1. Emergent Structure Models: Applications to World Politics
Prof. Lars-Erik Cederman
Center for Comparative and International Studies (CIS) Seilergraben 49, Room G.2,
Christa Deiwiks, CIS Room E.3,
Week 12

2. Applying Geosim to World Politics
Configurations
Processes
Qualitative
properties
Example 3.
Democratic peace
Example 4.
Emergence of the territorial state
Distributional
Example 2.
State-size distributions
Example 1.
War-size distributions

3. Cumulative war-size plot, 1820-1997
Data Source:
Correlates
of War
Project (COW)

4. Self-organized criticality
Per Bak’s sand pile
Power-law distributed
avalanches in a rice pile

5. Theory: Self-organized criticality
log f f
Input
Output
s-a
log s
Complex System s
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

6. War clusters in Geosim
t = 3,326
t = 10,000

7. Simulated cumulative war-size plot
log P(S > s)
(cumulative
frequency)
log s
(severity)
log P(S > s) = 1.68 – 0.64 log s
N = R2 = 0.991
See “Modeling the Size of Wars” American Political Science Review Feb. 2003

8. Applying Geosim to world politics
Configurations
Processes
Qualitative
properties
Example 3.
Democratic peace
Example 4.
Emergence of the territorial state
Distributional
Example 2.
State-size distributions
Example 1.
War-size distributions

9. 2. Modeling state sizes: Empirical data
log Pr (S > s)
(cumulative frequency)
log S ~ N(5.31, 0.79)
MAE = 0.028
1998
log s
(state size)
Data: Lake et al.

10. Simulating state size with terrain

11. Simulated state-size distribution
log s
(state size)
log Pr (S > s)
(cumulative
frequency)
log S ~ N(1.47, 0.53)
MAE = 0.050

12. Applying Geosim to world politics
Configurations
Processes
Qualitative
properties
Example 3.
Democratic peace
Example 4.
Emergence of the territorial state
Distributional
Example 2.
State-size distributions
Example 1.
War-size distributions

13. Simulating global democratization
Source:
Cederman &
Gleditsch 2004

14. A simulated democratic outcome

15. Applying Geosim to world politics
Configurations
Processes
Qualitative
properties
Example 3.
Democratic peace
Example 4.
Emergence of the territorial state
Distributional
Example 2.
State-size distributions
Example 1.
War-size distributions

16. The initial state of OrgForms

17. Modeling technological change

18. OrgForms: A dynamic network model
Conquest
Technological
Progress
Systems
Change
Organizational
Bypass

19. Indirect rule in the “Middle Ages”

20. Replications with moving threshold and slope

21. Exploring geopolitics using agent-based modeling
OrgForms
GeoSim 0
GeoSim 5
GeoSim 4
GeoContest

22. 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

23. Step I: Modeling nationalist insurgencies
Target Fearon & Laitin 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.
Political economists, working mostly in comparative politics, have energized the literature on civil wars.
This has been a very positive trend: it has raised the bar considerably:
* More coherent theories are being proposed, thanks to modeling
* More systematic evidence is presented
Two main problems have appeared, however:
1. There is a bias in favor of logistical and power-related mechanisms at the expense of identity-driven ones;
2. The cross-national research designs obscure contextual effects, such as diffusion
I will address the first of these problems with the current paper.
Quote Sambanis: “The gap between micro-level behavior and macro-level explanation is large.
It is magnified when micro-macro relationships are studied solely through cross-national statistical analyses.
What is often lost in such studies is information about causal pathways that link outcomes with causes.”
Follow political economists’ lead in proposing formal models.
Use agent-based modeling to articulate an alternative ID-based mechanisms of insurgency
that produce similar macro-level patterns as those observed by political economists.
Formal modeling of a slightly different type: opportunity-driven, where the actual configuration is more important
Structural but not reified: structuration?

24. Step I: Main building blocks
National identities
Cultural map
State system
Territorial obstacles
3##44#2#
All this requires a complex model with at least four basic building blocks.
Can only give you an overview -- we’ll have to use the Q & A period to discuss details
I propose a layered ontology:
First, at the bottom are the artificial mountains, that introduce obstacles to power extraction and projection in rugged terrain
Second, there is the political heart of the model: a state system with states as hierarchical organizations that interact and can fight wars.
In addition, capitals dominate provinces, but the latter can rebel and secede from the states.
Third, there is a cultural map based on a vector representation. Each site in the grid, provinces or capitals, have their own culture represented as a string of 8 digits with four values.
This can be seen as an abstract formalization of culture, be it language, religion, dress code, or any other ethnic trait.
Fourth, in order to get nationalist politics, we need to move one more step up to national identities, that are coalitions based on combinations of cultural traits, where only a subset of the possible cultural raw material needs to be relevant. Wild cards are introduced for traits that do not matter. This means that we can formally model the thickness of identity. Each capital or province can belong to one nation only, with capitals being more likely to create their own national identities.

25. Step I: An artificial system

26. 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

27. 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)

28. Step II: Constructing the N* index
State-centric ethnic configuration E*:
Micro-level
mechanism M*:
p(1) s1
EGIP
p(2) s0
p(i) s2 …
p(n-1)
sn-1
Here it is:
let us first assume that we have a start-like ethnic configuration with an ethnic partisan state at the center.
Here we are not assuming that everybody interacts with everybody, but that there are as many dyads as there are peripheral actors.
See ABM
Based on opportunity structures, we can now model the probability that violence will break out as one minus the product that each dyad will remain peaceful.
We now need to specify the dyadic probability of conflict, p(i). Let us assume that violence is more likely when the power balance is favorable for the challengers.
This can be done by letting a logistical function that approaches 1 as the relative power balance tips in favor of the peripheral group i.
Two parameters r and k determine the shape of the curve: r tells us where the threshold is located, and k fixes the curve’s steepness
r(i)=

29. Step II: N* values for Eurasia & N. Africa
Here is the world according to the N* index. Irak and United Arab Emirates are highlighted, but Afghanistan and large portions of the near abroad are also selected.

30. 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

31. Step II: Geo-Referencing of Ethnic Groups
Scanning and geo-coding ethnic groups
Polygon representation
Based on Atlas Narodov Mira (1964)

32. Step II: Ethnic Dyads Calculating distances from capital

33. Step II: Ethnic Dyads Calculating mountainous terrain

34. Step II: Results from dyadic model

35. Step III: GROWLab Technical approach Main features Discrete spaces
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:
Domain specific

36. Step III: GROWLab