Dates: June 6-10, 2005
Location: MIT, Cambridge, MA

This course offers an introduction to the essential concepts of complex systems and related mathematical methods and simulation strategies with application to physical, biological and social systems.

Concepts to be discussed include: emergence, complexity, networks, self-organization, pattern formation, evolution, adaptation, fractals, chaos, cooperation, competition, attractors, interdependence, scaling, dynamic response, information and function. Methods to be discussed include: statistical methods, cellular automata, agent-based modeling, pattern recognition, system representation and informatics.

Demonstration of the application of complex systems methods will be made through studies of:

• Social systems: education system, health care system, military system
• Psychosocial systems: patterns of social behavior, mind, creativity, awareness
• Biological systems: evolution, physiology, immune system, brain, cellular systems, genetic networks
• Physical systems: meteorology

There will be supervised group projects as an integral part of the course.

WEEK TWO: CX202 Modeling, Networks, and Evolution of Complex Systems

This course provides an introduction to building models of complex systems (physical, biological, social and engineering), networks architectures and evolutionary processes.

It will cover the basic construction and analysis of models including identifying what is to be modeled, constructing a mathematical representation, analysis tools and implementing and simulating the model in a computer simulation. Particular attention will be paid to choosing the right level of detail for the model, testing its robustness, and discussing which questions a given model can or cannot answer.

The study of networks will introduce the use of network topologies and the characterization of networks describing complex systems, including such concepts as small worlds, degree distribution, diameter, clustering coefficient, modules, motifs. Different types of network topologies and network behaviors that model aspects of real complex systems will be described including: modular, sparse, random, scale-free, and modular & scale free, influence, transport, transformaion, and structure.

The study of evolution will include the basic principles of evolutionary dynamics, its application and mechanisms in biological systems as well as socio-cultural and artificial contexts. Key concepts in traditional Neo-darwinian and more recent developments will be covered including: gene-centered view, fitness landscapes, evolutionary stability, co-evolution, group and strain selection, spatial evolutionary systems, selfishness and altruism, diversity, memetic evolution, genetic algorithms and other artificial evolutionary systems, and the origin of life.

There will be supervised group projects as an integral part of the course.

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For cancellations received two weeks prior to the program date, full NECSI course credit will be provided, good for two years. Cancellations received within two weeks of program date will receive a 50% NECSI course credit, good for two years. Substitutions may be made at any time. Payment must be made in US Dollars.

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Please note: Student = undergraduate and graduate students; Academic = postdocs, faculty and university researchers; Individual = non-academics

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