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MAT 259 Visualizing Information. Self-Organization Lecture 4, January 31, 2006. Self- Organization . Various mechanisms by which pattern, structure and order emerge spontaneously in complex systems

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mat 259 visualizing information
MAT 259 Visualizing Information

Self-Organization

  • Lecture 4, January 31, 2006

George Legrady

self organization
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

George Legrady

examples of self organization
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

George Legrady

systems
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.)

George Legrady

cellular automata
Cellular Automata
  • Invented by Stanislaw Ulam and John von Neumann in the 1940’s 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

George Legrady

swarm intelligence
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

George Legrady

swarm intelligence systems
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

George Legrady

so organization methods
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

George Legrady

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

George Legrady

relevance to data visualization
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)

George Legrady

references
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

George Legrady