Introduction to
Computational Modeling of Social Systems
Emergent Actor Models
Prof.Prof.
Lars-Erik
Cederman
Lars-Erik
Cederman
Center
for for
Comparative
andand
International
Studies
(CIS)(CIS)
Center
Comparative
International
Studies
Seilergraben
49, 49,
Room
G.2,G.2,
[email protected]
Seilergraben
Room
[email protected]
Weidmann,
Room
[email protected]
NilsNils
Weidmann,
CISCIS
Room
E.3,E.3,
[email protected]
http://www.icr.ethz.ch/teaching/compmodels
http://www.icr.ethz.ch/teaching/compmodels
Lecture, January 25, 2005
Emergent social forms
2
Emergent interaction patterns
Emergent boundaries
and networks
actor
actor
actor
actor
actor
actor
actor
actor
actor
Emergent property
configurations
actor actor actor
actor actor actor
actor actor actor
actor actor actor
actor 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
4
• Andrew Abbott “On Boundaries”: going
beyond variable-oriented modeling
• Grow composite actors with endogenous
boundaries based on a “soup of
preexisting actors”
Schelling’s segregation
model
5
Emergent results from Schelling’s
segregation model
Number of
neighborhoods
Happiness
Time
Time
6
Europe in 1500
7
Europe in 1900
8
“States made war and war
made the state” Charles Tilly
9
Geosim
10
• 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
Classes
11
• Model
• Actor
• Relation
• ModelGUI
• ModelBatch
Model architecture
12
Actor
Actor
Relation
x,y
res
capital
neighs
Relation
owner
other
twin
act,res..
pol,prov
owner
other
twin
act,res..
pol,prov
x,y
res
capital
neighs
Main simulation loop
13
initiation
phase
resource
updating
resource
allocation
decisions
interactions
structural
change
Resource updating
14
res = resUnit
for all provinces j of state i do
res = res + resUnit
Resource allocation
15
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)
Decision rule of actor i
16
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’
superiorityThreshold do
against j’
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
18
Conquest...
resulting in...
"near abroad"
cut off from
capital
Target
Province
Agent
Province
i
partial state collapse
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, 18201997
Data Source:
Correlates
of War
Project (COW)
20
Self-organized criticality
21
Per Bak’s sand pile
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 R2 = 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
0.5
0.4
0.3
0.2
0.0
0.0
Source:
Cederman &
Gleditsch 2004
0.1
0.2
0.3
0.4
Proportion of democracies
Proportion at war
0.1
Proportion of democracies
0.5
28
1850
1900
Year
1950
2000
A simulated democratic outcome
29
t=0
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
31
• 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
Main building blocks
32
• National identities
3##44#2#
• Cultural map
32144421
• State system
• Territorial obstacles
The model’s telescoped
phases
t=0
Phase I
Initialization
1000
Phase II
State formation
& Assimilation
2200
Phase III
Phase IV
Nation-building Civil war
identityformation
assimilation
1200
33
nationalist
collective
action
Sample run 3
34
• Geosim Insurgency Model