Emergent Structure Models: Applications to World Politics Prof. - PowerPoint PPT Presentation

Introduction to Computational Modeling of Social Systems Emergent Structure Models: Applications to World Politics Prof. Lars-Erik Cederman Center for Comparative and International Studies (CIS) Seilergraben 49, Room G.2, lcederman@ethz.ch Christa Deiwiks, CIS Room E.3, deiwiks@icr.gess.ethz.ch http://www.icr.ethz.ch/teaching/compmodels Week 12

Applying Geosim to World Politics 2 Configurations Processes Qualitative Example 3. Example 4. properties Democratic peace Emergence of the territorial state Distributional Example 2. Example 1. properties State-size War-size distributions distributions

Cumulative war-size plot, 1820-1997 3 log P(S>s) (cumulative frequency) 1.0 log P(S>s) = 1.27 – 0.41 log s 2 R = 0.985 N = 97 0.1 WWI Data Source: Correlates WWII of War 0.01 2 3 4 5 6 7 8 log s Project (COW) 10 10 10 10 10 10 10 (severity)

Self-organized criticality 4 Power-law distributed Per Bak’s sand pile avalanches in a rice pile

Theory: Self-organized criticality 5 log f f Input Output s - α log s s Complex System • Slowly driven systems that fluctuate around state of marginal stability while generating non-linear output according to a power law. • Examples: sandpiles, semi-conductors, earthquakes, extinction of species, forest fires, epidemics, traffic jams, city populations, stock market fluctuations, firm size

War clusters in Geosim 6 t = 3,326 t = 10,000

Simulated cumulative war-size plot 7 log P( S > s ) (cumulative frequency) log P( S > s ) = 1.68 – 0.64 log s N = 218 R 2 = 0.991 log s (severity) 2 3 4 5 6 7 See “Modeling the Size of Wars” American Political Science Review Feb. 2003

Applying Geosim to world politics 8 Configurations Processes Qualitative Example 3. Example 4. properties Democratic peace Emergence of the territorial state Distributional Example 2. Example 1. properties State-size War-size distributions distributions

2. Modeling state sizes: Empirical data 9 log Pr ( S > s ) (cumulative frequency) log S ~ N(5.31, 0.79) MAE = 0.028 log s 1998 (state size) Data: Lake et al.

Simulating state size with terrain 10

Simulated state-size distribution 11 log Pr ( S > s ) (cumulative frequency) log S ~ N(1.47, 0.53) MAE = 0.050 log s (state size)

Applying Geosim to world politics 12 Configurations Processes Qualitative Example 3. Example 4. properties Democratic peace Emergence of the territorial state Distributional Example 2. Example 1. properties State-size War-size distributions distributions

Simulating global democratization 13 0.5 0.5 Proportion of democracies Proportion at war 0.4 0.4 Proportion of democracies 0.3 0.3 0.2 0.2 0.1 0.1 Source: 0.0 0.0 Cederman & Gleditsch 2004 1850 1900 1950 2000 Year

A simulated democratic outcome 14 t = 0 t = 10,000

Applying Geosim to world politics 15 Configurations Processes Qualitative Example 3. Example 4. properties Democratic peace Emergence of the territorial state Distributional Example 2. Example 1. properties State-size War-size distributions distributions

The initial state of OrgForms 16

Modeling technological change 17 1 .8 Discouting .6 .4 .2 0 0 5 10 15 20 Distance t = 0 t = 500 t = 1000

OrgForms : A dynamic network model 18 Conquest Technological Systems Progress Change Organizational Bypass

Indirect rule in the “Middle Ages” 19

Replications with moving threshold and slope 20 .8 Indirect rule ratio .6 .4 .2 0 0 500 1000 1500 time

Exploring geopolitics using agent-based modeling 21 OrgForms GeoSim 0 GeoSim 5 GeoSim 4 GeoContest

Toward more realistic models of civil wars 22 • Our strategy: – Step I: extending Geosim framework – Step II: conducting empirical research – Step III: back to computational modeling

Step I: Modeling nationalist insurgencies 23 • Target Fearon & Laitin. 2003. Ethnicity, Insurgency, and Civil War. American Political Science Review 97 : 75-90 • Weak states that cannot control their territory are more prone to insurgency • Use agent-based modeling to articulate identity- based mechanisms of insurgency • Will appear in Cederman (forthcoming). Articulating the Geo-Cultural Logic of Nationalist Insurgency. In Order, Conflict, and Violence , eds. Kalyvas & Shapiro. Cambridge University Press.

