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Week 3aWeek 3aWeek 3aWeek 3a
Mechanisms for Adaptation
POLS-GEOG-SOC 495 Spring 2007 2
Lecture OverviewLecture OverviewLecture OverviewLecture 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 2007 3
Complex Adaptive SystemsComplex Adaptive SystemsComplex Adaptive SystemsComplex 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 2007 4
ChaosChaosChaosChaos
• 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 2007 5
Questions so far?Questions so far?Questions so far?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 2007 6
How do systems “adapt”?How do systems “adapt”?How do systems “adapt”?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 2007 7
A “system”A “system”A “system”A “system”
• A set of actors– “fireflies”, “people”, “cars”
OR
• A set of rules
POLS-GEOG-SOC 495 Spring 2007 8
““Credit Assignment”Credit Assignment”““Credit Assignment”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 2007 9
SelectionSelectionSelectionSelection
• Rules that perform well– Survive– Propagate
• Environment “selects” from among rules
POLS-GEOG-SOC 495 Spring 2007 10
SelectionSelectionSelectionSelection
• Examples– Biology
• “natural selection”• Advantageous traits survive in a population• Disadvantageous rules do not
POLS-GEOG-SOC 495 Spring 2007 11
SelectionSelectionSelectionSelection
• Social science example– Markets
• Investment strategies• Business models
POLS-GEOG-SOC 495 Spring 2007 12
SelectionSelectionSelectionSelection
• Social science example– Network effect
POLS-GEOG-SOC 495 Spring 2007 13
SelectionSelectionSelectionSelection
• Social science example– Network effect
POLS-GEOG-SOC 495 Spring 2007 14
SelectionSelectionSelectionSelection
• Social science example– Positive returns
POLS-GEOG-SOC 495 Spring 2007 15
SelectionSelectionSelectionSelection
• Social science example– The drive home
• “Best” route is constantly changing
– BAL elevators, January 2007
POLS-GEOG-SOC 495 Spring 2007 16
““Rule Discovery”Rule Discovery”““Rule Discovery”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 2007 17
Rule DiscoveryRule DiscoveryRule DiscoveryRule Discovery
• Biology example– Genetic crossover– Mutation
POLS-GEOG-SOC 495 Spring 2007 18
Rule DiscoveryRule DiscoveryRule DiscoveryRule Discovery
• Biology example– Monarch Butterfly and Viceroy Butterfly
POLS-GEOG-SOC 495 Spring 2007 19
Rule DiscoveryRule DiscoveryRule DiscoveryRule Discovery
• Social science example– Business mimicry
POLS-GEOG-SOC 495 Spring 2007 20
Rule DiscoveryRule DiscoveryRule DiscoveryRule Discovery
• Social science example– The drive home
• Always willing to try a new route
POLS-GEOG-SOC 495 Spring 2007 21
Mechanisms of adaptationMechanisms of adaptationMechanisms of adaptationMechanisms 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 2007 22
SoftwareSoftwareSoftwareSoftware
• 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 2007 23
NetLogo SoftwareNetLogo SoftwareNetLogo SoftwareNetLogo Software
POLS-GEOG-SOC 495 Spring 2007 24
NetLogo ModelsNetLogo ModelsNetLogo ModelsNetLogo Models
• Traffic• Traffic Grid• Flocking