MAT 259 Visualizing Information

1. MAT 259 Visualizing Information Self-Organization • Lecture 4, January 31, 2006 George Legrady

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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