Step I: Main building blocks 24 • National identities 3##44#2# • Cultural map 32144421 • State system • Territorial obstacles

Step I: An artificial system 25

Step I: Conclusions 26 • Important hunches: – Going beyond macro correlations – Developing mechanisms based on explicit actor constellations – Focus on center-periphery power balance – Location of ethnic groups crucial • But the model is too complex and artificial

Step II: Empirical research 27 • Beyond fractionalization (Cederman & Girardin, forthcoming in the APSR ) • Expert Survey of Ethnic Groups (Cederman, Girardin & Wimmer, in progress) • Geo-Referencing of Ethnic Groups (Cederman, Rød & Weidmann, just completed) • Modeling Ethnic Conflict in Center- Periphery Dyads (Buhaug, Cederman & Rød)

Step II: Constructing the N * index 28 State-centric ethnic Micro-level configuration E *: mechanism M *: p (1) s 1 EGIP 1 = p ( i ) p (2) − + k 1 { r ( i ) r } p ( i ) s 0 s 2 … p ( n -1) s n -1 − n 1 ( ) ∏ = − − Pr( CivilConfl ict ) 1 1 p ( i ) s = r ( i )= i 0 i + s s i 0

Step II: N* values for Eurasia & N. Africa 29

Step II: Expert Survey of Ethnic Groups 30 Project together with •Luc Girardin (ETH) •Andreas Wimmer (UCLA) Web-based interface in order to expand coding of ethnic groups and their power access to the rest of the world with the help of area experts

Step II: Geo-Referencing of Ethnic Groups 31 • Scanning and geo- coding ethnic groups • Polygon representation Hunga Hung HU HU 19 • Based on Atlas Slovenia Slovenia ^ ! 3 Ljubljana Ljubljana 4 HR ! ^ Croatia HR Croatia 4,19 Narodov Mira 6 Zagreb 6 Zagreb 1 6 1 1 4,6 4,6 6 6 4,17 4 5,6 (1964) 4 4,6 5 17 5 5 5 5 5,6 4,17 4 5 4 6 5,6 5 5,6 5 4 4 5,6 5 4 5 4 4 5 5 5 6 BA BA 4 5 Bosnia & Herzegovina Bosnia & Herzegovina 4,5 4,6 5,6 4 4 4 4,6 6 ^ ! Sarajevo Sarajevo 5,6 5 4 5 4 5 5

Step II: Ethnic Dyads Calculating distances from capital 32

Step II: Ethnic Dyads Calculating mountainous terrain 33

Step II: Results from dyadic model 34 UCDP/PRIO dyadic ethnic conflict, 1946–99 (4) (5) (6) Group-level variables Dyadic power balance r a 0.359 0.470 0.462 (3.49)** (4.97)** (5.45)** a Distance from capital 0.547 0.744 (1.99)* (3.91)** Mountains 1.243 1.220 (4.05)** (3.34)** Country-level variables GDP capita b –0.070 –0.067 –0.117 (0.65) (0.61) (1.00) Population a, b 0.401 0.222 (3.72)** (1.49) Mountains 0.052 (0.28) Oil –0.336 –0.493 (0.94) (1.21) Instability –0.038 –0.096 (0.05) (0.11) Polity score b 0.015 0.030 (0.46) (1.19) Democracy b 0.759 (3.31)** Year 0.058 0.062 0.063 (4.85)** (5.00)** (4.89)** Constant –120.365 –130.176 – 131.033 (5.10)** (5.19)** (5.01)** N 33,607 33,607 33,607

Step III: GROWLab 35 • Technical approach – Follow same tradition as other toolkits, but higher level of abstraction – Tailored to geopolitical modeling, but might be useful to others – Java based; targeted at programming literates • Main features – Support for agent hierarchies – Support for complex spatial relationships (e.g. borders) – Support for GIS data (raster with geodetic distance computation) • Discrete spaces • Integrated GUI • Comes with 13 example models • Batch runs (cluster support in development) • Available at: http://www.icr.ethz.ch/research/growlab/

Step III: GROWLab 36