p.9 This book is about the science of complexity - a subject that's still so new and so
wide-ranging that nobody knows quite how to define it, or even where its boundaries lie. But then, that's the whole point.
p.17,19 The world isn't stable. It's full of evolution, upheaval, and surprise. Economics
had to take that ferment into account. And now he [Brian Arthur] believed he'd found the way to do that, using a principle
known as "increasing returns" - or in the King James translation, "To them that hath shall be given." ...Susan Arthur
had seen her husband returning from the academic wars before. "Well," she said, trying to find something comforting to say,
"I guess it wouldn't be a revolution, would it, if everybody believed in it at the start?"
p.21 It didn't take very long for Arthur to realize that, when it came to real-world complexities,
the elegant equations and the fancy mathematics he'd spent so much time on in school were no more than tools - and limited
tools at that. The crucial skill was insight, the ability to see connections.
p.22 The brightest young economists seemed to be devoting their careers to proving theorem after theorem
- whether or not those theorems had much to do with the world.
p.24 [Stuart] Dreyfus [Brian Arthur's new adviser at Stanford] " ... believed in getting to the heart
of the problem," says Arthur. "Instead of solving incredibly complicated equations, he taught me to keep simplifying
the problem until you found something you could deal with. Look for what made a problem tick. Look for the key factor,
the key ingredient, the key solution."
p.38, 39 The point was that you have to look at the world as it is, not as some elegant theory says
it ought to be... [Arthur] saw the increasing-returns approach as a step down that same path for economics. "The
important thing is to observe the actual living economy out there," he says. "It's path-dependent, it's complicated, it's
evolving, it's open, and it's organic."
p.142 chaos theory tells you that the slightest uncertainty in your knowledge of the initial conditions
will often grow inexorably. After a while, your predictions are nonsense.
p.169 that's what this business of "emergence" was all about: building blocks at one level
combining into new building blocks at a higher level. It seemed to be one of the fundamental organizing principles
of the world. It certainly seemed to appear in every complex, adaptive system that you looked at.
p.170 "So if I have a process that can discover building blocks," says Holland, "the combinatorics start
working for me instead of against me. I can describe a great many complicated things with relatively few building
blocks."
p.177 [Holland quote] All complex, adaptive systems - economies, minds, organisms - build
models that allow them to anticipate the world
p.178, 179 In the cognitive realm, says Holland, anything we call a "skill" or
"expertise" is an implicit model - or more precisely, a huge, interlocking set of standard operating procedures that
have been inscribed on the nervous system and refined by years of experience... models and predictions are everywhere...
where do the models come from? ... feedback from the environment... an agent can improve its internal
models... It simply has to try the models out, see how well their predictions work in the real world, and... adjust the models
to do better the next time... an adaptive agent has to be able to take advantage of what its world is trying to tell
it.
p.193 knowledge can be expressed in terms of mental structures that behave very much like rules; that these
rules are in competition, so that experience causes useful rules to grow stronger and unhelpful rules to grow weaker; and
that plausible new rules are generated from combinations of old rules... these principles ought to cause the spontaneous emergence
of default hierarchies as the basic organizational structure of all human knowledge
p.193 The cluster of rules forming a default hierarchy is essentially synonymous with what Holland calls
an internal model. We use weak general rules with stronger exceptions to make predictions about how things should be assigned
to categories... these default-hierarchy models ought to emerge whether the principles are implemented as a classifier system
or in some other way
p.200 so far as [Chris] Langton was concerned, programming [computers] was the best game ever invented.
p.255 Look at meteorology, he [Holland] told them. The weather never settles down. It never repeats itself
exactly. It's essentially unpredictable more than a week or so in advance. And yet we can comprehend and explain almost everything
that we see up there. We can identify important features... We can understand their dynamics. We can understand how they interact
to produce weather on a local and regional scale. In short, we have a real science of weather - without full prediction.
And we can do it because prediction isn't the essence of science. The essence is comprehension and explanation.
p.277 Artificial life, he wrote, is essentially just the inverse of conventional biology. Instead of being
an effort to understand life by analysis - dissecting living communities into species, organisms, tissues, cells,
organelles, membranes, and finally molecules - artificial life is an effort to understand life by synthesis: putting
simple pieces together to generate lifelike behavior in man-made systems. Its credo is that life is not a property of matter
per se, but the organization of that matter. It operating principle is that the laws of life must be laws of dynamical form
p.278 the essence of a mechanical process - the "thing" responsible for its behavior - is not a
thing at all. It is an abstract control structure, a program that can be expressed as a set of rules without regard
to the material the machine is made of... the "machineness" of the machine is in the software, not the hardware.
And once you've accepted that, he [Langton] said... then it's a very small step to say that the "aliveness" of an organism
is also in the software - in the organization of the molecules, not the molecules themselves.
p.279 "The most surprising lesson we have learned from simulating complex physical systems on computers
is that complex behavior need not have complex roots," he [Langton] wrote, complete with italics.
"Indeed, tremendously interesting and beguilingly complex behavior can emerge from collections of extremely simple
components."
p.280 the way to achieve lifelike behavior is to simulate populations of simple units instead of
one big complex unit. Use local control instead of global control. Let the behavior emerge from the bottom
up, instead of being specified from the top down. And while you're at it, focus on ongoing behavior instead
of the final result.
p.289,292 One [model] that [Doyne] Farmer has given particular attention to is connectionism:
the idea of representing a population of interacting agents as a network of "nodes" linked by "connections"... So, in short,
says Farmer, the connectionist idea shows how the capacity for learning and evolution can emerge even if the nodes, the individual
agents, are brainless and dead. More generally, by putting the power in the connections and not the nodes, it points
the way toward a very precise theory of what Langton and the artificial lifers mean when they say that the
essence of life is in the organization and not the molecules.
p.329 The complexity revolution began the first time someone said, "Hey, I can
start with this amazingly simple system, and look - it gives rise to these immensely complicated and unpredictable consequences."
