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Introduction to Computational Modeling of Social Systems
Emergent Structure Models: Applications to World Politics
Prof. Lars-Erik CedermanCenter for Comparative and International Studies (CIS)
Seilergraben 49, Room G.2, [email protected] Deiwiks, CIS Room E.3, [email protected]
http://www.icr.ethz.ch/teaching/compmodels
Week 12
2
Applying Geosim to World Politics
Configurations Processes
Qualitative
properties
Example 3.
Democratic peace
Example 4.
Emergence of the territorial state
Distributional
properties
Example 2.
State-size distributions
Example 1.
War-size distributions
3
Cumulative war-size plot, 1820-1997
Data Source:Correlatesof WarProject (COW)
1.0
0.1
0.01
log P(S>s) = 1.27 – 0.41 log s
2 3 4 5 6 7 810 10 10 10 10 10 10
WWI
WWII
2R = 0.985 N = 97
log P(S>s) (cumulative frequency)
log s (severity)
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Self-organized criticality
Per Bak’s sand pile Power-law distributedavalanches in a rice pile
5
• Slowly driven systems that fluctuate around state of marginal stability while generating non-linear output according to a power law.
• Examples: sandpiles, semi-conductors, earthquakes, extinction of species, forest fires, epidemics, traffic jams, city populations, stock market fluctuations, firm size
Theory: Self-organized criticality
Input Output
Complex System
log f
log s
f
s
s-
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War clusters in Geosim
t = 3,326 t = 10,000
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Simulated cumulative war-size plot
2 73 4 5 6
log P(S > s)(cumulativefrequency)
log s(severity)
log P(S > s) = 1.68 – 0.64 log s N = 218 R2 = 0.991
See “Modeling the Size of Wars” American Political Science Review Feb. 2003
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Applying Geosim to world politics
Configurations Processes
Qualitative
properties
Example 3.
Democratic peace
Example 4.
Emergence of the territorial state
Distributional
properties
Example 2.
State-size distributions
Example 1.
War-size distributions
9
2. Modeling state sizes: Empirical data
log s(state size)
log Pr (S > s)(cumulative frequency)
1998Data: Lake et al.
log S ~ N(5.31, 0.79) MAE = 0.028
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Simulating state size with terrain
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Simulated state-size distribution
log s(state size)
log Pr (S > s)(cumulative frequency)
log S ~ N(1.47, 0.53) MAE = 0.050
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Applying Geosim to world politics
Configurations Processes
Qualitative
properties
Example 3.
Democratic peace
Example 4.
Emergence of the territorial state
Distributional
properties
Example 2.
State-size distributions
Example 1.
War-size distributions
13
Simulating global democratization
Source:Cederman &Gleditsch 2004
Year
Pro
port
ion
of d
emoc
raci
es
1850 1900 1950 2000
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
Proportion of democraciesProportion at war
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A simulated democratic outcome
t = 0 t = 10,000
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Applying Geosim to world politics
Configurations Processes
Qualitative
properties
Example 3.
Democratic peace
Example 4.
Emergence of the territorial state
Distributional
properties
Example 2.
State-size distributions
Example 1.
War-size distributions
16
The initial state of OrgForms
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Modeling technological change
0.2
.4.6
.81
Dis
cout
ing
0 5 10 15 20Distance
t = 0 t = 500
t = 1000
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OrgForms: A dynamic network model
TechnologicalProgress
Conquest
OrganizationalBypass
SystemsChange
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Indirect rule in the “Middle Ages”
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Replications with moving threshold and slope
0.2
.4.6
.8In
dire
ct r
ule
ratio
0 500 1000 1500time
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GeoSim 5
Exploring geopolitics using agent-based modeling
OrgFormsGeoSim 0
GeoContestGeoSim 4
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Toward more realistic models of civil wars
• Our strategy:– Step I: extending Geosim
framework– Step II: conducting empirical
research– Step III: back to computational
modeling
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Step I: Modeling nationalist insurgencies
• Target Fearon & Laitin. 2003. Ethnicity, Insurgency, and Civil War. American Political Science Review 97: 75-90
• Weak states that cannot control their territory are more prone to insurgency
• Use agent-based modeling to articulate identity-based mechanisms of insurgency
• Will appear in Cederman (forthcoming). Articulating the Geo-Cultural Logic of Nationalist Insurgency. In Order, Conflict, and Violence, eds. Kalyvas & Shapiro. Cambridge University Press.
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Step I: Main building blocks
32144421
3##44#2#
• National identities
• Cultural map
• State system
• Territorial obstacles
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Step I: An artificial system
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Step I: Conclusions
• Important hunches:– Going beyond macro correlations– Developing mechanisms based on
explicit actor constellations– Focus on center-periphery power
balance– Location of ethnic groups crucial
• But the model is too complex and artificial
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Step II: Empirical research
• Beyond fractionalization (Cederman & Girardin, forthcoming in the APSR)
• Expert Survey of Ethnic Groups (Cederman, Girardin & Wimmer, in progress)
• Geo-Referencing of Ethnic Groups (Cederman, Rød & Weidmann, just completed)
• Modeling Ethnic Conflict in Center-Periphery Dyads (Buhaug, Cederman & Rød)
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Step II: Constructing the N* index
s0
s1
s2
sn-1
1
0
)(11)Pr(n
i
ipictCivilConfl
…
State-centric ethnic configuration E*:
p(1)
p(2)
p(n-1)
kririp
})({1
1)(
Micro-level mechanism M*:
p(i)
r(i)=0ss
s
i
i
EGIP
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Step II: N* values for Eurasia & N. Africa
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Step II: Expert Survey of Ethnic Groups
Project together with •Luc Girardin (ETH)•Andreas Wimmer (UCLA)Web-based interface in order to expand coding of ethnic groups and their power access to the rest of the world with the help of area experts
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Step II: Geo-Referencing of Ethnic Groups
• Scanning and geo-coding ethnic groups
• Polygon representation
• Based on Atlas Narodov Mira (1964)
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Step II: Ethnic Dyads Calculating distances from capital
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Step II: Ethnic DyadsCalculating mountainous terrain
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Step II: Results from dyadic model
UCDP/PRIO dyadic ethnic conflict, 1946–99 (4) (5) (6) Group-level variables Dyadic power balance r a 0.359 0.470 0.462 (3.49)** (4.97)** (5.45)** Distance from capital
a 0.547 0.744 (1.99)* (3.91)** Mountains 1.243 1.220 (4.05)** (3.34)** Country-level variables GDP capita b –0.070 –0.067 –0.117 (0.65) (0.61) (1.00) Population a, b 0.401 0.222 (3.72)** (1.49) Mountains 0.052 (0.28) Oil –0.336 –0.493 (0.94) (1.21) Instability –0.038 –0.096 (0.05) (0.11) Polity score b 0.015 0.030 (0.46) (1.19) Democracy b 0.759 (3.31)** Year 0.058 0.062 0.063 (4.85)** (5.00)** (4.89)** Constant –120.365 –130.176 –131.033 (5.10)** (5.19)** (5.01)** N 33,607 33,607 33,607
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Step III: GROWLab
• Technical approach– Follow same tradition as other toolkits, but higher level of
abstraction– Tailored to geopolitical modeling, but might be useful to
others– Java based; targeted at programming literates
• Main features– Support for agent hierarchies– Support for complex spatial relationships (e.g. borders)– Support for GIS data (raster with geodetic distance
computation)• Discrete spaces• Integrated GUI• Comes with 13 example models• Batch runs (cluster support in development)• Available at: http://www.icr.ethz.ch/research/growlab/
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Step III: GROWLab
