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Complex Systems
Session 3.
Self-organization and criticality
The Matthew Effect
Robert K. Merton (1968)
Herbert A. Simon (1955) Nobel M. Prize (1978)
Udny Yule (1925)
Yule-Simon process and distribution
SzEEDSM Complex Systems 2017
Yule-Simon
SzEEDSM Complex Systems 2017
Preferential attachment
SzEEDSM Complex Systems 2017
Scaling in networks
SzEEDSM Complex Systems 2017
Complex Networks
SzEEDSM Complex Systems 2017
Degree distribution of Internet
routers
SzEEDSM Complex Systems 2017
α= 1.1
Degree distribution of WWW pages
α= 1.1
Degree distribution in Social
Networks
Gibrat’s law
Robert Gibrat (1931)
Growth rate (x=company- asset- or city size)
Growth is independent of size
Under this assumption, size distribution is power law again
with α approximately 1.
SzEEDSM Complex Systems 2017
Firm size distribution
SzEEDSM Complex Systems 2017
Sales &
Profit
SzEEDSM Complex Systems 2017
The Black Swan
SzEEDSM Complex Systems 2017 Nassim Taleb
Fat Tail
SzEEDSM Complex Systems 2017
Earthquake size distribution
SzEEDSM Complex Systems 2017
Phase transitions
Ferromagnetism and the Ising
model
Cluster size distribution
Mean field approximation
Mean field diagram
Bistability and bifurcation
Percolation
SzEEDSM Complex Systems 2017
Epidemic processes
SIS model
Traffic congestion
Inverse U curve
Social collapse
Collective intelligence
Power outages
10 4 10 5 10 6 10 710
-2
10-1
100
101
N= # of customers affected by outage
Frequency
(per year) of
outages > N
1984-1997
August 10, 1996
Square
site
percolation
or
simplified
“forest
fire”model.
The simplest possible toy model of cascading
failure.
connected
not
connected
Connected clusters
A “spark” that hits
a cluster causes
loss of that
cluster.
yield
=
density
- loss
Assume: one randomly
located spark
(average)
yield
=
density
- loss
Think of (toy) forest fires.
(average)
0 0.2 0.4 0.6 0.8 10
0.1
0.2
0.3
0.4
0.5
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(avg.)
yield
density
“critical point”
N=100
Critical point
criticality
This picture is very generic.
100
101
102
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10-1
100
101
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Power laws Criticality
100
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104
10-1
100
101
102
Power
laws: only
at the
critical point
low density
high density
Life, networks, the brain, the universe and
everything are at “criticality” or the “edge of
chaos.”
Does anyone really believe
this?
Self-organized criticality:
dynamics have critical point as global attractor
Simpler explanation: systems that
reward yield will naturally evolve to
critical point.
Would you
design a
system this
way?
Maybe random
networks
aren’t so great
High yields
0 0.2 0.4 0.6 0.8 10
0.1
0.2
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1
isolated
critical
tolerant
Why power laws?
Almost any
distribution
of sparks
Optimize
Yield
Power law
distribution
of events
0 0.2 0.4 0.6 0.8 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
random
“optimized”
density
yield
5 10 15 20 25 30
5
10
15
20
25
30
Probability distribution (tail of normal)
High probability region
Optimal “evolved”
“Evolved” = add one site at
a time to maximize
incremental (local) yield
Very local and limited optimization, yet still
gives very high yields.
Small events likely
large events
are unlikely
0 0.2 0.4 0.6 0.8 10
0.1
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random
“optimized”
density
High yields.
Optimized grid
Small events likely
large events
are unlikely
Optimized grid
0 0.2 0.4 0.6 0.8 10
0.1
0.2
0.3
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1
random
grid
High yields.
This source of power
laws is quite universal.
Almost any
distribution
of sparks
Optimize
Yield
Power law
distribution
of events
Tolerance is very different
from criticality.
• Mechanism generating power laws.
• Higher densities.
• Higher yields, more robust to sparks.
• Nongeneric, won’t arise due to random
fluctuations.
• Not fractal, not self-similar.
• Extremely sensitive to small perturbations that were
not designed for, “changes in the rules.”
Extreme robustness and extreme hypersensitivity.
Small
flaws
0 0.2 0.4 0.6 0.8 10
0.1
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1
0 0.2 0.4 0.6 0.8 10
0.1
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0.5
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0.8
0.9
1
