ABM – GIS Point-like agents Shape-like agents Rasterized agents Multiple layers

ABM - GIS Point-like agents Shape-like agents Rasterized agents Multiple layers www.phwiki.com

ABM – GIS Point-like agents Shape-like agents Rasterized agents Multiple layers

Corrao, Joe, Morning Show Executive Producer has reference to this Academic Journal, PHwiki organized this Journal Lars-Erik Cederman in addition to Luc Girardin Center as long as Comparative in addition to International Studies (CIS) Swiss Federal Institute of Technology Zurich (ETH) http://www.icr.ethz.ch/teaching/compmodels Advanced Computational Modeling of Social Systems 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 Point-like agents Pedestrian-oriented l in addition to uses in central Leeds RBSim – Recreation Behavior Simulator

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Shape-like agents Schelling GIS model of Chicago based on Zip code areas Rasterized agents Sprawlsim – model of suburban sprawl Multiple layers Properties Topography, l in addition to cover, soils, zoning Networks Hydrologic, transportation, social, in addition to communication Diffusion models In as long as mation transfer, positive in addition to negative spatial spillovers, transport of pollutants, species migration

Key relationships Causal Identity Temporal Topological GIS in addition to ABM Types of reconstructions

Geosim Emergent Actors in World Politics (Princeton University Press, 1997) Inspired by Bremer in addition to Mihalka (1977) in addition to Cusack in addition to Stoll (1990) Originally programmed in Pascal then ported to Swarm, in addition to 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 Data Source: Correlates of War Project (COW)

Self-organized criticality Per Bak’s s in addition to pile Power-law distributed avalanches in a rice pile 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 R2 = 0.991 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 t = 0 t = 10,000 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 in addition to postulated micro-level mechanisms Use computational modeling to articulate identity-based mechanisms of insurgency that also depend on state strength in addition to rugged terrain Main building blocks National identities Cultural map State system Territorial obstacles The model’s telescoped phases t = 0 1000 1200 2200 Phase I Initialization Phase II State as long as mation & Assimilation Phase III Nation-building Phase IV Civil war assimilation identity- as long as mation nationalist collective action

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 in addition to O’Mahony (2004) offer an explanation based on changes among democracies in the 19th in addition to 20th centuries My argument: nationalism caused the shift in state sizes Technological progress State size

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Territorial state sizes log Pr (S > s) log s log s log Pr (S > s) 1815 1998 Data: Lake et al. log S ~ N(4.98, 1.02) MAE = 0.048 log S ~ N(5.31, 0.79) MAE = 0.028 Estimated means, 1815-1998 log s m Year 1800 1850 1900 1950 2000 Nested processes

A sample system at t = 0 The sample system at t = 2000 t = 2054

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 = 0.046 log S ~ N(1.28, 0.09) MAE = 0.040

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