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Introduction to Computational Modeling of Social Systems Prof. Lars-Erik Cederman ETH - Center for Introduction to Computational Modeling of Social Systems Prof. Lars-Erik Cederman ETH - Center for Comparative and International Studies (CIS) Seilergraben 49, Room G. 2, [email protected] ch Christa Deiwiks, CIS Room E. 3, [email protected] gess. ethz. ch http: //www. icr. ethz. ch/teaching/compmodels Week 1

Today’s agenda • Introduction – Course goals – Course contents • Course logistics – Today’s agenda • Introduction – Course goals – Course contents • Course logistics – Prerequisites and grading – Schedule – Web. CT teaching system • Examples of agent-based models – Simple models: Schelling, Traffic, AIDS, Sugarscape – Complex models: Anasazi, Geosim 2

Course goals • • • Become familiar with the paradigm Advance your programming skills Course goals • • • Become familiar with the paradigm Advance your programming skills in Java Master Re. Past libraries Construct a simple computational model Start to think about how to apply the method to your own research puzzle 3

Course contents • Short Java Primer • Introduction to the principles of agentbased modeling Course contents • Short Java Primer • Introduction to the principles of agentbased modeling • Introduction to Re. Past modeling • In SS 2007 there will be an advanced course extending this introductory lecture 4

Course logistics • Prerequisites: Programming experience (preferably in an object-oriented language) • Grading: – Course logistics • Prerequisites: Programming experience (preferably in an object-oriented language) • Grading: – Four sets of exercises – To be completed through the Web. CT online teaching system • Resources: – Course web page http: //www. icr. ethz. ch/teaching/compmodels/ – …where you‘ll find the link to Web. CT: https: //aai-portal. ethz. ch/aai_portal/user/aai/login. php? rid=286. 347 FEAED 5 A 5

The Web. CT online teaching system 6 The Web. CT online teaching system 6

Course schedule • • • • October 24: Introduction Examples of agent-based models in Course schedule • • • • October 24: Introduction Examples 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 Re. Past Tutorial I November 28: Re. Past Tutorial II December 5: Re. Past Tutorial III December 12: Re. Past Tutorial IV December 19: Emergent Network models January 9: Emergent Structure models January 16: Emergent Actor models, Geo. Contest January 23: Emergent Actor models II January 30: Emergent Actor models III, Geo. Contest Presentation 7

What is agent-based modeling? • ABM is a computational methodology that allows the analyst What 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 8

Disaggregated modeling If <cond> then <action 1> else <action 2> Inanimate agents Animate agents Disaggregated modeling If then else Inanimate agents Animate agents Observer Data Organizations of agents Artificial world 9

Java 10 • Conceived by Sun in the early 1990 s • Became the Java 10 • Conceived by Sun in the early 1990 s • Became the new standard for the web thanks to platform-independence ntax sy C syntax C++ object model

Modeling in Re. Past • “Recursive Porous Agent Simulation Toolkit” • Re. Past is Modeling in Re. Past • “Recursive Porous Agent Simulation Toolkit” • Re. Past 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 11

Re. Past framework 12 Controlling simulations Displaying behavior Charting Managing parameters Re. Past framework 12 Controlling simulations Displaying behavior Charting Managing parameters

General readings on agentbased modeling • Axelrod, Robert. 1997. The Complexity of Cooperation: Agent-Based General readings on agentbased 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. 13

Simple sample models 1. Schelling’s segregation model 2. Traffic simulation 3. AIDS Re. Past Simple sample models 1. Schelling’s segregation model 2. Traffic simulation 3. AIDS Re. Past Net. Logo 14

Example 1: Neighborhood segregation Micro-level rules of the game Stay if at least a Example 1: Neighborhood segregation Micro-level rules of the game Stay if at least a third of neighbors are “kin” < 1/3 Move to random location otherwise Thomas C. Schelling Micromotives and Macrobehavior 15

Example 2: Traffic simulation (Net. Logo) • Model of the movement of cars on Example 2: Traffic simulation (Net. Logo) • 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 16

Example 3: AIDS (Net. Logo) • Simulate the spread of the human immunodeficiency virus Example 3: AIDS (Net. Logo) • 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 17

Complex sample models 1. Anasazi village formation 2. Nationalist insurgencies in Geosim 18 Complex sample models 1. Anasazi village formation 2. Nationalist insurgencies in Geosim 18

Example 1: Anasazi Village Formation • Gumerman et al. 2002 SFI Working Paper 02 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 19

Example 2: Geosim • Geopolitical simulation system • Cederman (2004) “Articulating the Mechanisms of Example 2: Geosim • Geopolitical simulation system • Cederman (2004) “Articulating the Mechanisms of Nationalist Insurgencies” • Based on Re. Past 3##44#2# 32144421 • National identities • Cultural map • State system • Territorial obstacles 20

Where to find more models: Links • See “Resources” under class home page • 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. 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 21