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Prof. Lars-Erik CedermanETH - Center for Comparative and International Studies (CIS)

Seilergraben 49, Room G.2, lcederman@ethz.chChrista Deiwiks, CIS Room E.3, deiwiks@icr.gess.ethz.ch

http://www.icr.ethz.ch/teaching/compmodels

Introduction to Computational Modeling of Social Systems

Week 1

2Today’s agenda

• Introduction– Course goals– Course contents

• Course logistics– Prerequisites and grading– Schedule– WebCT teaching system

• Examples of agent-based models– Simple models: Schelling, Traffic, AIDS, Sugarscape– Complex models: Anasazi, Geosim

3Course goals

• Become familiar with the paradigm• Advance your programming skills in Java• Master RePast libraries• Construct a simple computational model• Start to think about how to apply the

method to your own research puzzle

4Course contents

• Short Java Primer• Introduction to the principles of agent-

based modeling• Introduction to RePast modeling

• In SS 2007 there will be an advanced course extending this introductory lecture

5Course logistics

• Prerequisites: Programming experience (preferably in an object-oriented language)

• Grading:– Four sets of exercises– To be completed through the WebCT online teaching

system• Resources:

– Course web pagehttp://www.icr.ethz.ch/teaching/compmodels/

– …where you‘ll find the link to WebCT: https://aai-portal.ethz.ch/aai_portal/user/aai/login.php?rid=286.347FEAED5A

6

The WebCT online teaching system

7Course schedule

• October 24: IntroductionExamples of agent-based models in the social sciences

• October 31: Java Primer / Gearing up• November 7: Principles of agent-based modeling• November 14: A hand-crafted agent-based model• November 21: The Iterated Prisoner’s Dilemma and RePast Tutorial I• November 28: RePast Tutorial II• December 5: RePast Tutorial III• December 12: RePast Tutorial IV• December 19: Emergent Network models• January 9: Emergent Structure models• January 16: Emergent Actor models, GeoContest• January 23: Emergent Actor models II• January 30: Emergent Actor models III, GeoContest Presentation

8What is 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

• Different from other types of computational techniques: econometrics, numerical solution, global modeling, AI modeling

9Disaggregated modeling

Organizations of agents

Animate agents

Data

Artificial world

Observer

Inanimate agents

If <cond> then

<action1>else

<action2>

If <cond> then

<action1>else

<action2>

11Modeling in RePast

• “Recursive Porous Agent Simulation Toolkit”• RePast is an open-source software framework for

creating agent-based simulations using the Java programming language

• Initially developed by the Social Science Research Computing at the University of Chicago since January 2000: http://repast.sourceforge.net

• Modeled on Swarm but easier to use and better documented

12RePast framework

Controlling simulations

Displaying behavior

Charting Managingparameters

13

General readings on agent-based modeling

• Axelrod, Robert. 1997. The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton: Princeton University Press.

• Casti, John L. 1997. Would-Be Worlds: How Simulation Is Changing the Frontiers of Science. New York: Wiley.

• Cederman, Lars-Erik. 1997. Emergent Actors in World Politics: How States and Nations Develop and Dissolve. Princeton: Princeton University Press.

• Epstein, Joshua M. and Robert Axtell. 1996. Growing Artificial Societies: Social Science From the Bottom Up. Cambridge, Mass.: MIT Press.

• Holland, John H. 1995. Hidden Order: How Adaptation Builds Complexity. Reading, Mass.: Addison-Wesley.

14Simple sample models

1. Schelling’s segregation model RePast2. Traffic simulation NetLogo3. AIDS NetLogo

15

Example 1: Neighborhood segregation

Thomas C. SchellingMicromotives and

Macrobehavior

< 1/3

Micro-level rules of the game

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

Move to random location otherwise

16

Example 2: Traffic simulation (NetLogo)

• Model of the movement of cars on a highway • Each car follows a simple set of rules:

– if there’s car close ahead, it slows down– if there’s no car ahead, it speeds up

• The project demonstrates how traffic jams form spontaneously without obstacles

17

Example 3: AIDS (NetLogo)

• Simulate the spread of the human immunodeficiency virus (HIV), via sexual transmission

• Control of the– population's tendency to practice

abstinence– amount of time an average "couple"

in the population will stay together– population's tendency to use

condoms– population's tendency to get tested

for HIV

18Complex sample models

1. Anasazi village formation2. Nationalist insurgencies in Geosim

19

Example 1: Anasazi Village Formation

• Gumerman et al. 2002 SFI Working Paper 02-16-067 (among others)

• Reconstruction of settlement patterns and demographics of pueblo Indians in the American Southwest

• The main puzzle pertains to the group’s sudden disappearance

• Based on the Sugarscape model, and thus also programmed in Ascape

20Example 2: Geosim

• Geopolitical simulation system• Cederman (2004) “Articulating

the Mechanisms of Nationalist Insurgencies”

• Based on RePast

32144421

3##44#2# • National identities

• Cultural map

• State system

• Territorial obstacles

21

Where to find more models: Links

• See “Resources” under class home page• Santa Fe Institute: http://www.santafe.edu/

• Center for the Study of Complex Systems at the University of Michigan: http://www.pscs.umich.edu/

• European web sites on Computer simulation of societies http://www.soc.surrey.ac.uk/research/simsoc/ and “European Social Simulation Association” http://essa.eu.org/

• For the US counterpart, see http://www.casos.cs.cmu.edu/naacsos/

• Leigh Tesfatsions’s site on computational economics:http://www.econ.iastate.edu/tesfatsi/ace.htm

• See also the Journal of Artificial Societies and Social Simulation: http://jasss.soc.surrey.ac.uk/JASSS.html

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