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 lcederman@ethz.ch http://www.icr.ethz.ch/teaching/konflikt Assistent: Tim Dertwinkel CIS, Raum E.3

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

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

Disaggregated modeling If <cond> then <action1> else <action2> Inanimate agents Observer Animate agents Data Organizations of agents Artificial world

A view from the Berlin television tower

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

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

Sample run 1 Schelling's Segregation Model

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

Europe in 1500

Europe in 1900

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

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

Sample Run 2 Geosim Base Model

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

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

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

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)

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

Simulating global democratization Source: Cederman & Gleditsch 2004

A simulated democratic outcome

Trajectories of democratization Democratic share of territory Without collective security Democratic share of territory With collective security 0 2000 4000 6000 8000 10000 Time 0 2000 4000 6000 8000 10000 Time

Sample run 3 Geosim Insurgency Model

Simulationsergebnisse