1. Lars-Erik Cederman and Luc Girardin
Advanced Computational Modeling of Social Systems
Lars-Erik Cederman and Luc Girardin
Center for Comparative and International Studies (CIS) Swiss Federal Institute of Technology Zurich (ETH)

2. Types of reconstructions
Process
Configuration
Distributional
properties
Qualitative
The sample runs that I just showed you both belong to the lower right cell, here labeled 3.
I will now invite you to a very quick guided tour illustrating what Geosim is capable of. Obviously we will not be able to study each model in any detail, but I hope this survey will give you a flavor of the possibilities.
Moving clock-wise from the upper left corner, I will start by considering Example 1, which features war-size distributions. This case requires the researcher to reconstruct the distributional profile of an on-going process.

3. Geosim
Emergent Actors in World Politics (Princeton University Press, 1997)
Inspired by Bremer and Mihalka (1977) and Cusack and Stoll (1990)
Originally programmed in Pascal then ported to Swarm, and finally implemented in Repast

4. Applying Geosim to world politics
War-size distributions
Democratic peace
Nationalist insurgencies
State-size distributions

5. Cumulative war-size plot, 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.
Robustness: see Levy + Hui
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)

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

7. 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

8. Applying Geosim to world politics
War-size distributions
Democratic peace
Nationalist insurgencies
State-size distributions

9. Simulating global democratization
Source:
Cederman &
Gleditsch 2004

10. A simulated democratic outcome

11. Applying Geosim to world politics
War-size distributions
Democratic peace
Nationalist insurgencies
State-size distributions

12. 4. Modeling civil wars
Political economists argue that effectiveness of insurgency depends on projection of state power in rugged terrain rather than on ethnic cohesion
But there is a big gap between macro-level results and postulated micro-level mechanisms
Use computational modeling to articulate identity-based mechanisms of insurgency that also depend on state strength and rugged terrain

13. Main building blocks National identities Cultural map State system
Territorial obstacles
3##44#2#

14. The model’s telescoped phases
1000
1200
2200
Phase I
Initialization
Phase II
State formation
& Assimilation
Phase III
Nation-building
Phase IV
Civil war
identity-
formation
nationalist
collective
action
assimilation
Phased design, meant to capture different historical time scales in a compressed way
Each stage introduces a faster logic while exogenizing earlier stages
Simplification to simplify the research design (model more general!)
Mechanism:
state capitals in basins between mountains
==>
(1) Imperfect cultural state penetration esp. in rough terrain
(2) Nationalist mobilization of periphery incomplete
[oppositional groups will exist in geographic periphery]
(3) Cultural coalitions facilitate collective action, especially where helped by terrain
>> This is where the FL story picks up, but it has ignored from where the groups come and to what extend culture and identities have something to do with their location!

15. Sample run 3
Geosim Insurgency Model

16. Applying Geosim to world politics
War-size distributions
Democratic peace
Nationalist insurgencies
State-size distributions

17. Puzzle
Despite continuing progress, state sizes started declining in the late 19th century
Lake and O’Mahony (2004) offer an explanation based on changes among democracies in the 19th and 20th centuries
My argument: nationalism caused the shift in state sizes
Technological
progress ?
State size

18. Territorial state sizes
log Pr (S > s)
log Pr (S > s)
log S ~ N(4.98, 1.02)
MAE = 0.048
log S ~ N(5.31, 0.79)
MAE = 0.028
1815
log s
1998
log s
Data: Lake et al.

19. Estimated means, m
log s
Year

20. Nested processes
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.

21. A sample system at t = 0
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.

22. The sample system at t = 2000

23. t = 2054

24. t = 2060

25. t = 2813

26. Estimated m-values in 30 simulations
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.

27. Simulated state sizes fitted by log-normal curve
log Pr(S>s)
log Pr(S>s)
log S ~ N(1.28, 0.09)
MAE = 0.040
log S ~ N(1.41, 0.10)
MAE = 0.046
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.
t = 2000
log s
log s
t = 5000