Emergent Behavior in the International Political Systems

Lars-Erik CedermanCenter for Comparative and International Studies (CIS)

ETH Zurichhttp://www.icr.ethz.ch

Presentation prepared for lecture series “Complexity in the Social Sciences”Stockholm, September 28. 2007

Emergent Behavior in the International Political Systems

http://www.icr.ethz.ch/

2

Method: Agent-based modeling

• ABM is a computational methodology that allows the researcher to create, analyze, and experiment with, artificial worlds populated by agents that interact in non-trivial ways

• Bottom-up• Computational• Builds on CAs and DAI

3Disaggregated modeling

Organizations of agents

Animate agents

Data

Artificial world

Observer

Inanimate agents

If <cond> then

<action1>else

<action2>

If <cond> then

<action1>else

<action2>

4

A view from the Berlin television tower

5

Neighborhood segregation

Thomas C. SchellingMicromotives and

Macrobehavior

< 1/3

Micro-level rules of the game

Stay if at least a third of neighbors are “kin”

Move to random location otherwise

6Sample run 1

• Schelling's Segregation Model

7

Emergent results from Schelling’s segregation model

Time

Happiness

Time

Number ofneighborhoods

8Europe in 1500

9Europe in 1900

10

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

11Geosim

• Geosim uses Repast, a Java toolkit

• States are hierarchical, bounded actors interacting in a dynamic network imposed on a grid

• Emergent Actors in World Politics (PUP 1997)

12Sample Run 2

• Geosim Base Model

13Emergent results from the run

Time

Proportion ofsecure areas

Time

Number ofstates

14Possible outcomes

15-statemultipolarity(sample run)

bipolarity unipolarity

7-statemultipolarity

15Microeconomics ABM

Analytical Synthetic approachEquilibrium Non-equilibrium theoryNomothetic Generative methodVariable-based Configurative ontology

16

Analytical Synthetic approach

• Hope to solve problems through strategy of “divide and conquer”

• Need to make ceteris paribusassumption

• But in complex systems this assumption breaks down

• Herbert Simon: Complex systems are composed of large numbers of parts that interact in a non-linear fashion

• Need to study interactions explicitly

17

Equilibrium Non-equilibrium theory

• Standard assumption in the social sciences: “efficient” history

• But contingency and positive feedback undermine this perspective

• Complexity theory and non-equilibrium physics

• Statistical regularities at the macro level despite micro-level contingency

Example: Avalanches in rice pile

18

NomotheticGenerative method

• Search for causal regularities• Hempel’s “covering laws”• But what to do with complex

social systems that have few counterparts?

• Scientific realists explain complex patterns by deriving the mechanisms that generate them

• Axelrod: “third way of doing science”

• Epstein: “if you can’t grow it, you haven’t explained it!”

19

Variable-based Configurative ontology

• Conventional models are variable-based

• Social entities are assumed implicitly

• But variables say little about social forms

• A social form is a configuration of social interactions and actors together with the structures in which they are embedded

• ABM good at endogenizinginteractions and actors

• Object-orientation is well suited to capture agents

20

Applying Geosim to world politics

Configurations Processes

Qualitativeproperties

Example 3. Democratic peace

Example 4. Emergence of the territorial state

Distributionalproperties

Example 2.State-size distributions

Example 1. War-size distributions

21

Cumulative war-size plot, 1820-1997

Data Source:Correlatesof WarProject (COW)

1.0

0.1

0.01

log P(S>s) = 1.27 – 0.41 log s

2 3 4 5 6 7 810 10 10 10 10 10 10

WWI

WWII

2R = 0.985 N = 97

log P(S>s) (cumulative frequency)

log s (severity)

22

• 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

23Self-organized criticality

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

24War clusters in Geosim

t = 3,326 t = 10,000

25

Simulated cumulative war-size plot

2 73 4 5 6

log P(S > s)(cumulativefrequency)

log s(severity)

log P(S > s) = 1.68 – 0.64 log sN = 218 R2 = 0.991

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

26









27

2. Modeling state sizes: Empirical data

log s(state size)

log Pr (S > s)(cumulative frequency)

1998Data: Lake et al.

log S ~ N(5.31, 0.79)MAE = 0.028

28

Simulating state size with terrain

29Simulated state-size distribution

log s(state size)

log Pr (S > s)(cumulative frequency)

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

30









31

Simulating global democratization

Source:Cederman &Gleditsch 2004

Year

Pro

porti

on o

f dem

ocra

cies

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 democraciesProportion at war

