Introduction to Computational Modeling of Social Systems Emergent

Introduction to Computational Modeling of Social Systems Emergent Actor Models Prof. Lars-Erik Cederman • Prof. Lars-Erik Cederman Center for Comparative and International Studies (CIS) Seilergraben 49, Room lcederman@ethz. ch Studies • Center for Comparative and. G. 2, International Nils Weidmann, CIS Room E. 3, weidmann@icr. gess. ethz. ch (CIS) http: //www. icr. ethz. ch/teaching/compmodels Seilergraben 49, Room G. 2, Lecture, Januarylcederman@ethz. ch 25, 2005 • Nils Weidmann, CIS Room E. 3,

Emergent social forms 2 Emergent interaction patterns Emergent boundaries and networks actor actor actor Emergent property configurations actor actor actor actor actor Emergent Dynamic Networks

Sociational theory 3 • Georg Simmel’s “Vergesellschaftung” • Entity processes: – – Creation Death Amalgamation Division Existential processes Boundary processes Georg Simmel

The finite-agent method • Andrew Abbott “On Boundaries”: going beyond variable-oriented modeling • Grow composite actors with endogenous boundaries based on a “soup of preexisting actors” 4

Schelling’s segregation model 5

Emergent results from Schelling’s segregation model Number of neighborhoods Happiness Time 6

Europe in 1500 7

Europe in 1900 8

“States made war and war made the state” Charles Tilly 9

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 10

Classes • Model • Actor • Relation • Model. GUI • Model. Batch 11

Model architecture 12 Actor Relation x, y res capital neighs Relation owner other twin act, res. . pol, prov Actor x, y res capital neighs

Main simulation loop 13 initiation phase resource updating resource allocation decisions interactions structural change

Resource updating res = res. Unit for all provinces j of state i do res = res + res. Unit 14

Resource allocation fixed. Res(i, j) = (1 -prop. Mobile) * res / n mobile. Res = prob. Mobile * res for all relations j do in case i and j were fighting in the last period then mobile. Res(i, j) = res(j, i)/enemy. Res(i)*mobile. Res in case i and j were not fighting the last period then mobile. Res(i, j) = res(j, i)/(enemy. Res(i)+res(j, i))*mobile. Res res(i, j) = fixed. Res(i, j) + mobile. Res(i, j) 15

Decision rule of actor i for all external fronts j do if i or j fought in the previous period then attack j else cooperate with j {Grim Trigger} if there is no action on any select a neighboring state with res(i, j’)/res(j’, i) > launch unprovoked attack front then j’ superiority. Threshold do against j’ 16

Structural change: conquest • Conquest follows victorious battles • Each attacker randomly selects a “battle path” consisting of an attacking province and a target • The outcome depends on the target’s nature: – if it is an atom, the whole target is absorbed – if it is a capital, the target state collapses – if it is a province, the target is absorbed 17

Guaranteeing territorial contiguity Conquest. . . resulting in. . . i partial state collapse "near abroad" cut off from capital Target Province Agent Province 18 j*

Applying Geosim to world politics Process Configuration Distributional properties Example 1. War-size distributions Example 2. State-size distributions Qualitative properties Example 4. Nationalist insurgencies Example 3. Democratic peace 19

Cumulative war-size plot, 1820 -1997 Data Source: Correlates of War Project (COW) 20

Self-organized criticality Per Bak’s sand pile 21 Power-law distributed avalanches in a rice pile

Simulated cumulative war-size plot 22 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) See “Modeling the Size of Wars” American Political Science Review Feb. 2003

Applying Geosim to world politics Process Configuration Distributional properties Example 1. War-size distributions Example 2. State-size distributions Qualitative properties Example 4. Nationalist insurgencies Example 3. Democratic peace 23

2. Modeling state sizes: Empirical data log Pr (S > s) (cumulative frequency) log S ~ N(5. 31, 0. 79) MAE = 0. 028 1998 Data: Lake et al. log s (state size) 24

Simulating state size with terrain 25

Simulated state-size distribution 26 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 Process Configuration Distributional properties Example 1. War-size distributions Example 2. State-size distributions Qualitative properties Example 4. Nationalist insurgencies Example 3. Democratic peace 27

Simulating global democratization Source: Cederman & Gleditsch 2004 28

A simulated democratic outcome t=0 29 t = 10, 000

Applying Geosim to world politics Process Configuration Distributional properties Example 1. War-size distributions Example 2. State-size distributions Qualitative properties Example 4. Nationalist insurgencies Example 3. Democratic peace 30

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 microlevel mechanisms • Use computational modeling to articulate identity-based mechanisms of insurgency that also depend on state strength and rugged terrain 31

Main building blocks 32 • National identities 3##44#2# • Cultural map • State system • Territorial obstacles 32144421

The model’s telescoped phases t=0 Phase I Initialization 1000 Phase II State formation & Assimilation assimilation 1200 Phase III Nation-building identityformation 33 2200 Phase IV Civil war nationalist collective action

Sample run 3 • Geosim Insurgency Model 34