self-organised criticality order, chaos, andarpwhite/courses/5900h/notes/si lecture 24a.… ·...
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Order, Chaos, andSelf-Organised Criticality
Thiemo Krink, Dept. of Computer Science, University of Aarhus
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IntroductionOrder, Chaos, and SOC
Dynamic systems
Loose definitionAny system, where components have motion
Analysis requires to determine the• affected components• initial state of the components• set of rules that describe the state transitions
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IntroductionOrder, Chaos, and SOC
Types of behaviour in dynamic systems
(a) Fixed points
(b) Simple periodic orbits
(c) Period-n orbit
(d) Chaos
Quasi-periodic
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Order
OrderOrder, Chaos, and SOC
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OrderOrder, Chaos, and SOC
When do we find order in a system?E
nerg
y
When a system is in balance at a stable equilibrium
Main characteristics
• little disturbances have no consequences • the system response is proportional to the impact• dramatic disturbances can cause state transitions
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OrderOrder, Chaos, and SOC
Which kind of systems are stable?
• ice crystals• a sand grain on the floor • according to economic theory: economic systems (General Equilibrium Theory)
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Chaos
ChaosOrder, Chaos, and SOC
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ChaosOrder, Chaos, and SOC
What is chaos?
A type of dynamic behaviour, which is not predictablethough deterministic
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ChaosOrder, Chaos, and SOC
What is chaos?
Properties of chaos
• chaos is deterministic• causes highly complicated non-linear motion• can only be predicted on a short time scale
A type of dynamic behaviour, which is not predictablethough deterministic
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ChaosOrder, Chaos, and SOC
Where do we find chaos?
Almost everywhere, for instance in• turbulence of water and air • wobble of planets • global weather patterns• electric-chemical activity in the human brain
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ChaosOrder, Chaos, and SOC
A simple chaotic system in physics
F
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ChaosOrder, Chaos, and SOC
Chaos versus stochasticity
Chaos is• deterministic• only predictable for a very short while
Stochasticity is • unpredictable for single events• but allows valuable prediction of the overall future outcome
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ChaosOrder, Chaos, and SOC
The logistic map - a model for chaos
A simple population growth model (Bob May / Feigenbaum)
xt+1 = 4rxt (1− xt )123
r reproduction rate of the population; r ∈ [0...1]
xt number of individuals at time t; xt ∈ [0...1]
t time
decrease due to overpopulation
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For the population will die out
For the population will reach a fixed size > 0
ChaosOrder, Chaos, and SOC
The logistic map - fixed points
Logistic map with r = 7/10
r ≤ 14
⇒
14
< r ≤ 34
⇒
xt
xt+1
stablefixed point
unstable fixed point
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ChaosOrder, Chaos, and SOC
The logistic map - simple cycles
Logistic map with r = 8/10
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ChaosOrder, Chaos, and SOC
The logistic map - n-period cycles
Logistic map with r = 88/100
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ChaosOrder, Chaos, and SOC
Analyzing stability and instability
MethodExamine the local behaviour of the map near afixed point.
′f (xF ) <1 attracting and stable
′f (xF ) = 0 super - stable
′f (xF ) >1 repelling and unstable
′f (xF ) =1 neutral
f (x) = 4rx(1− x)
′f (x) = 4r(1− 2x)
xF fixed point
Let
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ChaosOrder, Chaos, and SOC
The logistic map - chaos
Logistic map with r = 1
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ChaosOrder, Chaos, and SOC
Chaos and Bifurcation
What is bifurcation?A point at which a system moves from a period-nlimit cycle to a period-2n cycle (here: by increase of parameter r)
Observationsthe increase of r gets smaller and smaller for higherbifurcationsat a critical value, the system will fall into an infinitecycle, i.e., chaos
•
•
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ChaosOrder, Chaos, and SOC
Bifurcation and self-similarity
r
xt
Observations• transitions between cycles become shorter and shorter• self-similarities
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ChaosOrder, Chaos, and SOC
How fast will the next bifurcation occur?
r
xt
ak value of r where the logistic map bifurcates into a period - 2k cycle
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ChaosOrder, Chaos, and SOC
Universality of the Feigenbaum constant
How fast will the next bifurcation occur?
ak value of r where the logistic map bifurcates into a period - 2k cycle
d∞ = 4.669202… for all one-dimensional systems!
da a
a akk k
k k
= −−
≥−
+
1
1
with k 2
Feigenbaum constant
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ChaosOrder, Chaos, and SOC
Why is the Feigenbaum constant useful?
