MAT 259 Visualizing Information

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MAT 259 Visualizing Information

• Self-Organization
• Lecture 4, January 31, 2006

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Self- Organization

• Various mechanisms by which pattern, structure
  and order emerge spontaneously in complex systems
• Originally from physics (thermodynamics),
  chemistry (molecular self-assembly particles
  organize)
• Insect world complex collective behavior

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Examples of Self-Organization

• Pattern of sand ripples in a dune, zebra stripes.
  the coordinated movements of flocks of birds or
  schools of fish
• The intricate nests of termites, wasps, ants
• Flocking behavior of fish, birds
• The spatial pattern of stars in a spiral galaxy

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Systems

• Open systems the flow of matter and energy
  through the system allows the system to
  self-organize, and to exchange entropy with the
  environment
• Autopoiesis (self-created, non-equilibrium
  structures) organized states that remain stable
  despite matter and energy continuously flowing
  through them
• Morphogenesis how living organisms develop
  (tissues, organs, etc.)

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Cellular Automata

• Invented by Stanislaw Ulam and John von Neumann
  in the 1940s to investigate self-replication in
  machines
• Within a cellular grid, each cell responds to
  neighbors based on a set of rules
• Mathematician Wolfram used it as the basis of his
  book New Kind of Science Simple programs that
  lead to complex results
• Rule based behavior can easily be presented in a
  visual way

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Swarm Intelligence

• The emergent collective intelligence of groups
  of simple agents (Bonabeau)
• Behavior of bees, ants, reflect problem-solving
  approach
• Social insect colony a decentralized
  problem-solving system

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Swarm Intelligence Systems

• Starting point for new metaphors in engineering
  and computer science (robotics)
• Help design artificial distributed
  problem-solving methods and devices
• Potential models for organizing data / information

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SO Organization Methods

• Bottom up tinkering approach rather then top down
• The behavior of the group is often unpredictable,
  emerging from the collective interactions of all
  of the individuals.
• Relies on amplification of fluctuations (random
  walks, errors) which function as seeds from
  structures to develop
• Simple rules by which individuals interact can
  generate complexity
• Structures emerge despite randomness (foraging,
  nest building, etc.) System converges to stable
  state

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Stigmergy

• A term to explain task coordination and
  regulation
• SO rely on multiple interactions (mutually
  tolerant individuals respond to each others
  actions)
• Individuals interact indirectly when one modifies
  the environment, and the other responds to the
  new environment

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Relevance to Data Visualization

• Provides models of organization
• Transfer knowledge from study of nature
• Methods of organization (local to global)
• Relevant for non-linear systems (where multiple
  players/data sources affect situation)

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References

• Self-Organization in Biological Systems,
  Camazine. Deneubourg, Franks, Sneyd, Theraulaz,
  Benabeau
• Hidden Order, John Holland
• Swarm Intelligence, Bonabeau, Dorigo, Theraulaz
• Swarm Intelligence, Kennedy, Eberhart
• New Kinds of Science, Wolfram