Out
of Control: The New Biology of Machines,
Social Systems and the Economic World
By Kevin Kelly
Addison-Wesley,
Reading, MA, 1994
ABSTRACT - This
ground-breaking, insightful work pulls important new pattern- building
findings from fields as diverse as computer science, biology, physics, and economics, relates
them to the new worlds of complexity, chaos theory, and post-Darwin evolution and lays out
the implications for creating complex organizations and systems of all types. Many of his
findings are contrary to management traditions and practices.
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Key Points:
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As organizations
become more complex and the need for adaptability
increases, leaders will need to adopt lessons from nature’s complex
systems (such as the critical role of variation and imperfections), which,
in many cases, suggest non-traditional approaches to leadership and
organization building.
- Complex systems (organizations) need to
be built up incrementally from
simple systems which work.
- Suggests that co-evolution, collaboration
among organizations is a better
strategy for insuring long-term survivability and stability than competition
- Provides guidance to organizations from
nature’s complex systems:
distributed; decentralized; collaborative; adaptive.
- Learn and follow the principles of evolution,
like punctuated equilibrium,
instead of trying to engineer the development of complex organizations.
- The powerful link between learning and
successful evolution is stressed
- Complex systems have the power to make
large scale change through
large, rather than incremental shifts.
- There is a desired number of connections
among components of a
system. This helps an organization live on the edge between chaos and
stability and thus insure is survivability.
- Makes a case for growth as natural law
- presents seven trends
underlying this organic evolution.
- Despite the complexity of systems, certain
types of prediction are
possible. This, along with organizational flexibility achieved though
decentralization and redundancy, foster successful adaptation.
Summarizes principle
ideas from the book which apply to the creation of
complex organizations
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Hive Mind
Key Point: As
organizations become more complex and the need for
adaptability increases, leaders will need to adopt lessons from nature’s complex
systems (such as the critical role of variation and imperfections), which, in many
cases, suggest non-traditional approaches to leadership and organization
building.
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- "It seems that the things we find
most interesting in the universe are all
dwelling near the web end...The class of systems to which all of the
above belong is variously called: networks, complex adaptive systems,
swarm systems, vivisystems, or collective systems. Organizationally,
each of these is a collection of many (thousands) of autonomous
members. "Autonomous" means that each member reacts individually
according to internal rules and the state of its local environment. This is
opposed to obeying orders from a center, or reacting in lock step to the
overall environment. These autonomous members are highly connected
to each other, but not to a central hub. They thus form a peer network.
Since there is no center of control, the management and heart of the
system are said to be decentrally distributed within the system, as a hive
is administered. ...One theme of his book is that distributed artificial
vivisystems...provide people with some of the attractions of organic
systems, but also some of the drawbacks." P. 21-22
- Benefits of swarm systems - adaptable,
evolvable, resilient, boundless.
p. 22-23
- Disadvantages of swarm systems - non-optimal,
non-controllable, non-
predictable, non-understandable, non-immediate. p. 23-24
- "As our inventions shift from the
linear, predictable, causal attributes of
the mechanical motor, to the crisscrossing, unpredictable, and fuzzy
attributes of living systems, we need to shift our sense of what we
expect from our machines (or organizations, my note). p. 24
- A simple rule of thumb may help: For jobs
where supreme control is
demanded, good old clockware is the way to go. Where supreme
adaptability is required, out-of-control swarmware is what you want."
p. 24
"The inefficiencies
of a network - all that redundancy and ricocheting vectors,
things going from here to there and back just to get across the street -
encompassing imperfection rather than rejecting it. A network nurtures small
failures in order that large failures don’t happen as often. It is its capacity to hold
error rather than scuttle it that makes the distributed being fertile ground for
learning, adaptation, and evolution." p. 26
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Machines with
an Attitude
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Key Point:
Complex systems (like organizations) need to be built up
incrementally from simple systems which work.
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- "When something works, don’t
mess with it; build on top of it." p. 39
- "A brain and body are made up the
same way. From the bottom up.
Instead of towns, you begin with simple behavior - instincts and
reflexes. You make a little circuit that does a simple job, and you get a
lot of them going. Then you overlay a secondary level of complex
behavior that can emerge out of that bunch of working reflexes. The
original level keeps working whether of not the second layer work or
not. But when the second layer manages to produce more complex
behavior, it subsumes the action of the layer below it. Here is the
generic recipe for distributed control...It can be applied to most
creations: 1. Do simple things first. 2. Learn to do them flawlessly. 3.