Scenario• a chaotic process with a single tuneable parameter r• first few bifurcations can empirically be determinede.g. a4 and a5
Benefit of Feigenbaum constant• estimation of when the system will become chaotici.e. a∞ -> value of r for chaos
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ChaosOrder, Chaos, and SOC
Why is chaos non-predictable by IT?
the computational complexity for an accurate predictionis NP space completemeasurement errorsan initial state involving an irrational number cannot beaccurately represented (finiteness problem)
•
••
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ChaosOrder, Chaos, and SOC
Non-predictability - an example
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ChaosOrder, Chaos, and SOC
Summary of chaos theory
Chaotic systems aredeterministicextremely sensitive to initial conditionshave no memory and cannot evolvenot complex
••••
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Swarm Intelligence Self-organization
Dynamics in agent-based vs. classical analytical models
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Swarm Intelligence Self-organization
Lotka-Volterra Systems vs. Individual-based models
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Swarm Intelligence Self-organization
Prey-predator systems - an example
Analogies in biology and computing
Prey-Predator <=> Producer-Consumer
Co-evolution <=> Adaptive Producer-Consumer
Common mechanisms
• coupled states and feedback dynamics
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Swarm Intelligence Self-organization
Lotka-Volterra systems
DefinitionMathematical models for the cyclic nature of population dynamics arising from the interactionsof agents.
Small fish population Shark population
requires &reduces
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Swarm Intelligence Self-organization
A prey-predator system in Lotka-Volterra
dF
dtF a bS
dS
dtS cF d
F
a
b
S
c
d
t
= −
= −
( )
( )
growth rate of small fish
growth rate of sharks
number of small fish
reproduction rate of small fish
number of small fish that a shark can eat
number of sharks
amount of energy that a shark gains from a small fish
death rate of sharks
time
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Swarm Intelligence Self-organization
Generalized Lotka-Volterra systems
dx
dtx A xi
i ijj
n
j= −=∑
1
1( )
Differential equations for a n-species predator-prey system
xi
Aij
number of individuals of the ith species
effect of species j on species i
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Swarm Intelligence Self-organization
The four classes of feedback behaviour
(a) Fixed points
(b) Simple periodic orbits
(c) Period-n orbit
(d) Chaos
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Swarm Intelligence Self-organization
Dynamics of the prey-predator system
A simple Lotka-Volterra attractor
infinite many cyclesdetermined by the startpopulation sizes F (fish population)
S (s
hark
pop
ulat
ion)
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Swarm Intelligence Self-organization
Do Lotka-Volterra syst. have fixed points?
dF
dt
dS
dtF
d
cS
a
b= = = =0 for and
Yes, they have a fixed point, i.e., stability at:
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Swarm Intelligence Self-organization
Can Lotka-Volterra systems be chaotic?
Yes, but only in more than two dimensions, i.e.,for more than two interacting agents.
A
A A A
A A A
A A A
=
= − −
11 12 13
21 22 23
31 32 33
0 5 0 5 0 1
0 5 0 1 0 1
0 1 0 1
. . .
. . .
. .α
dx
dtx A xi
i ijj
n
j= −=∑
1
1( )
with α = 1 5.
Example:
x1 x2
x3
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Swarm Intelligence Self-organization
Dynamics of Individual-Based Models (IBM)
2d world view
A very simple model of three interacting species...
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Swarm Intelligence Self-organization
Dynamics of Individual-Based Models (IBM)
Population dynamics
…the complex result on the population level
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Swarm Intelligence Self-organization
Individual-based models vs. Lotka-Volterra
Lotka-Volterra IBMs
World view top-down bottom-up
State space small astronomically huge
Validity large scale all scales
Prerequisites knowledge about knowledge about global mechanisms indiv. mechanisms
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Self-organised CriticalityOrder, Chaos, and SOC
What is complexity?
• Complexity is the study of how large collections ofsimple units produce a wide variety of behaviour.
• Complex behaviour is neither linear (stable systems)nor uncorrelated (chaos).
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degree of disorder
com
plex
ity
stable systems
complex systems
chaotic systems
Where do we find complexity?
