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

2 ABM - GIS Until recently, coupled GIS models of humans- environment interactions were rare Many GIS based biophysical models have been developed (soil erosion, hydrology, etc.) Urban CA models also common Need to include social science data in agent models, as well as create models of spatially intelligent agents A major barrier to building integrated models lies in the static structure of GIS databases

3 Point-like agents Pedestrian-oriented land uses in central Leedscentral Leeds RBSimRBSim - Recreation Behavior Simulator

4 Shape-like agents Schelling GIS model of Chicago based on Zip code areas

5 Rasterized agents SprawlsimSprawlsim - model of suburban sprawl

6 Multiple layers Properties –Topography, land cover, soils, zoning Networks –Hydrologic, transportation, social, and communication Diffusion models –Information transfer, positive and negative spatial spillovers, transport of pollutants, species migration

7 Key relationships Causal Identity Temporal Topological

8 GIS and ABM Relation to GISExamples Fully calibrated empirical modelFull integration of GIS and ABM Partially calibrated empirical modelAt least one GIS layer is integrated while the rest remains abstract Anasazi SchellingGIS Parameterized abstract modelGIS generate parameters that calibrate model Patterned abstract modelGIS reveal qualitative patterns that are modeled Purely abstract modelNoneSchelling Geosim

9 Types of reconstructions ConfigurationProcess Qualitative properties Distributional properties

10 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

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

12 Cumulative war-size plot, Data Source: Correlates of War Project (COW)

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

14 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 = 218 R 2 = See “Modeling the Size of Wars” American Political Science Review Feb. 2003

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

16 Simulating global democratization Source: Cederman & Gleditsch 2004

17 A simulated democratic outcome t = 0 t = 10,000

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

19 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

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

21 The model’s telescoped phases t = Phase I Initialization Phase II State formation & Assimilation Phase III Nation-building Phase IV Civil war assimilation identity- formation nationalist collective action

22 Sample run 3 Geosim Insurgency Model

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

24 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 ?

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

26 Estimated means, log s  Year

27 Nested processes

28 A sample system at t = 0

29 The sample system at t = 2000

30 t = 2054

31 t = 2060

32 t = 2813

33 Estimated  -values in 30 simulations

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