1. Prof. Lars-Erik Cederman ETH - Center for Comparative and International Studies (CIS) Seilergraben 49, Room G.2, lcederman@ethz.chlcederman@ethz.ch Nils Weidmann, CIS Room E.3, weidmann@icr.gess.ethz.chweidmann@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

2. 2 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

3. 3 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

4. 4 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

5. 5 Today’s agenda Prehistory Other types of models Principles of agent based modeling Categories of ABM models The pros and cons of ABM

6. 6 Historical Lineages of ABM Source: Nigel Gilbert

7. 7 Von Neumann’s theory of cellular automata 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

8. 8 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 Stephen Wolfram Expert on CAs John Conway Simple rules: http://www.math.com/students/wonders/life/life.html

9. 9 Four types of models Analytical focus: Systemic variables Micro- mechanisms Modeling language: Deductive Computational 4. Agent- based modeling 3. Rational choice 1. Analytical macro models 2. Macro- simulation

10. 10 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

11. 11 2. Macro simulation Dynamic systems, tracing macro variables over time Based on simulation Systems theory and Global Modeling Jay Forrester, MIT

12. 12 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

13. 13 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

14. 14 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

15. 15 Microeconomics  ABM Analytical  Synthetic approach Equilibrium  Non-equilibrium theory Nomothetic  Generative method Variable-based  Configurative ontology

16. 16 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

17. 17 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

18. 18 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!”

19. 19 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

20. 20 Emergent social forms 1.Interaction patterns 2.Property configurations 3.Dynamic networks 4.Actor structures

21. 21 1. Emergent interaction patterns actor Models of “emergent order” producing configurations Axelrod (1984, chap. 8): “The structure of cooperation”

22. 22 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

23. 23 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-d-

24. 24 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?