Self-organised CriticalityOrder, Chaos, and SOC
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E
E
E
EE
E E E E E E E E E E E E E E E0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 2 4 6 8 10 12 14 16 18 20
E
E
E
E
EE
EEEEEEEEEEEEEE-3
-2.5
-2
-1.5
-1
-0.5
0
0 0.5 1 1.5 2 2.5 3
Log/Log transform
f (x) = x−τ f (x) = x−1 = 1x
In general: here:
Fre
quen
cy
Impactlo
g (F
requ
ency
)log (Impact)
Self-organised CriticalityOrder, Chaos, and SOC
Observation in Complex Systems• the frequency of an event regarding its impact
follows a power-law distribution
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Order, Chaos, and SOC
Earthquakes and power laws
Self-organised Criticality
The frequency is correlated with the impact strength
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Order, Chaos, and SOC
Cities and population sizes
Self-organised Criticality
The Zipf law is a power law
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Order, Chaos, and SOC
The game of life revisited
Self-organised Criticality
The game of life follows a power law
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Order, Chaos, and SOC
Fractals revisited
Self-organised Criticality
Fractal structures cause power law phenomena
δ area of a measure square
D = 1.52
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Order, Chaos, and SOC
Fractals in time - “One-over-f” signals
Self-organised Criticality
The strength of the signal is inversely proportional to thefrequency f - here: light emitted from a quasar
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Order, Chaos, and SOC
How does a non-complex signal look like?
Self-organised Criticality
time
ampl
itude
White noise - a typical example for uncorrelated behaviour
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Order, Chaos, and SOC Self-organised Criticality
What is the mechanism behind power laws?
Ideascooperative phenomenon of many components, sincesystems with few degrees of freedom cannot do itmust be an open system - closed systems reach anequilibriumcould be related to spatial structure
•
•
•
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Order, Chaos, and SOC
What is self-organised criticality (SOC)?
Self-organised Criticality
A critical state at the edge between stability and chaos,where complexity is formed by self-organisation
Important characteristic
The process of self-organisation takes place over a longtransient period
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Order, Chaos, and SOC
The Sandpile Model
Self-organised Criticality
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Z(x, y) → Z(x, y) +1
Select a random x and y
If Z(x,y) = 4 then
Repeat until termination
Let
Z(x, y) → Z(x, y) − 4
Z(x ±1, y) → Z(x ±1, y) +1
Z(x, y ±1) → Z(x, y ±1) +1
and
Z(x, y) no. of grains at position x,y
For all (x,y)
Repeat until no Z(x,y) = 41 2 0 2 3
2 3 2 3 0
1 2 3 3 2
3 1 3 2 1
0 2 2 1 2
Z(2,1) = 2
Order, Chaos, and SOC Self-organised Criticality
The Sandpile Model
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1 2 0 2 3
2 3 2 3 0
1 2 3 3 2
3 1 3 2 1
0 2 2 1 2
adding of one sand grain: Z(3,3) -> Z(3,3) + 1
Order, Chaos, and SOC Self-organised Criticality
The Sandpile Model
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1 2 0 2 3
2 3 2 3 0
1 2 4 3 2
3 1 3 2 1
0 2 2 1 2
Order, Chaos, and SOC Self-organised Criticality
The Sandpile Model
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1 2 0 2 3
2 3 2 3 0
1 2 4 3 2
3 1 3 2 1
0 2 2 1 2
Order, Chaos, and SOC Self-organised Criticality
The Sandpile Model
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1 2 0 2 3
2 3 3 3 0
1 3 0 4 2
3 1 4 2 1
0 2 2 1 2
Order, Chaos, and SOC Self-organised Criticality
The Sandpile Model
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1 2 0 2 3
2 3 3 3 0
1 3 0 4 2
3 1 4 2 1
0 2 2 1 2
Order, Chaos, and SOC Self-organised Criticality
The Sandpile Model
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1 2 0 2 3
2 3 3 4 0
1 3 2 0 3
3 2 0 4 1
0 2 3 1 2
Order, Chaos, and SOC Self-organised Criticality
The Sandpile Model
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1 2 0 2 3
2 3 3 4 0
1 3 2 0 3
3 2 0 4 1
0 2 3 1 2
Order, Chaos, and SOC Self-organised Criticality
The Sandpile Model
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1 2 0 3 3
2 3 4 0 1
1 3 2 2 3
3 2 1 0 2
0 2 3 2 2
Order, Chaos, and SOC Self-organised Criticality
The Sandpile Model
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1 2 0 3 3
2 3 4 0 1
1 3 2 2 3
3 2 1 0 2
0 2 3 2 2
Order, Chaos, and SOC Self-organised Criticality
The Sandpile Model
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1 2 1 3 3
2 4 0 1 1
