Principles of agent-based modeling Prof. Lars-Erik Cederman ETH - - - PowerPoint PPT Presentation

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Principles of agent-based modeling Prof. Lars-Erik Cederman ETH - - - PowerPoint PPT Presentation

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, lcederman@ethz.ch Nils Weidmann, CIS


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  • Prof. Lars-Erik Cederman

ETH - Center for Comparative and International Studies (CIS) Seilergraben 49, Room G.2, lcederman@ethz.ch Nils Weidmann, CIS Room E.3, weidmann@icr.gess.ethz.ch http://www.icr.ethz.ch/teaching/compmodels Lecture, November 2, 2004

Introduction to Computational Modeling of Social Systems

Principles of agent-based modeling

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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

  • 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

  • Cellular automata are

discrete dynamical systems that model complex behavior based

  • n simple, local rules

animating cells on a lattice

Invented by John von Neumann

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Game of Life

  • First practical CA invented

by John Conway in the late 1960s

  • Later popularized by

Martin Gardner

  • A dead cell with 3 live

neighbors comes to life

  • A live cell with 2 or 3

neighbors stays alive

  • Otherwise the cell dies

John Conway

Simple rules:

Stephen Wolfram Expert on CAs

http://www.math.com/students/wonders/life/life.html

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Four types of models

Modeling language: Deductive Computational Analytical focus: Systemic variables Micro- mechanisms

  • 4. Agent-based

modeling

  • 3. Rational

choice

  • 1. Analytical

macro models

  • 2. Macro-

simulation

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  • 1. Analytical macro models
  • Equilibrium conditions
  • r 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

A CAS is a network exhibiting aggregate properties that emerge from primarily local interaction among many, typically heterogeneous agents mutually constituting their own environment.

Emergent properties Large numbers of diverse agents Local and/or selective interaction Adaptation through selection Endogenous, non-parametric environment

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Microeconomics ABM

Analytical Synthetic approach Equilibrium Non-equilibrium theory Nomothetic Generative method Variable-based 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

  • 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. Interaction patterns 2. Property configurations 3. Dynamic networks

  • 4. Actor structures
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  • 1. Emergent interaction patterns
  • Models of “emergent
  • rder” producing

configurations

  • Axelrod (1984, chap. 8):

“The structure of cooperation”

actor actor actor actor actor actor actor actor actor

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  • 2. Emergent property

configurations

  • Models of “emergent structure”

constituted as property configruations

  • Example: Schelling’s

segregation model; Carley 1991; Axelrod 1997

  • See Macy 2002 for further

references

actor actor actor actor actor actor actor actor actor actor actor actor actor actor actor actor actor actor

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  • 3. Emergent dynamic networks
  • Most computational

models treat networks as exogenous

  • Recent exceptions:

– Albert and Barabási’s scale- free networks – Economics and evolutionary game theory: e.g. Skyrms and Pemantle

frequency degree d d-α

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  • 4. Emergent actor structures
  • 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

  • Emergence?