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There are two extreme ways to structure "moreness." At one extreme,
you can construct a system as a long string of sequential operations,
such as we do in a meandering factory assembly line. The internal logic
of a clock as it measures off time by a complicated parade of movements
is the archetype of a sequential system. Most mechanical systems follow
the clock.
At the other far extreme, we find many systems ordered as a
patchwork of parallel operations, very much as in the neural network of
a brain or in a colony of ants. Action in these systems proceeds in a
messy cascade of interdependent events. Instead of the discrete ticks of
cause and effect that run a clock, a thousand clock springs try to
simultaneously run a parallel system. Since there is no chain of
command, the particular action of any single spring diffuses into the
whole, making it easier for the sum of the whole to overwhelm the parts
of the whole. What emerges from the collective is not a series of
critical individual actions but a multitude of simultaneous actions
whose collective pattern is far more important. This is the swarm model.
These two poles of the organization of moreness exist only in theory
because all systems in real life are mixtures of these two extremes.
Some large systems lean to the sequential model (the factory); others
lean to the web model (the telephone system).
It seems that the things we find most interesting in the universe
are all dwelling near the web end. We have the web of life, the tangle
of the economy, the mob of societies, and the jungle of our own minds.
As dynamic wholes, these all share certain characteristics: a certain
liveliness, for one.
We know these parallel-operating wholes by different names. We know
a swarm of bees, or a cloud of modems, or a network of brain neurons, or
a food web of animals, or a collective of agents. The class of systems
to which all of the above belong is variously called: networks, complex
adaptive systems, swarm systems, vivisystems, or collective systems. I
use all these terms in this book.
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.
There are four distinct facets of distributed being that supply
vivisystems their character: -
The absence of imposed centralized control -
The autonomous nature of subunits -
The high connectivity between the subunits -
The webby nonlinear causality of peers influencing peers.
The relative strengths and dominance of each factor have not yet been
examined systematically.
One theme of this book is that distributed artificial vivisystems,
such as parallel computing, silicon neural net chips, or the grand
network of online networks commonly known as the Internet, provide
people with some of the attractions of organic systems, but also, some
of their drawbacks. I summarize the pros and cons of distributed systems
here:
Benefits of Swarm Systems -
Adaptable -- It is possible to build a clockwork system that can adjust
to predetermined stimuli. But constructing a system that can adjust to
new stimuli, or to change beyond a narrow range, requires a swarm -- a hive
mind. Only a whole containing many parts can allow a whole to persist
while the parts die off or change to fit the new stimuli. -
Evolvable -- Systems that can shift the locus of adaptation over time
from one part of the system to another (from the body to the genes or
from one individual to a population) must be swarm based. Noncollective
systems cannot evolve (in the biological sense). -
Resilient -- Because collective systems are built upon multitudes in
parallel, there is redundancy. Individuals don't count. Small failures
are lost in the hubbub. Big failures are held in check by becoming
merely small failures at the next highest level on a hierarchy.
-
Boundless -- Plain old linear systems can sport positive feedback
loops -- the screeching disordered noise of PA microphone, for example. But
in swarm systems, positive feedback can lead to increasing order. By
incrementally extending new structure beyond the bounds of its initial
state, a swarm can build its own scaffolding to build further structure.
Spontaneous order helps create more order. Life begets more life, wealth
creates more wealth, information breeds more information, all bursting
the original cradle. And with no bounds in sight. -
Novelty -- Swarm systems generate novelty for three reasons: (1) They
are "sensitive to initial conditions" -- a scientific shorthand for saying
that the size of the effect is not proportional to the size of the
cause -- so they can make a surprising mountain out of a molehill. (2) They
hide countless novel possibilities in the exponential combinations of
many interlinked individuals. (3) They don't reckon individuals, so
therefore individual variation and imperfection can be allowed. In swarm
systems with heritability, individual variation and imperfection will
lead to perpetual novelty, or what we call evolution.
Apparent Disadvantages of Swarm Systems -
Nonoptimal -- Because they are redundant and have no central control,
swarm systems are inefficient. Resources are allotted higgledy-piggledy,
and duplication of effort is always rampant. What a waste for a frog to
lay so many thousands of eggs for just a couple of juvenile offspring!
