonlinear in terms of equal gender/racial opportunity and
merit, may be just the nonlinear response which stabilizes the whole system and prevent
key variables from exceeding the fourth feigenbaum number. Head start programs for poor
children, apparently inimical to free market ideas, social darwinism and individualism may
make the market work better in the future by improving education such that next generation
workers might move to other attractor basins (i.e., high tech occupations rather than low
tech). Nonlinear policies, often called social justice programs, are nonlinear in terms of
free market ideas, but they tend to keep key parameters within limits which satisfy two
important needs of any open system; the need for pattern and predictability on the one
side and the need for change and creativity on the other.

If we want to maintain the integrity of a market economy with its many advantages, then
there must be such forms of nonlinear feedback with which to defeat the transformation of
the torus which describes, say, frequency of entry into the market for essential goods and
services by race, class and ethnicity. If not, the market itself may split into 2 or more
attractors: one oriented to those with discretionary income and those without. As income
inequalities between minority groups, economic classes or nations grow, more and more
capital is attracted to the basin of production allocated to luxury goods and less and
less to the production basin which essential goods are produced. When those with
discretionary income determines the price and supply of essential goods and services, more
and more low wage earners are driven out of the market. At some point, low wage earners
will begin to seek alternative ways to increase income or to reduce costs in order to stay
in the marketplace. Some of these alternative ways may be outside the logics of the
marketplace; i.e., they may be nonlinear in terms of a cash economy.

Much strategic effort is outside the direct control of even the most democratic management
effort. The holistic nature of Chaotic dynamics requires a democratic politics which
involve all significant sectors of a population; in a complex society, that means every
adult citizen. One cannot plan on abandoning whole sectors of a population without long
term costs.

**Chaos and Opportunity** Even in a fully chaotic field, there are emergent social
forms which offer great opportunity to an alert management team. The Chaos in the former
USSR, for example, will produce heretofore unimaginable economic forms, some of which may
incorporate the advantages of a market system together with the advantages of democratic
socialism. Indeed, several American economists are working on 'market' socialism today.
There will be much to learn from the chaotic conditions now developing there.

In his Santa Cruz presentation, Hübler made the point that, given stability, large well
administered systems are workable. Given full chaos, small systems are preferred since
they can be more sensitive to local conditions. One would want to develop a politics in
which seed money was used generously to support new practices, products and services. The
ordinary criteria for such loans, grants, shared ventures or investments are set aside in
a time of chaos. One takes risks; some pay off and some don't. There is no present
strategy for knowing which will serve.

The point continues to be made that rationality is as much a feature of the whole system
as it is a capacity contained within the mind and thought of particular workers, students,
administrators or managers. Within attractors in a 1n-basin, predictability hence control
is possible--a loose and tolerant policy of goal assessment and evaluation is sensible. In
the margins between the attractors of 2n, 4n, 8n, or 16n basins, predictability is
difficult for the businesses and persons pushed out to these regions. The most
advantageous ratio between order and disorder is the central problematic of postmodern
management science rather than the quest for order per se.

Management science took on new dimensions when cybernetics, information theory, modern
systems theory, decision theory and other linear-oriented aids in planning and
implementing developed in the 1940s and 1950s as a result of seminal work by Bertalanffy,
Boulding, Buckley and others. Now management scientists are called upon to learn of the
work of Lorenz, Feigenbaum, Mandelbrot and many others who work out of this new paradigm.
The new perspective will inform policy in a great number of areas now untractable.
Problems of crime, bankruptcies, drug use, health care, education and homelessness offer
other fields in which to pursue the implications of Chaos theory. We now sit at the edge
of scientific history and cannot but choose to join.

