1. 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 Advanced Computational Modeling of Social Systems

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

3. 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. 4 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

5. 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. 6 2. Macro simulation Dynamic systems, tracing macro variables over time Based on simulation Systems theory and Global Modeling Jay Forrester, MIT

7. 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. 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. 9 Disaggregated modeling Organizations of agents Animate agents Data Artificial world Observer Inanimate agents If then else If then else

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

11. 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. 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. 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. 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. 15 A third way of doing science 1.Deduction –Derive theorems from assumptions 2.Induction –Find patterns in empirical data 3.Simulation –Start with explicit assumptions (deduction) –Generate data suitable for analysis (induction)

16. 16 Empirical understanding Why have particular large-scale regularities evolved and persisted, even when there is little top-down control? Examples: standing ovations, trade networks, socially accepted monies, mutual cooperation based on reciprocity, and social norms ABM: seek causal explanations grounded in the repeated interactions of agents operating in specified environments

17. 17 Normative understanding How can agent-based models be used as laboratories for the discovery of good designs? Examples: design of auction systems, voting rules, and law enforcement ABM: evaluate whether designs proposed for social policies, institutions, or processes will result in socially desirable system performance over time

18. 18 Heuristic How can greater insight be attained about the fundamental causal mechanisms in social systems? Examples: city segregation (or “tipping”) model developed by Thomas Schelling The large-scale effects of interacting agents are often surprising because it can be hard to anticipate the full consequences of even simple forms of interaction

19. 19 Methodological advancement How to provide ABM researchers with the methods and tools they need of social systems through controlled computational experiments? Examples: methodological principles, programming tools, visualization techniques

20. 20 A methodological approach ABM is a methodological approach that could ultimately permit two important developments: –The rigorous testing, refinement, and extension of existing theories that have proved to be difficult to formulate and evaluate using standard statistical and mathematical tools –A deeper understanding of fundamental causal mechanisms in multi-agent systems whose study is currently separated by artificial disciplinary boundaries

21. 21 Logistics Performance evaluation –Class participation –Class presentation –Term paper Readings –On our server Class home page: http://www.icr.ethz.ch/teaching/compmodels http://www.icr.ethz.ch/teaching/compmodels

22. 22 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

23. 23 Complexity theory A model of the Internet The Santa Fe Institute “Boids” Complex adaptive systems exhibit properties that emerge from local interactions among many heterogeneous agents mutually constituting their own environment

24. 24 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