Introduction to
Computational Modeling of Social Systems
Principles of agent-based modeling
Prof. Lars-Erik Cederman
ETH - Center for Comparative and International Studies (CIS)
Seilergraben 49, Room G.2, [email protected]
Nils Weidmann, CIS Room E.3, [email protected]
http://www.icr.ethz.ch/teaching/compmodels
Lecture, November 2, 2004
Grading (revised)
Two „paths“ to get your grade:
Either
1. by completing a series of homework exercises given in the
lecture
Or
2. by submitting a term project due at the end of this semester
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Path 1: Exercises
• Four sets of questions and exercises will be given throughout
the course
• For due dates see the course schedule
• The more difficult exercises will be marked with a star (*)
• In order to receive the best grade, students are required to
hand in all exercises given, including the starred ones
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Path 2: Term project
• Create a model about a social topic
• You are required to submit a one-page proposal by January
11, 2005.
• Final project is due March 7, 2005
– Project report (no more than 20 pages)
– Runnable model based on RePast
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Today’s agenda
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Prehistory
Other types of models
Principles of agent based modeling
Categories of ABM models
The pros and cons of ABM
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Historical Lineages of ABM
Source:
Nigel
Gilbert
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Von Neumann’s theory of cellular automata
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• Cellular automata are discrete
dynamical systems that model
complex behavior based on simple,
local rules animating cells on a lattice
Invented by John von Neumann
Game of Life
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First practical CA invented by John
Conway in the late 1960s
Later popularized by Martin Gardner
John Conway
Simple rules:
•A dead cell with 3 live neighbors
comes to life
•A live cell with 2 or 3 neighbors
stays alive
•Otherwise the cell dies
http://www.math.com/students/wonders/life/life.html
Stephen
Wolfram
Expert on CAs
Four types of models
Analytical
focus:
Systemic
variables
Micromechanisms
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Modeling language:
Deductive Computational
1. Analytical
macro models
2. Macrosimulation
3. Rational
choice
4. Agent-based
modeling
1. Analytical macro models
• Equilibrium conditions or systemic
variables traced in time
• Closed-form, and often based on
differential equations
• Examples: macro economics and
traditional systems theory
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2. Macro simulation
• Dynamic systems, tracing macro
variables over time
• Based on simulation
• Systems theory and Global Modeling
Jay Forrester, MIT
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3. Rational choice modeling
• Individualist reaction to macro
approaches
• Decision theory and game theory
• Analytical equilibrium solutions
• Used in micro-economics and
spreading to other social sciences
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4. Agent-based modeling
• ABM is a computational methodology that allows the analyst 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
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Complex Adaptive Systems
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A CAS is a network exhibiting aggregate properties that emerge from
primarily local interaction among many, typically heterogeneous agents
mutually constituting their own environment.
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Emergent properties
Large numbers of diverse agents
Local and/or selective interaction
Adaptation through selection
Endogenous, non-parametric environment
Microeconomics  ABM
Analytical
Equilibrium
Nomothetic
Variable-based
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Synthetic approach
Non-equilibrium theory
Generative method
Configurative ontology
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Analytical 
Synthetic approach
• Hope to solve problems through strategy of “divide
and conquer”
• Need to make ceteris paribus assumption
• 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
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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
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Nomothetic 
Generative 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!”
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Variable-based

Configurative ontology
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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 endogenizing interactions and actors
Object-orientation is well suited to capture agents
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Emergent social forms
1.
2.
3.
4.
Interaction patterns
Property configurations
Dynamic networks
Actor structures
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1. Emergent interaction patterns
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actor
actor
actor
actor
actor
actor
• Models of “emergent order”
producing configurations
• Axelrod (1984, chap. 8): “The
structure of cooperation”
actor
actor
actor
2. Emergent property configurations
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actor
actor
actor
actor
actor
actor
actor
actor
actor
actor
actor
actor
actor
actor
actor
actor
actor
actor
Models of “emergent structure” constituted
as property configruations
Example: Schelling’s segregation model;
Carley 1991; Axelrod 1997
See Macy 2002 for further references
3. Emergent dynamic networks
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• Most computational models treat
networks as exogenous
• Recent exceptions:
frequency
d-a
degree d
– Albert and Barabási’s scale-free
networks
– Economics and evolutionary game
theory: e.g. Skyrms and Pemantle
4. Emergent actor structures
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Computational models normally assume the
actors to be given
Exceptions:
– Axelrod’s model of new political actors
– Axtell’s firm-size model
– Geopolitical models in the Bremer &
Mihalka tradition
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Emergence?
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