1. Kriegsursachen im historischen Kontext Prof. Dr. Lars-Erik Cederman
Konfliktforschung I:
Kriegsursachen im historischen Kontext
9. Woche: Computersimulation und Konfliktforschung am Beispiel von GeoSim
Prof. Dr. Lars-Erik Cederman
Eidgenössische Technische Hochschule Zürich
Center for Comparative and International Studies (CIS) Seilergraben 49, Raum G.2
Assistent: Tim Dertwinkel
CIS, Raum E.3

2. Outline Introduction to Agent-Based Modeling
Schelling‘s segregation Model
Introduction to Geosim
Applications to conflict research

3. Agent-based modeling
ABM is a computational methodology that allows the analyst to create, analyze, and experiment with artificial worlds populated by agents that interact in non-trivial ways
Bottom-up
Computational
Builds on CAs and DAI

4. Disaggregated modelingIf
<cond>
then
<action1>
else
<action2>
Inanimate agents
Observer
Animate agents
Data
Organizations of agents
Artificial world

5. A view from the Berlin television tower

6. Ethnic neighborhoods
Little Italy, San Diego
Chinatown, New York City

7. Neighborhood segregation
Micro-level rules of the game
Stay if at least a third of neighbors are “kin”
< 1/3
Thomas C. Schelling
Micromotives and Macrobehavior
Move to random location otherwise

8. Sample run 1
Schelling's Segregation Model

9. Emergent results from Schelling’s segregation model
Number of
neighborhoods
Happiness
Time
Time

10. Europe in 1500

11. Europe in 1900

12. “States made war and war made the state” Charles Tilly

13. Geosim Geosim uses Repast, a Java toolkit
States are hierarchical, bounded actors interacting in a dynamic network imposed on a grid

14. Sample Run 2
Geosim Base Model

15. Emergent results from the run
Number of
states
Proportion of
secure areas
Time
Time

16. Possible outcomes 15-state multipolarity (sample run) 7-state
bipolarity
unipolarity

17. Applications Example 1: State formation and war-size distributions
Example 2: Democratization of the international system
Example 3: Nationalist insurgencies

18. Cumulative log-log frequency plot, interstate wars 1820-1997
To check whether Richardson's law still holds up, I used casualty data from the Correlates of War data set based on interstate wars in the last two centuries. (standard data set)
If we plot the cumulative frequency that there is war of a larger size in a logarithmic diagram, the power law turns into a straight line. I.e. like the Richter scale of earthquakes, both axes are logarithmic, both size and cumulative prob.
Obviously for small wars, this probability is close to one. For very large wars, however, the probability is going to be very small.
As you can see, the linear fit is striking, in fact almost spooky!
This result translates into a factor 2.6.
The steeper the line, the more peaceful is the system.
But conventional IR theories have little to say about this finding. Thus it is a true puzzle. We need to turn elsewhere...
Data Source:
Correlates
of War
Project (COW)

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

20. Simulated cumulative war-size plot
log P(S > s)
(cumulative
frequency)
log P(S > s) = 1.68 – 0.64 log s
N = R2 = 0.991
Does the sample run generate a power-law distribution? Yes!
Wit a R^2 at it even surpasses the empirical distribution. Range over four orders of magnitude. The slope of –0.64 is also realistic.
This looks very much like the distribution in the empirical case.
But is this representative?
I have chosen the sample run such that it generates median linear fit out of 15 replications: lowest at and highest at Look at histograms:
log s
(severity)
See “Modeling the Size of Wars” American Political Science Review Feb. 2003

21. Simulating global democratization
Source:
Cederman &
Gleditsch 2004

22. A simulated democratic outcome

23. Trajectories of democratization
Democratic share
of territory
Without
collective security
Democratic share
of territory
With
collective security
Time
Time

24. Sample run 3
Geosim Insurgency Model

25. Simulationsergebnisse