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Week 3a Mechanisms for Adaptation
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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?
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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
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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
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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.’”
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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”
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POLS-GEOG-SOC 495 Spring 2007 7 A “system” A set of actors –“fireflies”, “people”, “cars” OR A set of rules
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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”
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POLS-GEOG-SOC 495 Spring 2007 9 SelectionSelection Rules that perform well –Survive –Propagate Environment “selects” from among rules
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POLS-GEOG-SOC 495 Spring 2007 10 SelectionSelection Examples –Biology “natural selection” Advantageous traits survive in a population Disadvantageous rules do not
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POLS-GEOG-SOC 495 Spring 2007 11 SelectionSelection Social science example –Markets Investment strategies Business models
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POLS-GEOG-SOC 495 Spring 2007 12 SelectionSelection Social science example –Network effect
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POLS-GEOG-SOC 495 Spring 2007 13 SelectionSelection Social science example –Network effect
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POLS-GEOG-SOC 495 Spring 2007 14 SelectionSelection Social science example –Positive returns
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POLS-GEOG-SOC 495 Spring 2007 15 SelectionSelection Social science example –The drive home “Best” route is constantly changing –BAL elevators, January 2007
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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.”
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POLS-GEOG-SOC 495 Spring 2007 17 Rule Discovery Biology example –Genetic crossover –Mutation
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POLS-GEOG-SOC 495 Spring 2007 18 Rule Discovery Biology example –Monarch Butterfly and Viceroy Butterfly
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POLS-GEOG-SOC 495 Spring 2007 19 Rule Discovery Social science example –Business mimicry
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POLS-GEOG-SOC 495 Spring 2007 20 Rule Discovery Social science example –The drive home Always willing to try a new route
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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
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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
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POLS-GEOG-SOC 495 Spring 2007 23 NetLogo Software
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POLS-GEOG-SOC 495 Spring 2007 24 NetLogo Models Traffic Traffic Grid Flocking
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