Between Order and Chaos
James Odell, James Odell Associates, Ann Arbor, U.S.A. |
 |
COLUMN
PDF Version |
Nature has moments both of order and chaos. Interestingly enough,
those forms that are considered most fit actually reside someplace
in between. In fact many consider this in-between state a necessary
property of emergence for nature. Such a phenomenon applies to business
and software agents, as well.
1 FIRST, BASIC AGENT BEHAVIOR
One of the earliest form of agents is called a cellular automata (CA).
The idea was originally conceived by the Polish mathematician Stanislaw
Ulam in the early 1950s and further developed by John von Neumann and
Arthur Brooks. Basically, a CA consists of a lattice of cells, or sites.
Each cell has a state whose value is commonly expressed as 0 or 1,
black or white, on or off, or a color selected from a set of colors.
At discrete “ticks” of the CA clock, this value is updated
according to a set of rules that specifies how the state of each cell
is computed from its present value and the values of its neighbors.
The
most familiar example is John Conway’s game, Life. As described
in the October 1970 issue of Scientific American, only a checkerboard
and an ample supply of markers are needed. The rules of Life are simple:
- A dead cell (state 0), with exactly three of its eight
immediate neighbors alive (state 1), is born. Under the right conditions,
the cell comes
alive.
- A living cell with two or three living neighbors remains
alive, that is, the cell stays alive when nurtured by its neighbors
to the right
extent.
- All other cells die (or remain dead) due to overcrowding
or loneliness.
- Each cell is updated once per time period.
The checkerboard rules represent
the laws of physics (or life) and, while the cells themselves are
not mobile, an amazing amount of behavior
emerges. Figure 1(a) depicts how a CA society can die out over three
generations. Figure 1(b), on the other hand, shows how a society
can form a fixed configuration. Lastly, Fig. 1(c) illustrates how some
patterns oscillate indefinitely.
Figure 1 Some examples of Life patterns.
2 CLASSIFYING AGENT BEHAVIOR
Over the long run, CA societies have
similar kinds of emergent behavior. The patterns of Fig. 2 illustrate
the four classes of behavior identified
by Stephen Wolfram in 1983 when he was at Princeton’s Institute
for Advanced Studies. Class I societies are those that exhibit a static,
or limit point, behavior. Figures 1(a) and 1(b) are examples
of this Class, because the lattice will not change after generation
3. Class
II societies exhibit periodic, or limit cycle, behavior which
is the indefinite oscillation depicted in Fig. 1(c).
Class I and II
can be considered one extreme of CA behavior because
everything is predictable and orderly. Class III on the other hand
is aperiodic, or chaotic; that is, its structures display
no obvious order or uniformity. In between these extremes, is a mysterious
and
complex class of behavior: Class IV. Such automata exhibit considerable
local organization, yet also have areas of irregular behavior. In other
words, Class IV automata are some place in between the two extremes:
they exhibit orderly behavior as well as some chaotic behavior. (Images
are courtesy of Andrew Wuensche, generated using his software “Discrete
Dynamic Lab” from http://www.santafe.edu/~wuensch/ddlab.html)
[1].
3 ORDER VERSUS CHAOS
Cellular Automata offer a way to model natural
and artificial processes, such as modeling crystallization, complex
fluid flows, chemical reactions,
and hardware architecture. Yet, CA involve an elementary form of
agent. Imagine the kinds of systems that can be built with agents
that are
mobile and have sophisticated forms of communication and interaction.
Such agent systems not only provide a richer way of modeling natural
and artificial processes but provide a way of implementing such
systems, as well.
Figure 2 — Wolfram’s four classes of long-run behavior.
Such mature agents systems are still subject to the same Wolfram behavior.[2]
You can build agents systems that are orderly (Class I and II), and
such orderly behavior is appropriate for some kinds of systems. However,
when agents are expected to learn and change their behavior, an orderly
system discourages change. In a business example, all the jobs in an
organization would be subdivided so that employees have no latitude
and only do the job for which they are hired. For an automated supply-chain
system, the results would always be predictable. On the surface, such
predictability would seem to be a good thing. However, when the business
landscape changes (as it often does), the supply-chain operation would
no longer suit the organization’s needs. Instead, it would be
predictably wrong. In both of these scenarios, everybody would benefit
if the individual agents had the freedom to change. In short, orderly
agent systems should become more fluid—and a bit closer to chaos.
Figure
3 — Complex systems poised between order and chaos are best able
to carry out ordered yet flexible behaviors.
