Week 3a Mechanisms for Adaptation

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

POLS-GEOG-SOC 495 Spring 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

POLS-GEOG-SOC 495 Spring 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

POLS-GEOG-SOC 495 Spring 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.’”

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

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

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

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

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

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

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

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

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

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

POLS-GEOG-SOC 495 Spring “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.”

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

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

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

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

POLS-GEOG-SOC 495 Spring 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

POLS-GEOG-SOC 495 Spring 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

POLS-GEOG-SOC 495 Spring NetLogo Software

POLS-GEOG-SOC 495 Spring NetLogo Models Traffic Traffic Grid Flocking