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algorithms - trust the process
An algorithm is a formal process that will produce the same results every time it is run.
examples: solving a multiplication problem; tic-tac-toe; a computer program; a cooking recipe
1 - substrate neutrality - by hand or by machine, it still works
2 - underlying mindlessness - zeros and ones; lots of meaningless small decisions adding up to something that is judgemental or wise
3 - guaranteed results
The process of unpredictable, complex pattern formation from
simpler rules.
The emergence of a system's global properties and
capabilities which are not prespecified by network design and are difficult or
impossible to predict from knowledge of its constituent parts
High
Tech Evolves (no longer available for free)
by Eric Roston
Time, June 10, 2002
More businesses are studying biology to solve complex management and computing problems.
What
is the Game of Life?
by Paul Callahan
The Game of Life (or simply Life) is not a game in the conventional sense. There are no players, and no winning or losing. Once the "pieces" are placed in the starting position, the rules determine everything that happens later. Nevertheless, Life is full of surprises! In most cases, it is impossible to look at a starting position (or pattern) and see what will happen in the future. The only way to find out is to follow the rules of the game.
birth, death, and survival (loneliness and overcrowding)
download your own: Life32 by Johan Bontes
Conway's Game of Life freeware for Windows 95/98/NT
New book
proposes 'A New Kind of Science' (no
longer available for free)
by Matt Crenson
Associated Press, June 9, 2002
"A New Kind of Science" proposes that simple
rules, not complex equations, are the key to such profound scientific mysteries
as the structure of the universe and the incredible diversity of life on Earth.
...
"A New Kind of Science" is rooted in Wolfram's discovery during the
1980s that very simple computer programs known as cellular automata can produce
strikingly complex results. In their most basic form, these programs take a
pattern of gridded black and white squares and add to or modify them according
to simple rules. Such programs usually generate beautiful checkered, striped and
nested patterns that can exhibit incredible intricacy.
But Wolfram's obsession is the occasional cellular automaton that reels off a
random pattern of black and white squares ad infinitum.
You can make one of these curiosities by filling in some of the squares in the
top row of a sheet of graph paper. Then move to the next row of squares. Blacken
a square unless the one directly above it and both of its neighbors are the same
color, or the one above has a black neighbor on the left and a white neighbor on
the right. Then go to row three, repeat the process, and so on.
Wolfram has programmed computers to do this simple exercise and similar ones
millions and millions of times over.
"What I found - to my great surprise - was that . . . even some of the very
simplest programs that I looked at had behavior that was as complex as anything
I had ever seen," he wrote.
No amount of careful observation, no mathematical equation could ever predict
what the next row of squares produced by one of these simple programs will look
like. The only way to find out is to follow the rule.
Why would a software millionaire with impeccable scientific credentials spend
years poring over such apparent trivialities?
Well, Wolfram explains, when traditional science encounters something with no
mathematically reducible pattern, it simply throws up its hands. But his
experiments show that apparently random processes can arise from surprisingly
simple rules.
"It's our intuition that when we see something in nature that looks
complicated, then somehow the explanation must be complicated," Wolfram
said. "The surprising thing that I've found is that in the world of simple
programs, this is not the case."
His book argues that nature doesn't require complicated explanations either. If
he's right, scientists should be able to expose some fairly simple machinery
buried deep inside some of nature's most seemingly complex creations.
"What I'm trying to do here is answer some of the questions that
traditional science hasn't had much success in answering," he said.
Seeing Around Corners
(no longer available for free)
by Jonathan Rauch
Atlantic Monthly, April 2002
The new science of artificial societies suggests that real ones are both more predictable and more surprising than we thought. Growing long-vanished civilizations and modern-day genocides on computers will probably never enable us to foresee the future in detail—but we might learn to anticipate the kinds of events that lie ahead, and where to look for interventions that might work.
Jeffrey Ventrella's Gene Pool and Darwin Pond
Discrete Dynamic Systems: Tools & Theory emphasizing Epistemology,
Knowledge, & Perception
Layered Emergent Dynamics: Perceptual Processes which enable Apparent Motion
by Tom Malloy
Department of Psychology University of Utah
Center for
Complex Network Research
Albert-László Barabási
Notre Dame University
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