|ONE-WEEK INTENSIVE COURSE PROGRAM
Note: Each topic below reflects a 1/2 hour to 1 hour module.
- Introduction: Examples, Questions and Methods. Emergence, interdependence, and networks.
- Interactions and Pattern Formation: Spatial patterns. Examples: Developmental biology, Collective behavior in social systems.
- Patterns in Brain and Mind: Neural networks, associative and feed forward networks, substructure in networks, attributes and creativity
- Application of patterns: Social networks and marketing
- Patterns and meaning: The relationship of external and internal patterns. Example: Art
- Methodology of spatial patterns: Constructing models of collective behaviors, frustration and complex landscapes, dynamics and optimization on complex landscapes, categories of network models.
- Application of complex landscapes: Development in the third world.
- Describing complex systems: Space of possibilities and complexity, multiscale representations, the complexity profile.
- Application of multiscale analysis I: History of human civilization.
- Application of multiscale analysis II: Social systems: Medical system, education system
- Application of multiscale analysis III: Global terrorism, complex warfare, home security.
- Application of multiscale analysis IV: The history of Art.
- Connections: Complexity and emergence, interdependence, and patterns.
- Methodology of multiscale analysis: Constructing fine and large scale models, scaling, scale invariant models, blocking, clustering, dimensional reduction, relevant variables.
- 1. Dynamic patterns and chaos: Characteristics of dynamic patterns, chaotic systems, modeling dynamical systems. Examples: feedback, evolutionary competition, predator -prey systems.
- 2. Randomness and noise: Ensembles and averaging, random walks, Markov chains, fractal time series, information theory.
- 3. Slow dynamics in small and large scale systems. Separation of time scales: Treating fast, slow and dynamic degrees of freedom.
- 4. Randomness, determinism, causality, prediction, intention, anticipation.
- 5. Methods: Modeling dynamical systems: Iterative maps, differential equations.
- Evolution: Darwinian evolution, neoDarwinian theory and the breakdown of the gene centered view.
- Spatial models of evolution: Biodiversity, ecology of natural habitats and preserves.
- Competition and cooperation in evolution: Altruism and selfishness, teams and individuals. Example: Sports.
- Application of evolution: Engineering and management.
- Connections: Evolution and emergence, interdependence, patterns, complexity.
- Methodology of evolution: Genetic algorithms, game theory, spatial evolution, multiscale approaches.
- Project Reports
- Summary and Review