Add new layers of activity over the results of the simple task. 4. Don’t
change the simple things. 5. Make the new layer work as flawlessly as
the simple. 6. Repeat, ad infinitum. This script could also be called a
recipe for managing complexity of any type, for that is what it is." p. 41
- "In the human management of distributed
control, hierarchies of a
certain type will proliferate rather than diminish...While authoritarian
"top-down" hierarchies will retreat, no distributed system can survive
for long without nested hierarchies of lateral "bottom-up" control. As
influence flows peer to peer, it coheres into a chunk- a whole organelle -
which then becomes the bottom unit in a larger web of slower actions.
Over time a multi-level organization forms around the percolating-up
control: fast at the bottom, slow at the top. The second important
aspect of generic distributed control is that the chunking of control must
be done incrementally from the bottom. It is impossible to take a
complex problem and rationally unravel the mess into logical interacting
pieces. Such well-intentioned efforts inevitably fail." p. 45
"The law is
concise: Distributed control has to be grown from simple local
control. Complexity must be grown from simple systems that already work." p.
46
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Co-evolution
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Key Point:
Suggests that co-evolution, collaboration among organizations is a
better strategy for insuring long-term survivability and stability than competition.
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- "Here’s the news: half of the
living world is codependent!...The surge of
alliance-making in the 1990s among large corporations...is another facet
of an increasing, co-evolutionary economic world. Rather than eat or
compete with a competitor, the two form an alliance - a symbiosis." p.
75
- "Paul Ehrlich sees co-evolution pushing
two competitors into "obligate
cooperation." He wrote, " It’s against the interests of either predator or
prey to eliminate the enemy" That is clearly irrational, yet that is clearly a
force that drives nature." p. 76
- "Every complex adaptive organization
faces a fundamental tradeoff. A
creature must balance perfecting a skill of trait (building up legs to run
faster) against experimenting with new traits (wings). It can never do all
things at once. This daily dilemma is labeled the tradeoff between
exploration and exploitation." p. 87
"It turns
out that no matter what clever strategy you engineer or evolve in a
world laced by chameleon-on-a-mirror loops, if it is applied as a perfectly pure
rule that you obey absolutely, it will not be evolutionary resilient to competing
strategies. That is, a competing strategy will figure out how to exploit your rule
in the long run. A little touch of randomness (mistakes, imperfections), on the
other hand, actually creates long-term stability in co-evolutionary worlds by
allowing some strategies to prevail for relative eons by not being so easily
aped." p.89
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- "The highly connected loops of co-evolutionary
conflict mean the whole
can reward (or at times cripple) all members. Axelrod told me, "One of
the earliest and most important insights from game theory was that
nonzero-sum games had very different strategic implications than zero-
sum games. In zero-sum games whatever hurts the other guy is good for
you. In non-zero-sum games you can both do well, or both do poorly.""
p. 89
- "Perhaps the most useful lesson of
co-evolution for "wannabe" gods is
that in co-evolutionary worlds control and secrecy are
counterproductive. You can’t control, and revelation works better than
concealment. "In zero-sum games you always try to hide your strategy,"
says Axelrod. "But in non-zero-sum games you might want to announce
your strategy in public so the other players need to adapt to it."" p. 90
"In the Network
Era - that age we have just entered - dense communications is
creating artificial worlds ripe for emergent co- evolution, spontaneous self-
organization, and win-win cooperation. In this Era, openness wins, central
control is lost, and stability is a state of perpetual almost-falling ensured by
constant error." p. 90
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Network
Economics
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Key Point:
Provides guidance to organizations from nature’s complex systems:
distributed; decentralized; collaborative; adaptive.
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- "The challenge is simply stated: Extend
the company’s internal networks
outward to include all those with whom the company interacts in the
marketplace. Spin a grand web to include employees, suppliers,
regulators, and customers, they; they all become part of your
company’s collective being. They are the company." p. 188
- "One can imagine the future shape
of companies by stretching them until
they are pure network. a company that was pure network would have
the following traits: distributed, decentralized, collaborative, and
adaptive. p. 189
- "Distributed - There is not
single location of the business. It dwells
among many place concurrently." p. 189
- "Decentralized - Now, when
the economy shifts daily, owning the
whole chain of production is a liability....In short, networks make
outsourcing feasible, profitable, and competitive." p. 191
- "Collaborative - Networking
internal jobs can make so much
economic sense that sometimes vital functions are outsourced to
competitors, to mutual benefit. Enterprises may be collaborators on one
undertaking and competitors on another, at the same time....The
metaphor for corporations is shifting from the tightly coupled, tightly
bounded organism to the loosely coupled, loosely bounded ecosystem."
p. 193
"Adaptive
- "DESPITE MY SUNNY FORECAST for the network economy,
there is much about it that is worrisome. These are the same concerns that
accompany other large decentralized, self-making systems: *You can’t
understand them. *You have less control. *They don’t optimize well." p. 194
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Artificial
Evolution
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Key Points: Learn
and follow the principles of evolution, like punctuated
equilibrium, instead of trying to engineer the development of complex
organizations.