32A simulated democratic outcome

t = 0 t = 10,000

33

0 1e+042e+03 4e+03 6e+03 8e+030 1e+042e+03 4e+03 6e+03 8e+03

Replications with regime change

Democratic shareof territory

Democratic shareof territory

0 2000 4000 6000 8000 10000 Time 0 2000 4000 6000 8000 10000 Time

Withcollective security

Withoutcollective security

34









35

The initial state of OrgForms

36Taxation in a linear state

0.6 0.6

0.4

.576

.4

0.4

0.6

1.224

Tax: k= .5

Discount: δ = .8

1 2 3 4 5

1.576

37

Modeling technological change

0.2

.4.6

.81

Dis

cout

ing

0 5 10 15 20Distance

t = 0 t = 500t = 1000

38

OrgForms: A dynamic network model

TechnologicalProgress

Conquest

OrganizationalBypass

SystemsChange

39

Indirect rule in the “Middle Ages”

40

Direct rule in the modern system

41

Replications with moving threshold and slope

0.2

.4.6

.8In

dire

ct ru

le ra

tio

0 500 1000 1500time

42

GeoSim 5

Exploring geopolitics using agent-based modeling

OrgFormsGeoSim 0

GeoContestGeoSim 4

43

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

44

Step I: Nationalist insurgency model

Use agent-based modeling to articulate identity-based mechanisms of insurgency

Forthcoming in Order, Conflict, and Violence, eds. Kalyvas & Shapiro. Cambridge University Press.

45Step II: Empirical research

• Beyond fractionalization, EGIP, N*(Cederman & Girardin, APSR 2007)

• GREG: Geo-Referencing of Ethnic Groups (Cederman, Rød & Weidmann completed)

• ESEG: Expert Survey of Ethnic Groups (Cederman, Wimmer, Girardin & Min, in progress)

• GREG-II (Cederman, Weidmann & Rød)

46

Step II: GREGGeo-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

46

4

4,6

5

4

4,19

4

6

4,6

5

5

5

6

4

5,6

4

5

4,17

4,65,6

5,6

4,6

6

5

4

44

4

5

4,17

5,6

5

1

5

6

55,64

5

1

6

5

5,6

1

17

19

LjubljanaLjubljana

ZagrebZagreb

SarajevoSarajevo

SloveniaSlovenia

HungHunga

CroatiaCroatia

Bosnia & HerzegovinaBosnia & Herzegovina

HUHU

BABA

HRHR

47GREG: First results

0.0

2.0

4.0

6.0

8P

r(con

flict

)

0 .2 .4 .6 .8r

Median dyad Distant Distant and mountainous

48

Step II: ESEGExpert Survey of Ethnic Groups

Collaboration with Andreas Wimmer and Brian Min (UCLA)

Web-based interface in order to expand coding of politically relevant ethnic groups and their power access to the rest of the world with the help of area experts

4949Step 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 several example models• Batch runs (cluster support in development)

50

The architecture of GROWLab

51GROWLab: User interface

52GROWLab: GIS Data

123456

Types of GIS data: (1) country borders, (2) ethnic groups, (3) population, (4) GDP, (5) elevation, and (6) vegetation

53

Where to find more models: Links

• Our class web pages (also see /archive): http://www.icr.ethz.ch/teaching/compmodels

• Santa Fe Institute: http://www.santafe.edu/• Center for the Study of Complex Systems at the

University of Michigan: http://www.pscs.umich.edu/• European web sites on Computer simulation of societies

http://www.soc.surrey.ac.uk/research/simsoc/ and “European Social Simulation Association” http://essa.eu.org/

• For the US counterpart, see http://www.dis.anl.gov/naacsos• Leigh Tesfatsions’s site on computational economics:

http://www.econ.iastate.edu/tesfatsi/ace.htm• See also the Journal of Artificial Societies and Social

Simulation: http://jasss.soc.surrey.ac.uk/JASSS.html

http://www.icr.ethz.ch/teaching/compmodels

http://www.santafe.edu/

http://www.pscs.umich.edu/

http://www.soc.surrey.ac.uk/research/simsoc/

http://essa.eu.org/

http://www.dis.anl.gov/naacsos/home.html

http://www.econ.iastate.edu/tesfatsi/ace.htm

http://jasss.soc.surrey.ac.uk/JASSS.html

54RePast framework

Controlling simulations

Managingparameters

Displaying behavior

Charting

55Development Environment

int a = 12;if (a == b)

b++;else

a++;

Java source code

EditorJava

compiler

Java libs

Integrated Development Environmente.g. IDEA

JVM

Repast libs

Documents

Emergent Behavior in the International Political Systems