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