fres 1010: complex adaptive systems prof. eileen kraemer fall 2005 lecture 1
TRANSCRIPT
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Fres 1010:Complex Adaptive Systems
Prof. Eileen Kraemer
Fall 2005
Lecture 1
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Theme:
Simple agents following simple rules can generate amazingly complex structures.
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What are complex adaptive systems?
Systems composed of many interacting parts that evolve and adapt over time.
Organized behavior emerges from the simultaneous interactions of parts without any global plan.
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Properties of Complex Adaptive Systems
• Many interacting parts• Emergent phenomena• Adaptation• Specialization & modularity• Dynamic change• Competition and cooperation• Decentralization• Non-linearities
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Many interacting parts
Businesses made of people, colonies made of ants, brains made of neurons, networks composed of hosts and routers, etc.Systems are more than mere collections because of interactions among the elementsSize matters
A critical number of amoeba needed to create clusters in slime molds
Massive parallelismOften, all agents do same, simple thingComplexity comes from interactions
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Slime mold??
Slime mold does something interesting
Cool damp conditions: reddish-orange mass
It moves! (slowly, but it does)
Cooler, wetter: disappears!!
Dictyostelium discoideum
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Slime mold
Spends much of its life as distinct single-celled units, each moving separatelyRight conditions: cells coalesce into single, larger organism that crawls across forest floor, eating rotting wood and leavesOscillates between single creature and swarm modes …
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How is aggregation controlled?
Fact: slime molds emit acrasin (cyclic AMP)
Original (centralized control) theory:Swarms formed at the command of pacemaker cells that order the other cells to begin aggregating
Idea: pacemakers emit cyclic AMP, others follow suit, cells follow trails, cluster forms
Problem: no one could find the pacemakers
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How is aggregation controlled?
Distributed control:Slime mold cells follow trails of cyclic AMP
Slime mold cells generate trails of cyclic AMP
If slime cells start to pump out enough cyclic AMP, cells begin following trails started by other cells, clusters form, which leave more cyclic AMP, which causes more cells to join … & so on: a positive feedback loop develops
Classic study in bottom-up behavior
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Other example systems
Slime mold (Keller & Segel)
City neighborhoods (Jane Jacobs)
Human brain (Marvin Minsky)
Ants (E.O. Wilson)
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Common elements:
Solve problems by drawing on masses of simple elements, rather than use of a centralized intelligent controller
Agents residing on one scale produce behavior that resides on a scale above them
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Definitions of Emergence
Whole is more than sum of parts
Higher-level phenomena not easily predicted from lower-level behaviors
Higher-level descriptionsSpecial laws apply
High-level phenomena are not built in explicitlypredator-prey cycles
Fractal images
“gliders”
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Emergence
“Active essay” on emergence at MIT:
http://llk.media.mit.edu/projects/emergence/
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Adaptation
Improved performance over timeThree time courses of adaptation
Within a single event presented to an organismPerception of an organized formAdaptation of parts to each otherAdaptation of parts to external world
Within the lifetime of an organismLearning
Across lifetimesEvolution
Do interactions exist among these levels?
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Specialization and Modularity
Originally homogenous agents become differentiated as a result of interactions with each other
Shift from renaissance thinkers to specialized scientistsIncreased dependency of partsThe more dependencies between parts, the more organism-like is the whole
Self-organization - systems become more structured than they were originally
Advantages of modularitySpeedEfficiencybenefit of information encapsulation: module does not need to know about what is going on in the rest of the system
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Specialization and Cooperation: The Jack of all Trades
10 stages, 20 antsProb ant completes a stage = 0.4Prob ant finishes = 0.410 = 0.0001Prob ant fails = (1-0.0001) = 0.9999Prob all 20 ants fail = (0.9999)20 = 0.998Prob at least one completes = 1 – 0.998 = 0.002
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Specialization and Cooperation: Specialization
10 stages, 20 antsProb ant completes a stage = 0.4Prob ant fails a stage = 1 – 0.4 = 0.6Prob both ants fail = 0.62 = 0.36Prob of stage completion = 0.64Prob at least one completed task = (0.64)10 = 0.012
Moral: by specializing, probability of completion is 6 times greater.
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Dynamic Change
Complex adaptive systems viewed in terms of trajectories rather than fixed points
Complex systems often times never “settle down”
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Competition and Cooperation
Simple interactions: facilitation and antagonismExcitation and inhibition in neuronsDiffusion and reaction
Oscillating chemical reactions
Predator-prey dynamicsPositive and negative feedback cycles
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Decentralization
Self-organization without leaders Queen ants and head birds in a flock are not “in charge” Alternatives to centralized mind-sets (Resnick, 1994)
Peer-to-peer computing gridsThe World Wide WebGrass-roots movementsAdvantages of decentralization
AdaptabilitySystem can be “smarter” than smartest agent
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Nonlinearities
Output is not proportional to inputCan’t predict how system will work by understanding parts separately,and combining them additively
The tipping point (Gladwell)Cascades of consequences from small eventsIdeas are “sticky”Hushpuppies and a couple of East Village kids
1994: 30,000 sold1995: 430,0001996: 2,000,000
Phase transitions: ice to water to steamSymmetry breaking: Systems that start out (nearly) symmetric develop qualitatively large asymmetries
Milk dropsDevelopment of a fetus from blastula to embryo
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Symmetry Breaking in a droplet of milk
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Symmetry breaking in a fetal development
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Model aesthetics
High-level phenomenon is explained, not assumedMechanism-oriented accounts
Simplest system that produces phenomena is preferredComplex adaptive system models as caricatures
Want explanations, not clonesconcentrate on essence of a system
Do parameters of variation correspond to existing natural systems?
Can most naturally occurring systems be modeled with parametric variations? Do most parametric variations result in patterns that are found in nature?
Constraint is goodWant a system that could not have predicted anything
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Raup’s Shell Generator
Shells grow as tubesCapture variations in shells with as few parameters as possible (explaining patterns that occur, and only those patterns)
Flare: expansion rate of spiral2 = for every turn, spiral opens out to twice its previous size (spiral, not tube)
Verm: How much tube fills area of spiral.7 = distance from center of spiral to the inner margin of tube is 70% of the distance from center to outer margin.
Spire: rate at which tube creeps up 3-D cone0 = all windings are in one plane
Raup's CubeCan explain many types of shells that are found Cube is larger than set of existing shells, but this will always be the case.
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Raup’s Shell Generator
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Raup’s Shell Generator
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Raup’s Cube
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Dawkin’s Blind Snailmaker
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Dawkin’s Blind Snailmaker
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D’arcy Thompson’s constrained transformations
Explain regularities in animal and plant forms by constrained transformations
Transformations explained by growth processes
Four standard transformationsStretch the dimensions
Taper
Shear
Radial coordinates from a fixed focus
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D’arcy Thompson’s constrained transformations
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D’arcy Thompson’s constrained transformations
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D’arcy Thompson’s constrained transformations
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D’arcy Thompson’s constrained transformations
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Next week
More on slime mold
Ants too!