Emergent controls such as prices in free-market economy -- a swarm if there
ever was one -- tend to dampen inefficiency, but never eliminate it as a
linear system can. -
Noncontrollable -- There is no authority in charge. Guiding a swarm
system can only be done as a shepherd would drive a herd: by applying
force at crucial leverage points, and by subverting the natural
tendencies of the system to new ends (use the sheep's fear of wolves to
gather them with a dog that wants to chase sheep). An economy can't be
controlled from the outside; it can only be slightly tweaked from
within. A mind cannot be prevented from dreaming, it can only be plucked
when it produces fruit. Wherever the word "emergent" appears, there
disappears human control. -
 Nonpredictable-The complexity of
a swarm system bends it in unforeseeable ways. "The history of biology
is about the unexpected," says Chris Langton, a researcher now
developing mathematical swarm models. The word emergent has its dark
side. Emergent novelty in a video game is tremendous fun; emergent
novelty in our airplane traffic -- control system would be a national
emergency. -
Nonunderstandable -- As far as we know, causality is like clockwork.
Sequential clockwork systems we understand; nonlinear web systems are
unadulterated mysteries. The latter drown in their self-made paradoxical
logic. A causes B, B causes A. Swarm systems are oceans of intersecting
logic: A indirectly causes everything else and everything else
indirectly causes A. I call this lateral or horizontal causality. The
credit for the true cause (or more precisely the true proportional mix
of causes) will spread horizontally through the web until the trigger of
a particular event is essentially unknowable. Stuff happens. We don't
need to know exactly how a tomato cell works to be able to grow, eat, or
even improve tomatoes. We don't need to know exactly how a massive
computational collective system works to be able to build one, use it,
and make it better. But whether we understand a system or not, we are
responsible for it, so understanding would sure help. -
Nonimmediate -- Light a fire, build up the steam, turn on a switch, and
a linear system awakens. It's ready to serve you. If it stalls, restart
it. Simple collective systems can be awakened simply. But complex swarm
systems with rich hierarchies take time to boot up. The more complex,
the longer it takes to warm up. Each hierarchical layer has to settle
down; lateral causes have to slosh around and come to rest; a million
autonomous agents have to acquaint themselves. I think this will be the
hardest lesson for humans to learn: that organic complexity will entail
organic time.
The tradeoff between the pros and cons of swarm logic is very similar to
the cost/benefit decisions we would have to make about biological
vivisystems, if we were ever asked to. But because we have grown up with
biological systems and have had no alternatives, we have always accepted
their costs without evaluation.
We can swap a slight tendency for weird glitches in a tool in
exchange for supreme sustenance. In exchange for a swarm system of 17
million computer nodes on the Internet that won't go down (as a whole),
we get a field that can sprout nasty computer worms, or erupt
inexplicable local outages. But we gladly trade the wasteful
inefficiencies of multiple routing in order to keep the Internet's
remarkable flexibility. On the other hand, when we construct autonomous
robots, I bet we give up some of their potential adaptability in
exchange for preventing them from going off on their own beyond our full
control.
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. 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.
For each step we push our machines toward the collective, we move them
toward life. And with each step away from the clock, our contraptions
lose the cold, fast optimal efficiency of machines. Most tasks will
balance some control for some adaptability, and so the apparatus that
best does the job will be some cyborgian hybrid of part clock, part
swarm. The more we can discover about the mathematical properties of
generic swarm processing, the better our understanding will be of both
artificial complexity and biological complexity.
Swarms highlight the complicated side of real things. They depart
from the regular. The arithmetic of swarm computation is a continuation
of Darwin's revolutionary study of the irregular populations of animals
and plants undergoing irregular modification. Swarm logic tries to
comprehend the out-of-kilter, to measure the erratic, and to time the
unpredictable. It is an attempt, in the words of James Gleick, to map
"the morphology of the amorphous" -- to give a shape to that which seems to
be inherently shapeless. Science has done all the easy tasks -- the clean
simple signals. Now all it can face is the noise; it must stare the
messiness of life in the eye.
continue...
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