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- "To scientists, the most exhilarating
news to come out of Ray’s artificial
evolution machine is that his small worlds display what seems to be
punctuated equilibrium. For relatively long periods of time, the ratio of
populations remain in a steady tango of give and take with only the
occasional extinction or birth of a new species. Then, in a relative blink,
this equilibrium is punctuated by a rapid burst of rolling change with
many newcomers and eclipsing of the old. For a short period change is
rampant. Then things sort out and stasis and equilibrium reigns again.
The current interpretation of fossil evidence on Earth is that this pattern
predominates in nature. Stasis is the norm; change occurs in bouts." p.
289
- "There are only two ways we know of
to make extremely complicated
things," says Hillis. "One is by engineering, and the other is evolution.
And of the two, evolution will make the more complex." p. 295
- "Little dumb creatures in parallel
that can "write" better software than
humans can suggests to Ray a solution for our desire for parallel
software....When it comes to distributed network kinds of things, Ray
says, "Evolution is the natural way to program." The natural way to
program! That’s an ego-deflating lesson. Humans should stick to what
they do best: small, elegant, minimal systems that are fast and deep. Let
natural evolution (artificially injected) do the messy big work." p. 308
- "The trouble of evolution is not entirely
out of our control; surrendering
some control is simply a tradeoff we make when we employ it. The
things we are proud of in engineering - precision, predictability,
exactness, and correctness - are diluted when evolution is introduced.
These have to be diluted because survivability in a world of accidents,
unforeseen circumstances, shifting environments - in short, the real
world - demands a fuzzier, looser, more adaptable, less precise stance.
Life is not controlled. Vivisystems are not predictable. Living creatures
are not exact." p. 310
"Give up control,
and we’ll artificially evolve new worlds and undreamed-of
richness. Let go, and it will blossom." p. 311
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The
Structure of Organized Change
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Key Point: The
powerful link between learning and successful evolution is
stressed.
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- "Despite the confusion about the word
"evolution," our strongest terms
of change are rooted in the organic: grow, develop, mutate, learn,
metamorphose, adapt. Nature is the realm of ordered change. p. 353
"Only in the
last couple of years has the exhilarating link between learning,
behavior, adaptation, and evolution even begun to be investigated...A number of
researchers...have shown clearly and unequivocally how a population of
organisms that are learning - that is, exploring their fitness possibilities by
changing behavior - evolve faster than a population that are not learning." p. 358
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Post-Darwinism
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Key Point: Complex
systems have the power to large scale change through
large, rather than incremental shifts.
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- "As the French evolutionist Pierre
Grasse said, "Variation is one thing,
evolution quite another; this cannot be emphasized strongly
enough...Mutations provide change, but not progress." So while natural
selection may be responsible for microchange - a trend in variations -
no one can say indisputably that it is responsible for macrochange - an
open-ended creation of an unexpected novel form and progress toward
increasing complexity." p. 370
- "But intriguing suspicions now accumulating
in the study of complex
systems, particularly complex systems that adapt, learn, and evolve,
suggest Darwin was wrong in his most revolutionary premise. Life is
largely clumped into parcels and only mildly plastic. Species either
persist of die. They transmute into something else under only the most
mysterious and uncertain conditions. By and large, complex things fall
into categories and the categories persist. Human institutions clumps -
churches, departments, companies - find it easier to grow than to
evolve.
"Required
to adapt too far from their origins, most institutions will die. "Organic"
entities are not infinitely malleable because complex systems cannot easily be
gradually modified in a sequence of functional intermediates. A complex system
is severely limited in the directions and ways it can evolve, because it is a
hierarchy composed entirely of sub- entities, which are also limited in their room
for adaptation because they are composed of sub-sub-entities, and so on down
the tower. It should be no surprise, then, to find that evolution works in quantum
steps. The given constituents of an organism can collectively make this or that,
but not everything is between this and that. The hierarchical nature of the whole
prevents it from reaching all the possible states it might theoretically hit. At the
same time, the hierarchical arrangement of the whole gives it power to make
some large-scale shifts." p. 381-382
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The
Butterfly Sleeps
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Key Point:
There is a desired number of connections among components of a
system. This helps an organization live on the edge between chaos and stability
and thus insure its survivability.
