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) http://www.icr.ethz.ch/teaching/compmodels

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.

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

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

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)

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

Simulated cumulative war-size plot log P(S > s) (cumulative frequency) log P(S > s) = 1.68 – 0.64 log s N = 218 R2 = 0.991 Does the sample run generate a power-law distribution? Yes! Wit a R^2 at 0.991 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 0.975 and highest at 0.996. Look at histograms: log s (severity) See “Modeling the Size of Wars” American Political Science Review Feb. 2003

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

Simulating global democratization Source: Cederman & Gleditsch 2004

A simulated democratic outcome

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

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

Main building blocks National identities Cultural map State system Territorial obstacles 3##44#2# 32144421

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!

Sample run 3 Geosim Insurgency Model

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

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

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.

Estimated means, 1815-1998 m log s 1800 1850 1900 1950 2000 Year

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.

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.

The sample system at t = 2000

t = 2054

t = 2060

t = 2813

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.

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