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

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

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

4. 4 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”

5. 5 Schelling’s segregation model

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

7. 7 Europe in 1500

8. 8 Europe in 1900

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

10. 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. 11 Classes Model Actor Relation ModelGUI ModelBatch

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

13. 13 Main simulation loop resource updating resource allocation decisions inter- actions structural change initiation phase

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

15. 15 Resource allocation fixedRes(i,j) = (1-propMobile) * res / n mobileRes = probMobile * res for all relations j do in case i and j were fighting in the last period then mobileRes(i,j) = res(j,i)/enemyRes(i)*mobileRes in case i and j were not fighting the last period then mobileRes(i,j) = res(j,i)/(enemyRes(i)+res(j,i))*mobileRes res(i,j) = fixedRes(i,j) + mobileRes(i,j)

16. 16 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 front then select a neighboring state j’ with res(i,j’)/res(j’,i) > superiorityThreshold do launch unprovoked attack against j’

17. 17 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

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

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

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

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

22. 22 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 = 0.991 See “Modeling the Size of Wars” American Political Science Review Feb. 2003

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

24. 24 2. Modeling state sizes: Empirical data log s (state size) log Pr (S > s) (cumulative frequency) 1998 Data: Lake et al. log S ~ N(5.31, 0.79) MAE = 0.028

25. 25 Simulating state size with terrain

26. 26 Simulated state-size distribution log s (state size) log Pr (S > s) (cumulative frequency) log S ~ N(1.47, 0.53) MAE = 0.050

27. 27 Applying Geosim to world politics ProcessConfiguration Distributional properties Example 1. War-size distributions Example 2. State-size distributions Qualitative properties Example 4. Nationalist insurgencies Example 3. Democratic peace

28. 28 Simulating global democratization Source: Cederman & Gleditsch 2004

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

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

31. 31 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

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

33. 33 The model’s telescoped phases t = 01000 1200 2200 Phase I Initialization Phase II State formation & Assimilation Phase III Nation-building Phase IV Civil war assimilation identity- formation nationalist collective action

34. 34 Sample run 3 Geosim Insurgency Model