**Footnotes**

There are four numbering systems of which only one is rational in the technical sense.
The other, simple scaling systems are: nominal, ordinal and interval. The inappropriate
use of rational scaling is a source of much mischief in physical and social science. **Return**

Phase-space is a make believe map of the change of a variable or set of variables over
time. It involves placing the value of a given variable on a grid made up by the
intersection (0) of n variables of interest to research. The mathematician/philosopher
René Descartes (1596-1650) developed the technique hence it is often called a cartesian
map. **Return**

See Briggs and Peat (1989), Chapter 0, for a very accessible treatment of this 'rubber'
math by which fractals are measured; see Holden, (1986) Chapters 13 and 14 for a more
rigorous treatment of fractals. **The Social Dynamicist**, V.3, N.2, June, 1992, offers
ways to estimate five different fractal dimensions: capacity, information, correlation,
near neighbors, and rates of expansion (Lyapunov dimension). Mandelbrot (1977:363)
discusses classical and other methods for evaluating the area occupied by fractals. **Return**

Roger Penrose (1989:152) asserts that, in order for any system to be amenable to the
generation of 'superb' theory, each cycle in its behavior must approximate the previous
cycle with an accuracy of one part in 1014th parts. Indeed, all of modern science is
devoted to the quest for such precision and discards all other patterns of behavior as
'error,' 'faulty observation,' 'inadequate measurement,' or observer bias. Chaos theory
honors such variable behavior as the actual dynamics of the system; the subsequent
theoretical statements made about such systems has the same validity as do 'superb'
theories. Hence we call the study of self similar systems postmodern science in order to
distance it from the privilege accorded precision in the knowledge process by modern
science. **Return**

See Briggs and Peat (1987:58) for a very accessible explanation of the Feigenbaum
numbers. **Return**

Data are from the Communications Division, State of Oklahoma, calendar year 1990, from
the work of L. Douglas Kiel, "Nonlinear Dynamical Analysis: Assessing Systems
Concepts in a Government Agency," **Public Administration Review**, (Nov./Dec.,
1992). **Return**

See 'Chaos Theory and Human Agency in Management Science' in draft. **Return**

Chaos theory has many new concepts with which to grasp its ideas. There is a Glossary
to which one may turn for explanations of such terms as phase-space, fractal or
nonlinearity. **Return**

N = 100

X = number of workers who are entry level (new hires)

Y = number of workers who are advanced level (retained employees) = sX

a = a 'crowding' factor (based upon the number of new contracts)

b = new hire rate

t = hiring generation

s = survival rate = .30

**Return**

**GLOSSARY
**by T. R. Young, Patricia Hamilton and L. Douglas Kiel

**Attractor**: A region in an outcome basin to which the dynamics of a system tends
to take it. The size and shape of the attractor depends, sensitively, upon key parameters
and the dynamics to which it is driven by such parameters. An attractor may occupy space
between dimensions; if so, it is said to be a fractal.

**Attractor, Limit:** A very stable pattern of behavior in which a system moves
between two points in phase-space of which an automobile cruise control or thermostat are
examples.

**Attractor, Point: **The pattern of behavior of a system whose dynamics tend to
converge to one point in phase-space.

**Attractor, Strange:** A strange attractor is simply the pattern, in visual form,
produced by graphing the behavior of a nonlinear system. Since that behavior tends to be
both patterned and unpredictable, it is called strange. If the dynamics are likely to fall
somewhere in a region of phase-space, it is said to be attracted to that shape.

**Attractor, Torus:** An attractor with more than 2 and up to three dimensions in
phase-space shaped a bit like a doughnut. A nonlinear system can move anywhere on or in
the cylinder of the torus but one cannot say just where.

**Basin:** The region in an larger basin of outcomes to which a set of initial
conditions (causes) drives a system or set of similar systems. A system is said to be
'attracted' to that region, hence the pattern of nonlinear dynamics seen in such a basin
is called an attractor. Image a saucer inside which spins a marble. The path of the marble
is the attractor; the whole region is the basin. Since the path is a nonlinear function of
key parameters, that area can be considered a causal basin. There can be n number of such
basins/attractors in a larger outcome field.

**Bifurcation** A doubling of a period of a key parameter. With each doubling, there is
distinct change from one behavioral regime to a new one for all systems affected by that
parameter. After the third bifurcation in key parameter(s), the system tends to move in
ways which fill the space available to it in an outcome basin. This latter state is a far
from stable chaotic state.

