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Agent-Based Models of Geopolitical Processes Prof. Dr. Lars-Erik Cederman Swiss Federal Institute of Technology (ETH) Center for Comparative and International Studies (CIS) Seilergraben 49, Room G.2 lcederman@ethz.ch Einführungsvorlesung , June 10, 2004 A time of flux

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Agent based models of geopolitical processes l.jpg

Agent-Based Models of Geopolitical Processes

Prof. Dr. Lars-Erik Cederman

Swiss Federal Institute of Technology (ETH)

Center for Comparative and International Studies (CIS) Seilergraben 49, Room G.2

lcederman@ethz.ch

Einführungsvorlesung, June 10, 2004


A time of flux l.jpg

A time of flux


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Challenges of complexity

Time


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Challenges of complexity

Time

Space


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Challenges of complexity

Time

Space

Identity


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Sociological process theory

  • Georg Simmel

  • Vergesellschaftung

  • Large social organizations exist despite:

    • long duration

    • vast spatial extension

    • diversity of their members


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Complexity theory

Complex adaptive systems exhibit properties that emerge from local interactions among many heterogeneous agents mutually constituting their own environment

“Boids”

A model of the Internet

The Santa Fe Institute


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A view from the Berlin television tower


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Ethnic neighborhoods

Little Italy, New York City

Chinatown, New York City


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Neighborhood segregation

Micro-level rules of the game

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

< 1/3

Thomas C. Schelling

Micromotives and Macrobehavior

Move to random location otherwise


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Sample run 1

  • Schelling's Segregation Model


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Emergent results from Schelling’s segregation model

Number of

neighborhoods

Happiness

Time

Time


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Europe in 1500


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Europe in 1900


States made war and war made the state charles tilly l.jpg

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


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Geosim

  • Geosim uses Repast, a Java toolkit

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


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Sample Run 2

  • Geosim Base Model


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Emergent results from the run

Number of

states

Proportion of

secure areas

Time

Time


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Possible outcomes

15-state

multipolarity

(sample run)

7-state

multipolarity

bipolarity

unipolarity


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Applying Geosim to world politics


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Cumulative war-size plot, 1820-1997

Data Source:

Correlates

of War

Project (COW)


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Self-organized criticality

Power-law distributed

avalanches in a rice pile

Per Bak’s sand pile


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Simulated cumulative war-size plot

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


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Applying Geosim to world politics


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2. Modeling state sizes: Empirical data

log Pr (S > s)

(cumulative frequency)

log S ~ N(5.31, 0.79)

MAE = 0.028

log s

(state size)

1998

Data: Lake et al.


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Simulating state size with terrain


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Simulated state-size distribution

log Pr (S > s)

(cumulative

frequency)

log S ~ N(1.47, 0.53)

MAE = 0.050

log s

(state size)


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Applying Geosim to world politics


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Simulating global democratization

Source:

Cederman &

Gleditsch 2004


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A simulated democratic outcome

t = 0

t = 10,000


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Applying Geosim to world politics


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Sample run 3

  • Geosim Insurgency Model


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Future activities

  • The International Conflict Research Group:

    http://www.icr.ethz.ch

  • Search for Ph D students

  • Annual courses on “Computational Models of Social Systems”

  • TAICON = Trans-Atlantic Initiative on Complex Organizations and Networks (Harvard, ETH)

    • Inaugural lecture given by Duncan Watts, Columbia Univ., January 12, 2005

Claudia Jenny Luc Girardin

Duncan Watts


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