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ONE-WEEK INTENSIVE COURSE PROGRAM

Syllabus (approximate):

Note: Each topic below reflects a 1/2 hour to 1 hour module.

DAY 1:

  1. Introduction: Examples, Questions and Methods. Emergence, interdependence, and networks.
  2. Interactions and Pattern Formation: Spatial patterns. Examples: Developmental biology, Collective behavior in social systems.
  3. Patterns in Brain and Mind: Neural networks, associative and feed forward networks, substructure in networks, attributes and creativity
  4. Application of patterns: Social networks and marketing
  5. Patterns and meaning: The relationship of external and internal patterns. Example: Art
  6. Methodology of spatial patterns: Constructing models of collective behaviors, frustration and complex landscapes, dynamics and optimization on complex landscapes, categories of network models.
  7. Application of complex landscapes: Development in the third world.

DAY 2:

  1. Describing complex systems: Space of possibilities and complexity, multiscale representations, the complexity profile.
  2. Application of multiscale analysis I: History of human civilization.
  3. Application of multiscale analysis II: Social systems: Medical system, education system
  4. Application of multiscale analysis III: Global terrorism, complex warfare, home security.
  5. Application of multiscale analysis IV: The history of Art.
  6. Connections: Complexity and emergence, interdependence, and patterns.
  7. Methodology of multiscale analysis: Constructing fine and large scale models, scaling, scale invariant models, blocking, clustering, dimensional reduction, relevant variables.

DAY 3:

  1. 1. Dynamic patterns and chaos: Characteristics of dynamic patterns, chaotic systems, modeling dynamical systems. Examples: feedback, evolutionary competition, predator -prey systems.
  2. 2. Randomness and noise: Ensembles and averaging, random walks, Markov chains, fractal time series, information theory.
  3. 3. Slow dynamics in small and large scale systems. Separation of time scales: Treating fast, slow and dynamic degrees of freedom.
  4. 4. Randomness, determinism, causality, prediction, intention, anticipation.
  5. 5. Methods: Modeling dynamical systems: Iterative maps, differential equations.

DAY 4:

  1. Evolution: Darwinian evolution, neoDarwinian theory and the breakdown of the gene centered view.
  2. Spatial models of evolution: Biodiversity, ecology of natural habitats and preserves.
  3. Competition and cooperation in evolution: Altruism and selfishness, teams and individuals. Example: Sports.
  4. Application of evolution: Engineering and management.
  5. Connections: Evolution and emergence, interdependence, patterns, complexity.
  6. Methodology of evolution: Genetic algorithms, game theory, spatial evolution, multiscale approaches.

DAY 5:

  1. Project Reports
  2. Summary and Review
  3. Test
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