**Chaos Theory:** A science which deals with the complex harmonies and dis-harmonies
exhibited by natural and social systems. It is the study of the changing ratio of order
and disorder in an outcome field. That set of foundational ideas which describe the
behavior of complex unpredictable systems. Chaos theory focuses upon states with multiple
periods or without predictable periodicity. Chaos research studies the transitions between
linear and non-linear states of such dynamical systems. [From Chaos, <L. <G. abyss;
from which our word, chasm also comes. The presumed original state of disorder of the
unformed world. **Funk and Wagnalls Dictionary.**]

**Dynamical Key:** Each attractor has its own characteristic set of cycles. If one can
identify that set and match it, then it is possible to affect the behavior of the system
(Hübler, 1992:18).

**Fractals**: From the Latin, **fractus,** broken (frangere, to break). A
measurement of the degree to which a body takes up space available it; an estimate of its
efficiency in using the space it occupies. In more simple terms a fractal is a measure of
the irregularity of an object. Mandelbrot uses fractal as a generic term applicable to all
mappings of system dynamics in phase space.

**Key Parameter:** Any factor which affects the behavior of a system. Food supply is a
key parameter which affect the population growth and decline of animals as are climate and
predators.

**Linearity **A linear system is one in which cause and effect are related to each
other in a proportional way. For instance, if one pound makes a rubber band stretch twice
its length; a two pound weight will make it stretch four times its length. For many
systems, causality is curvilinear; a given cause will have an effect that is smaller with
each additional doubling of it. For many systems, the addition of the nth doubling will
cause it to transform into non-linearity. Nuclear fission is linear up to the point of a
critical mass. Nonlinearity marks the onset of chaos.

Nonlinearity: A pattern of behavior in which a change is out of proportion to the value of
a driving force. There are three generic forms of nonlinear change which a researcher may
chart. The simplest form is:

**First Order Change: Self-similarity**: A self similar system tends to occupy about
the same region in phase-space but never exactly the same. This form of change is the most
stable and most congenial to the human need for pattern on the one side and flexibility on
the other. A torus is a good example of a system displaying similarity but not sameness.

**Second Order Change: Bifurcation** A doubling of a key parameter of a system. With
each doubling, there is discernible change from one behavioral regime to a new one. This
change is qualitatively different from self-similarity since a system now has two (or
more) causal basins to which it might move. Each basin is very different yet still one can
discern some local similarity.

**Third Order Change: Chaos **After the third bifurcation in key parameters, the system
tends to behave in ways which fill the space available to it in an outcome basin. This
latter state is a 'far from stable' chaotic state. Entirely new forms of order arise in
such an outcome field.

**Outcome Basin: **The range of alternative outcomes available to a nonlinear system at
any given stage in its behavior. Simple systems such as a pendulum have a point to which
they are attracted; more complex systems such as a thermostat are limited to a slightly
larger region in phase-space. See limit attractor.

**Ontology** That which exists; the study of that which exists. Modern science sees
ontology as that which exists in and of itself. The ontological base of modern science was
deemed to act as a deterministic system. In modern social science, the ontology of the
natural and physical world was thought to exist apart from the will, perceptions or
activities of either the people studied or the observing scientist. The geometry of such
objects were thought to be euclidean; either they existed as points, planes or solids or
they didn't exist. The postmodern scientific view is that dynamics may be and often are
nonlinear; that geometries may be and usually are fractal; that the scientist shapes the
world as it is studied. Often knowledge reenters the world from which it came as part of a
self fulfilling prophecy which changes the reality quotient of such worlds.

**Phase-space**: A Cartesian map of the dynamics of a system (or a set of systems)
created in n-dimensional space by turning time-series data into a picture (above called an
attractor) showing its topology.

**Postmodern** came into use in the 1950s in reference to architectural styles as a
protest to the cubes, circles, pyramids and cones that marked modern architecture. The
term was extended to literary critique of all pretensions at universal standards for
novels, poetry, and composition. It use spread to art criticism; to critique of music and
theater. Now it is used to denote an era in which all absolutes; all universals; all
claims to objective Truth and all pretensions of perfection are challenged. Such a
perspective fits excellently well into the ontological paradigm defined by nonlinear
dynamics since there are no centers, absolutes or final states to which systems
'naturally' evolve.

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