1 3 3 2 3
3 2 1 0 2
0 2 3 2 2
Order, Chaos, and SOC Self-organised Criticality
The Sandpile Model
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1 2 1 3 3
2 4 0 1 1
1 3 3 2 3
3 2 1 0 2
0 2 3 2 2
Order, Chaos, and SOC Self-organised Criticality
The Sandpile Model
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1 3 1 3 3
3 0 1 1 1
1 4 3 2 3
3 2 1 0 2
0 2 3 2 2
Order, Chaos, and SOC Self-organised Criticality
The Sandpile Model
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1 3 1 3 3
3 1 1 1 1
2 0 4 2 3
3 3 1 0 2
0 2 3 2 2
Order, Chaos, and SOC Self-organised Criticality
The Sandpile Model
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1 3 1 3 3
3 1 1 1 1
2 0 4 2 3
3 3 1 0 2
0 2 3 2 2
Order, Chaos, and SOC Self-organised Criticality
The Sandpile Model
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1 3 1 3 3
3 1 2 1 1
2 1 0 3 3
3 3 2 0 2
0 2 3 2 2
Order, Chaos, and SOC Self-organised Criticality
The Sandpile Model
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Order, Chaos, and SOC Self-organised Criticality
The Sandpile Model
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Order, Chaos, and SOC Self-organised Criticality
The Sandpile Model
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Order, Chaos, and SOC Self-organised Criticality
The Sandpile Model
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Order, Chaos, and SOC Self-organised Criticality
The Sandpile Model
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Geological time (million years)
Per
cent
ext
inct
ion
(Sepkoski, 1993)
96% (Raup, 1986)lo
g (f
requ
ency
)log (percent extinction)
Order, Chaos, and SOC Self-organised Criticality
Mass Extinction follows a power-law
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Gradualist Model Punctuated Equilibrium
Charles Darwin, John Maynard Smith, Richard Dawkins
Stephen J. Gould, Stuart Kauffman, Per Bak
Tim
e
Change Change
Order, Chaos, and SOC Self-organised Criticality
Gradualist Model vs. Punctuated Equilibrium
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Order, Chaos, and SOC
The evolution/extinction model
Self-organised Criticality
Topologya ring model of neighboured species
Algorithm1. Initialize all species with random fitness2. Repeat
Substitute the species with the worst fitnessand its direct neighbours
species 1 species 2 species 3 species 4 species 5
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species 1 species 2 species 3 species 4 species 5
Order, Chaos, and SOC
The evolution/extinction model
Self-organised Criticality
F=0.23 F=0.47 F=0.05 F=0.31 F=0.54
currently worst species
block of species, which getsubstituted
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Order, Chaos, and SOC
The evolution/extinction model
Self-organised Criticality
Fitn
ess
Time
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Order, Chaos, and SOC
The evolution/extinction model
Self-organised Criticality
Species index
Fitn
ess
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Order, Chaos, and SOC
Consequence of self-organised criticality
Self-organised Criticality
avalanches always occur in complex systemscatastrophes in complex systems are inevitable!This includes man-made complex systems, such asaeroplanes, power plants, and stock exchange
••
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Order, Chaos, and SOC Self-organised Criticality
Application to Evolutionary AlgorithmsChallenges in EAs• parameter control• premature loss of diversity
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SOC sandpile model
controlscreates
Mass extinction model
Outbreeding model
Mutation modelcontrols
controls
Order, Chaos, and SOC Self-organised Criticality
Application to Evolutionary Algorithms1
2
3
Challenges in EAs• parameter control• premature loss of diversity
size
of
aval
anch
e
time steps
Powerlaw distribution
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Summary
Summary
• Chaos is unpredictable though deterministic• Chaos is extremely sensitive to initial conditions• Chaos has no memory and cannot evolve• The transition into a chaotic state can be estimated
Order, Chaos, and SOC
Chaos
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Summary
Summary
• Chaos is unpredictable though deterministic• Chaos is extremely sensitive to initial conditions• Chaos has no memory and cannot evolve• The transition into a chaotic state can be estimated
• Chaos Complexity• Complex behaviour is neither linear nor uncorrelated• Complexity is found at the border of stability and chaos• The common mechanism of complex systems can be
simulated by simple models (SOC)
Order, Chaos, and SOC
≠Complexity
Chaos