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

SLIDE 1

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

SLIDE 2

2

Applying Geosim to World Politics

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

SLIDE 3

3

Cumulative war-size plot, 1820-1997

Data Source: Correlates

f War

Project (COW)

1.0 0.1 0.01

log P(S>s) = 1.27 – 0.41 log s

2 3 4 5 6 7 8 10 10 10 10 10 10 10

WWI WWII

2

R = 0.985 N = 97

log P(S>s)

(cumulative frequency)

log s (severity)

SLIDE 4

4

Self-organized criticality

Per Bak’s sand pile Power-law distributed avalanches in a rice pile

SLIDE 5

5

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

Theory: Self-organized criticality

Input Output

Complex System

log f log s f s

s-α

SLIDE 6

6

War clusters in Geosim

t = 3,326 t = 10,000

SLIDE 7

7

Simulated cumulative war-size plot

2 7 3 4 5 6

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

SLIDE 8

8

Applying Geosim to world politics

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

SLIDE 9

9

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

SLIDE 10

10

Simulating state size with terrain

SLIDE 11

11

Simulated state-size distribution

log s

(state size)

log Pr (S > s)

(cumulative frequency)

log S ~ N(1.47, 0.53) MAE = 0.050

SLIDE 12

12

Applying Geosim to world politics

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

SLIDE 13

13

Simulating global democratization

Source: Cederman & Gleditsch 2004

Year Proportion of democracies 1850 1900 1950 2000 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5

Proportion of democracies Proportion at war

SLIDE 14

14

A simulated democratic outcome

t = 0 t = 10,000

SLIDE 15

15

Applying Geosim to world politics

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

SLIDE 16

16

The initial state of OrgForms

SLIDE 17

17

Modeling technological change

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

SLIDE 18

18

OrgForms: A dynamic network model

Technological Progress Conquest Organizational Bypass Systems Change

SLIDE 19

19

Indirect rule in the “Middle Ages”

SLIDE 20

20

Replications with moving threshold and slope

.2 .4 .6 .8 Indirect rule ratio 500 1000 1500 time

SLIDE 21

21

GeoSim 5

Exploring geopolitics using agent-based modeling

OrgForms GeoSim 0 GeoContest GeoSim 4

SLIDE 22

22

Toward more realistic models of civil wars

Our strategy:

– Step I: extending Geosim framework – Step II: conducting empirical research – Step III: back to computational modeling

SLIDE 23

23

Step I: Modeling nationalist insurgencies

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.

SLIDE 24

24

Step I: Main building blocks

32144421 3##44#2#

National identities
Cultural map
State system
Territorial obstacles

SLIDE 25

25

Step I: An artificial system

SLIDE 26

26

Step I: Conclusions

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

SLIDE 27

27

Step II: Empirical research

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)

SLIDE 28

28

Step II: Constructing the N* index

s0 s1 s2 sn-1

( )

∏

− =

− − =

1

) ( 1 1 ) Pr(

n i

i p ict CivilConfl

…

State-centric ethnic configuration E*: p(1) p(2) p(n-1)

k

r i r i p

−

+ = } ) ( { 1 1 ) (

p(i) r(i)=

Micro-level mechanism M*:

s s s

i i

+

EGIP

SLIDE 29

29

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

SLIDE 30

30

Step II: Expert Survey of Ethnic Groups

Project together with

Luc Girardin (ETH)
Andreas Wimmer (UCLA)

Web-based interface in

rder to expand coding of

ethnic groups and their power access to the rest of the world with the help of area experts

SLIDE 31

31

Step II: Geo-Referencing of Ethnic Groups

Scanning and geo-

coding ethnic groups

Polygon

representation

Based on Atlas

Narodov Mira (1964)

! ^

6 4 6 3 4 5 5 5,6 4,5 5 5 4,6 4 5 4 5 4 4 6 4 4,6 5 4 4,19 4 6 4,6 5 5 5 6 4 5,6 4 5 4,17 4,6 5,6 5,6 4,6 6 5 4 4 4 4 5 4,17 5,6 5 1 5 6 5 5,6 4 5 1 6 5 5,6 1 17 19 Ljubljana Ljubljana Zagreb Zagreb Sarajevo Sarajevo Slovenia Slovenia Hung Hunga Croatia Croatia Bosnia & Herzegovina Bosnia & Herzegovina HU HU BA BA HR HR

SLIDE 32

32

Step II: Ethnic Dyads Calculating distances from capital

SLIDE 33

33

Step II: Ethnic Dyads Calculating mountainous terrain

SLIDE 34

34

Step II: Results from dyadic model

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)** Distance from capital

a

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

SLIDE 35

35

Step III: GROWLab

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/

SLIDE 36

36