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Advanced Computational Modeling of Social Systems Lars-Erik - - PowerPoint PPT Presentation

May 21, 2023 127 likes •302 views

Advanced Computational Modeling of Social Systems Lars-Erik Cederman and Luc Girardin Center for Comparative and International Studies (CIS) Swiss Federal Institute of Technology Zurich (ETH) http://www.icr.ethz.ch/teaching/compmodels

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Lars-Erik Cederman and Luc Girardin Center for Comparative and International Studies (CIS) Swiss Federal Institute of Technology Zurich (ETH) http://www.icr.ethz.ch/teaching/compmodels

Advanced Computational Modeling

  • f Social Systems
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Today‘s agenda

  • Complexity
  • Historical background
  • Power laws
  • Networks
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Cybernetics

  • Norbert Wiener

(1894-1964)

  • Science of

communication and control

  • Circularity
  • Process and change
  • Further development

into general systems theory

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General systems theory

  • Ludwig von Bertalanffy

(1901-1972)

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Catastrophe theory

  • René Thom (1923-2002)
  • Catastrophes as

discontinuities in morphogenetic landscapes

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Chaos theory

  • E. N. Lorenz
  • Chaotic dynamics generated

by deterministic processes

Butterfly effect Strange attractor

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Non-equilibrium physics

  • Dissipative structures are
  • rganized arrangement in non-

equilibrium systems that are dissipating energy and thereby generate entropy

Convection patterns

Ilya Priogogine

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  • 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

Self-organized criticality

Input Output

Complex System

log f log s f s

s-α

Per Bak

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Self-organized criticality

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

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Strogatz: Exploring complex networks (Nature 2001)

  • Problems to overcome:

1. structural complexity 2. network evolution 3. connection diversity

  • 4. dynamic complexity

5. node diversity

  • 6. meta-complication

Steven H. Strogatz

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Between order and randomness

Watts and Strogatz’s Beta Model Short path length & high clustering

Duncan Watts

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The small-world experiment

Stanley Milgram

Sharon, MA Omaha, NE

“Six degrees of separation”

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Two degree distributions

p(k) p(k) k Normal distribution k Power law

log p(k) log k log p(k) log k

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Scale-free networks

  • Barabási and Albert’s 1999

model of the Internet:

  • Constantly growing network
  • Preferential attachments:

– p(k) = k / Σi ki

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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)

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Tooling

  • RePast

http://repast.sourceforge.net/

  • JUNG

http://jung.sourceforge.net/

  • R SNA package

http://erzuli.ss.uci.edu/R.stuff/

  • Pajek

http://vlado.fmf.uni-lj.si/pub/networks/pajek/