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- "Deep down Kauffman felt that his
systems built themselves. In some
way he hoped to discover, evolutionary systems controlled their own
structure. From the first glimpse of his visionary network image, he had
a hunch that in those connections lay the answer to evolution’s self-
governance." p. 398
- "As Kauffman increased the average
number of links between nodes,
the system became more resilient, "bouncing back" when perturbed.
The system could maintain stability while the environment changed. It
would evolve. The completely unexpected finding was that beyond
certain level of linking density, continued connectivity would only
decrease the adaptability of the system as a whole....In the long run, an
overly linked system was as debilitating as a mob of uncoordinated
loners" p. 399
- "At the ideal number of connections,
the ideal amount of information
flowed between agents, and the system as a whole found the optimal
solutions consistently. If their environment was changing rapidly, this
meant that the network remained stable - persisting as a whole over
time." p. 400
- "He (Langton) says that systems that
are most adaptive are so loose
they are a hairsbreadth away from being out of control. Life, then, is a
system that is neither stagnant with non-communication nor grid-locked
with too much communication. Rather life is a vivsystem tuned "to the
edge of chaos" - that lambda point where there is just enough
information flow to make everything dangerous." p. 402
"Self-tuning
may be the mysterious key to evolution that doesn’t stop - the holy
grail of open-ended evolution. Chris Langton formally describes open-ended
evolution as a system that succeeds in ceaselessly self- tuning itself to higher and
higher level of complexity, or in his imagery, a system that succeeds in gaining
control over more and more parameters affecting its evolvability and staying
balanced on the edge." p. 403
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Rising
Flow
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Key Point:
Makes the case for growth as a natural law and presents seven
trends underlying this organic evolution.
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- "The search for a Second Law of Biology,
a law of rising order, is
unconsciously behind much of the search for deeper evaluations and the
quest for hyperlife." p. 405
- "The order accumulated by the rising
wave serves as a plank to extend
itself, using energy from outside, into the next realm of further order. As
long a Carnot’s force flows downhill and cools the universe, the rising
flow can steal heat to flow uphill in places, building itself high by pulling
on its bootstraps." p. 405
- "Caveats aside, I discern about seven
large trends or directions
emerging from the ceaseless, hourly toil of organic evolution. These
trends, as far as anyone can tell, are also the seven trends that will bias
artificial evolution when it goes marathon; they may be said to be the
Trends of Hyper-evolution: Irreversibility, Increasing Complexity,
Increasing Diversity, Increasing Number of Individuals, Increasing
Specialization, Increasing Codependency, Increasing Evolvability." p.
412
"Evolution
is a conglomeration of many processes which form a society of
evolutions. As evolution has evolved over time, evolution itself has increased in
diversity and complexity and evolvability." p. 417
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Prediction
Machinery
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Key Point:
Despite the complexity of systems, certain types of prediction are
possible. This, along with organizational flexibility achieved though
decentralization and redundancy, foster successful adaptation..
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- "....prediction is a form of control.
It is a type of control particularly
suited to distributed systems. By anticipating the future, a vivisystem can
shift its stance to preadapt to it, and in this way control its destiny. John
Holland says, "Anticipation is what complex adaptive systems do."" p.
420
- "...the character of chaos carries
both good news and bad news. The
bad news is that very little, if anything, is predicable far into the future.
The good news - the flip side of chaos - is that in the short term, more
may be more predictable than it first seems...."There is order is chaos.""
p. 424
- "The short answer is that tiny errors
(caused by limited information)
compound into grievous errors when extended very far into the future."
p. 426
- "Most of the time most of a complex
system may not be forecastable,
but some small part of it may be for short times." p. 428
- "...the work of Theodore Modis, whose
1992 book, Predictions,
nicely sums up the case for utility and believability of predictions. Modis
addresses three types of found order in the greater web of human
interactions. Each variety forms a pocket of predictability at certain
times.
- Invariants. The natural and unconscious
tendency for all organisms to
optimize their behavior instills in that behavior "invariants" that change
very little over time...
- Growth Curve. Growing things share
several universal characteristics.
Among them are a lifespan that can be plotted as an S-shaped curve:
slow birth, steep growth, slow decline..."What is hidden under the
graceful shape of the S-curve is that fact that natural growth obeys a
strict law." This law says that the shape of the ending is symmetrical to
the shape of the beginning...
