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