Conversely, if agents are deep in a chaotic regime (Class III), they
can never get the job done. For example, employees who do not know
what they’re supposed to do half the time end up working at cross
purposes. A supply-chain system would not be able to deliver the right
product, to the right person, at the right time. In both of these scenarios,
if the individual agents could have tighter connections with fewer
individuals, a greater degree of stability would be introduced. Chaotic
agents, then, should become less fluid by adapting to what other agents
are doing, resulting in aggregate behavior. This means pulling back
from chaos.
4 THE EDGE OF CHAOS
Neither order nor chaos seems to be the best
place for complex systems—whether
their agents are implemented using software, hardware, machines, or
people. Instead, such agent systems need to be someplace in between.
With too much order, the system stagnates and dies in the face of new
competition that needs to be only a little bit better. With too much
chaos, the system will not survive because it can not make useful products.
The edge of chaos is on average where fitness is best (Fig. 3). Such
systems can exploit what they have learned, as well as extend that
learning through exploration.
Complex systems (including both living
and business systems) are characterized by perpetual novelty. This
approach can be scary: things can get out
of control and errors will be made. Yet without this kind of approach,
there will be no change—only status quo. To talk about complex
adaptive systems being in equilibrium is meaningless because the system
never gets there. It is always unfolding, always in transition. If
a system ever reaches equilibrium, it is not just stable—it is
dead.
Now, I am not suggesting that such complex systems be built immediately.
In fact, this would probably result in chaos itself. Complex systems
should be built simply at first, initially (and gradually) placing
any edge-of-chaos processing with human agents rather than automated
agents.
The reasons for this are technical and psychological. Technically,
we do not yet fully understand how to build complex systems that function
properly. We lack both a systematic methodology and industrial-strength,
agent-system toolkits. Psychologically, living on the edge of chaos
can make us uncomfortable. And, when we must delegate our tasks to
automated agents, we will feel even more out of control. It’s
bad enough when people are intimidated by their home appliances. What
will happen when automated agents choose the articles we read, automatically
answer our mail, and schedule appointments? On top of this, imagine
how uneasy we will feel when automated agents begin making critical
business decisions and acting on them. Confidence and understanding
come slowly.
5 EDGE OF CHAOS CONCLUSION
Stability is probably something valued
in accounting and payroll systems. However, the next generation business
systems should be operating on
the edge of chaos. Order entry, inventory control, and supply chain
systems are particularly appropriate. These are systems whose agents
are people, machines, and software. To work effectively, these agents
must work together as a living system: requiring flux, change, and
the forming and dissolving of patterns.
Complex systems theory points
us away from isolated units and toward interactions between individuals
and their environment. Strategy focuses
on the management of volatility, not the achievement of specific goals.
Growth comes from agent relationships and rules rather than through
a significant increase in size. Opposing thoughts or points of view
are held simultaneously. Mild instability is encouraged. Build something
workable, rather than “optimal.” Developing complex systems
is not for the faint-hearted, which applies to both executives and
IT system developers. We need to unleash our software and let it grow
and learn like any living system. Only then can our systems mature
beyond our limitations—and exceed our expectations.
A greater
kind of courage and a different psychology is now required—to
be willing to let go and experience the creativity, innovation and
disturbance which comes about when we risk the outer boundaries of
trying to maintain a balance and the excitement of living, developing
and coaching at the “edge of chaos.” Learning will perhaps
ultimately prove less valuable in the third millennium than the skill
and attitudes of unlearning—in the same way that knowing what
to do may become far less important than knowing what to do when you
no longer know what to do.
-- Petruska Clarkson, psychologist, a chartered consultant in UK
REFERENCES
[1] Coveney, Peter, and Roger Highfield, Frontiers of Complexity:
The Search for Order in a Chaotic World, Fawcett Columbine, New
York, 1995.
[2] Wolfram, S., A New Kind of Science. 2002, Champaign,
IL: Wolfram Media.
About the author James J. Odell is a consultant, writer, and educator in the areas
of object-oriented and agent-based systems, business reengineering,
and complex adaptive systems. He has written four books on object orientation
and has two books in progress on agent-based system design. His website
is http://www.jamesodell.com.
About the author
|
 |
James J. Odell is a consultant, writer,
and educator in the areas of object-oriented and agent-based systems,
business reengineering,
and complex adaptive systems. He has written four books on object orientation
and has two books in progress on agent-based system design. His website
is http://www.jamesodell.com.
|
Cite this column as follows: James Odell: “Between Order and
Chaos”, in Journal of Object Technology, vol. 2, no.
6, November-December 2003, pp. 45-50. http://www.jot.fm/issues/issue_2003_11/column4
|