- Cyclic Waves. According to Modis,
cyclic phenomenon in nature can
infuse a cyclic flavor to systems running within it." p. 436-437
- "Together, these three modes of prediction
suggests that at certain
moments of heightened visibility, the invisible pattern of order becomes
clear to those paying attention." p. 437
- "...we know that feedback loops alone
are insufficient to breed the
behaviors of the vivissystems we find most interesting. There are two
additional types of complexity (there may be others) the researchers in
this book have found necessary in order to give birth to a full spectrum
of vivisystem character: distributed being and open-ended evolution..."
p. 448
- "The key insight uncovered by the
study of complex systems in recent
years is this: the only way for a system to evolve into something new is
to have a flexible structure...This is why distributed being is so important
to learning and evolving systems. A decentralized, redundant
organization can flex without distorting its function, and this it can adapt.
It can manage change. We call that growth. Direct feedback
models...can achieve stabilization - one attribute of living systems - but
they can’t learn, grow, diversity - three essential complexities for a
model of changing culture or life." p. 448
"But
we cannot import evolution and learning without exporting control." p. 448
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The
Nine Laws of God
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Key Point:
Summarizes principle ideas from the book which apply to the
creation of complex organizations.
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- "Out of nothing, nature makes something....How
do you make
something from nothing? Although nature knows this trick, we haven’t
learned much just by watching her. We have learned more by our
failures in creating complexity and by combining these lessons with small
successes in imitating and understanding natural systems. So from the
frontiers of computer science, and the edges of biological research, and
the odd corners of interdisciplinary experimentation, I have compiled
The Nine Laws of God governing the incubation of somethings from
nothing...These nine laws are the organizing principles that can be found
operating in systems as diverse as biological evolution and SimCity.
- Distribute being. The spirit of
a beehive, the behavior of an economy,
the thinking of a supercomputer, and the life in me are distributed over a
multitude of smaller units (which themselves may be distributed). When
the sum of the parts can add up to more than the parts, then that extra
being (that something from nothing) is distributed among the parts...All
the mysteries we find most interesting - life, intelligence, evolution - are
found in the soil of large distributed systems.
- Control from the bottom up. When
everything is connected to
everything in a distributed network, everything happens at once. When
everything happens at once, wide and fast moving problems simply
route around any central authority. Therefore overall governance must
arise from the most humble interdependent acts done locally in parallel,
and not from a central command...
- Cultivate increasing returns. Each
time you use an idea, a language,
or a skill you strengthen it, reinforce it, and make it more likely to be
used again...Anything which alters its environment to increase
production of itself is playing the game of increasing returns. And all
large, sustaining systems play at the game...Life on Earth alters Earth to
beget more life...
- Grow by chunking. The only way to
make a complex system that
works is to begin with a simple system that works. Attempts to instantly
install highly complex organization...without growing it, inevitably lead to
failure... Complexity is created then, by assembling it incrementally from
simple modules that can operate independently.
- Maximize the fringes. In heterogeneity
is creation of the world. A
uniform entity must adapt to the world by occasional earth-shattering
revolutions, one of which is sure to kill it. A diverse heterogeneous
entity, on the other hand, can adapt to the world in a thousand daily
mini-revolutions, staying in a state of permanent, but never fatal
churning. Diversity favors borders, the outskirts, hidden corners,
moments of chaos, and isolated clusters. In economic, ecological,
evolutionary, and institutional models, a healthy fringe speeds
adaptation, increases resilience, and is almost always the source of
innovations.
Pursue no optima;
have multiple goals. Simple machines can be efficient, but
complex adaptive machinery cannot be...Rather than strive for optimization of
any function, a large system can only survive by "satisficing" (making "good
enough") a multitude of functions. For instance, an adaptive system must trade
off between exploiting a known path of success (optimizing a current strategy),
or diverting resources to exploring new paths (thereby wasting energy trying less
efficient methods)....forget elegance; if it works, it’s beautiful.
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- Seek persistent disequilibrium.
Neither consistent nor relentless change will support a creation. A good
creation, like good jazz, must balance the stable formula with frequent out-of-kilter notes... A Something
is persistent disequilibrium - a continuous state of surfing forever on the edge between never stopping
but
never falling....
Change change
itself. When extremely large systems are built up out of complicated systems, then each system
begins to influence and ultimately change the organization of other systems." p. 468-471
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1. Thoughts
