Complexity_ A Guided Tour - Melanie Mitchell [175]
Carroll, Sean, 278
carrying capacity, 25, 27
cascading failure, 255–258
C. elegans, 158, 238, 247
cellular automata
architecture of 146–148
classes of behavior in, 155–156
computation in, 157–158, 161, 164–168, 171–172, 303
elementary, 152–153 (see also rule 110 cellular automaton; rule 30 cellular automaton)
as evolved by genetic algorithms, 161–164
as idealized models of complex systems, 148–149, 211
information processing in, 157–158, 161, 164–168, 171–172, 303
as models for the universe, 158–159
numbering of, 153–154
particles in, 166–168, 171–172
as pseudo-random number generators, 155
rules, 147–149
space-time diagrams of, 153–155, 162, 164–165, 167
as substrate for self-reproducing automata, 149
as universal computers, 149–151, 156
central processing unit (CPU), 145–146, 160–161
chaos, 20–22, 28, 31–39, 211, 273, 284, 293, 300
edge of, 284–285
in the logistic map, 31–33
onset of, 35–36
period-doubling route to, 34–35
in random Boolean Networks, 284–285
revolutionary ideas from, 38
universal properties of, 34–38, 294
characteristic scale (of a distribution), 243–244
chromosomes, 88–89, 96, 274–275
citric acid cycle, 179
classical mechanics, 19, 48
Clausius, Rudolph, 47, 51
clockwork universe, 19, 33
clustering (in networks), 235–236, 238–240, 245, 252, 255
coarse graining, 101, 183
codons, 90–92
coevolution of Web and search engines, 10
Cohen, Irun, 40
colonial organisms, 110
complex adaptive systems
distinction from complex systems, 13
See also complexity
complexity (or complex systems)
as algorithmic information content, 98–99
“calculus” of, 301–303
central question of sciences of, 13
common properties of, 294–295
as computational capacity, 102
definitions of, 13, 94–111
as degree of hierarchy, 109–111
effective, 98–100
in elementary cellular automata, 155
as entropy, 96–98
as fractal dimension, 102–109
future of, 301–303
Horgan’s article on, 291–292
Latin root, 4
as logical depth, 100–101
measurement of, 13, 94–111
problems with term, 95, 299, 301
roots of sciences of, 295–298
science of versus sciences of, 14, 95
significance of in science, 300
as size, 96
source of biological, 233, 248–249, 273–288
statistical, 102–103
as thermodynamic depth, 101–102
as a threat, 257
unified theories of, 293, 299
universal computation as upper limit on, 157
universal principles for, 299
vocabulary for, 293, 298, 301–303
complex systems. See complexity
computable problem (or process), 157
computation
biologically inspired, 184–185, 207 (see also genetic algorithms)
in the brain, 168
in cellular automata, 157–158, 161, 164–168, 171–172, 303
courses on theory of, 67
defined as Turing machine (see Turing machines)
definite procedures as, 63–64, 146
definitions of, 57
evolutionary (see genetic algorithms)
limits to, 68
linked to life and evolution, 115
in logical depth, 100
in natural systems, xi, 56–57, 145–146, 156–158, 169–170, 172, 179–185
non-von-Neumann-style, 149, 151, 171
reversible, 46–47
in stomata networks, 168
in traditional computers, 170–171
universal (see universal computation)
von-Neumann-style, 146, 169–171, 209
See also information processing
computational capacity, 102
computational mechanics, 303
computer models
caveats for, 222–224, 291
of genetic regulatory networks, 282–284
period-doubling route to chaos in, 37
prospects of, 158, 220–222
replication of, 223–224
of weather, 22, 37
See also models
computing. See computation
conceptual slippage, 188, 191–193, 196–197, 202, 206
consciousness, 4, 6, 184, 189
convergent evolution, 280
Conway, John, 149–151
Cook, Matthew, 156
Copernicus, 17
Copycat program, 193
analogies with biological systems, 208
codelets, 197–198
as example of idea model, 211
example run of, 198–206
frequencies of answers in, 206–207
parallel