1. Week 3a Mechanisms for Adaptation

2. POLS-GEOG-SOC 495 Spring 2007 2 Lecture Overview Review –CAS –Principles of chaos How do systems “learn”? –“Credit assignment” –“Rule discovery” How do we create computer simulations?

3. POLS-GEOG-SOC 495 Spring 2007 3 Complex Adaptive Systems Massively parallel –lots of agents doing their own thing Exhibit interesting characteristics –“Evolution” or “dynamism”: change over time –“Emergence”: aggregate behavior –“Anticipation”: ability to adapt

4. POLS-GEOG-SOC 495 Spring 2007 4 ChaosChaos Simple deterministic rules These rules produce –Sensitivity to initial condition –Seemingly random behavior –Surprises, unpredictability Implication –We can’t use traditional methods –Computers can help us simulate these systems

5. POLS-GEOG-SOC 495 Spring 2007 5 Questions so far? Holland, p. 20 “... Standard theories in physics, economics, and elsewhere, are of little help because they concentrate on optimal end- points, whereas complex adaptive systems ‘never get there.’”

6. POLS-GEOG-SOC 495 Spring 2007 6 How do systems “adapt”? Systems have many rules Rules compete: some are better than others Better rules survive, causing the whole system to “learn”

7. POLS-GEOG-SOC 495 Spring 2007 7 A “system” A set of actors –“fireflies”, “people”, “cars” OR A set of rules

8. POLS-GEOG-SOC 495 Spring 2007 8 “Credit Assignment” Holland, p. 23: “The more a rule contributes to good performance, the stronger it becomes, and vice versa.” –Some rules “survive”

9. POLS-GEOG-SOC 495 Spring 2007 9 SelectionSelection Rules that perform well –Survive –Propagate Environment “selects” from among rules

10. POLS-GEOG-SOC 495 Spring 2007 10 SelectionSelection Examples –Biology “natural selection” Advantageous traits survive in a population Disadvantageous rules do not

11. POLS-GEOG-SOC 495 Spring 2007 11 SelectionSelection Social science example –Markets Investment strategies Business models

12. POLS-GEOG-SOC 495 Spring 2007 12 SelectionSelection Social science example –Network effect

13. POLS-GEOG-SOC 495 Spring 2007 13 SelectionSelection Social science example –Network effect

14. POLS-GEOG-SOC 495 Spring 2007 14 SelectionSelection Social science example –Positive returns

15. POLS-GEOG-SOC 495 Spring 2007 15 SelectionSelection Social science example –The drive home “Best” route is constantly changing –BAL elevators, January 2007

16. POLS-GEOG-SOC 495 Spring 2007 16 “Rule Discovery” Holland, p. 23: “If it is to evolve to deal with new situations, the system will have to create new rules.” –P. 24: “It is useful to think of ‘breeding’ strong rules.”

17. POLS-GEOG-SOC 495 Spring 2007 17 Rule Discovery Biology example –Genetic crossover –Mutation

18. POLS-GEOG-SOC 495 Spring 2007 18 Rule Discovery Biology example –Monarch Butterfly and Viceroy Butterfly

19. POLS-GEOG-SOC 495 Spring 2007 19 Rule Discovery Social science example –Business mimicry

20. POLS-GEOG-SOC 495 Spring 2007 20 Rule Discovery Social science example –The drive home Always willing to try a new route

21. POLS-GEOG-SOC 495 Spring 2007 21 Mechanisms of adaptation Parallelism –A failure of a given rule does not cause the system to fail Competition/selection –Best rules propagate, making the system “fitter” Recombination/rule discovery –By constantly exploring new rules, the system can adapt to changing circumstances

22. POLS-GEOG-SOC 495 Spring 2007 22 SoftwareSoftware Creates massively parallel system –Each “actor” a program (i.e. a set of rules) –No single governing equation or routine –Computer executes each program simultaneously –“Fitter” rules survive and propagate –New rules constantly explore

23. POLS-GEOG-SOC 495 Spring 2007 23 NetLogo Software

24. POLS-GEOG-SOC 495 Spring 2007 24 NetLogo Models Traffic Traffic Grid Flocking