Lars-Erik Cederman and Luc Girardin

1. Advanced Computational Modelingof 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

2. Today‘s agenda • Course goals • Introduction to ABM • Course logistics

3. Course goals • Study the principles of agent-based modeling • Survey applications to the social sciences • Develop your own computational model of a social system • Prerequisite: Programming skills

4. Four types of models Modeling language: Deductive Computational Analytical focus: Systemic variables Micro- mechanisms 1. Analytical macro models 2. Macro- simulation 3. Rational choice 4. Agent-based modeling

5. 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

6. 2. Macro simulation • Dynamic systems, tracing macro variables over time • Based on simulation • Systems theory and Global Modeling Jay Forrester, MIT

7. 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

8. 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

9. Disaggregated modeling If <cond> then <action1> else <action2> Inanimate agents Observer Animate agents Data Organizations of agents Artificial world

10. Microeconomics  ABM Analytical  Synthetic approach Equilibrium  Non-equilibrium theory Nomothetic  Generative method Variable-based  Configurative ontology

11. 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

12. 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

13. 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!”

14. 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

15. Logistics • Performance evaluation • Class participation • Class presentation • Term paper • Readings • On our server • Class home page: http://www.icr.ethz.ch/teaching/compmodels

16. Course schedule • 29.03.2005: Introduction and logistics • Concepts • 05.04.2005: Complexity theory • 12.04.2005: Artificial life and intelligence • 19.04.2005: Network models • Applications • 26.04.2005: Traffic Project memo due! • 03.05.2005: Economy • 10.05.2005: Sociology • 17.05.2005: Conflict • Empirical methods • 24.05.2005: Validation • 31.05.2005: GIS • Student presentations • 07.06; 14.06; 21.06; 28.06.2005 • Final paper due July 5, 2005