ECE521

Complexity
Fall 2012
Designation: 
Elective for ECE
Catalog Data: 

Graduate Course Information

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ECE 521 - Complexity 

Credits: 3.00

UA Catalog Description:  http://catalog.arizona.edu/allcats.html 

Course Assessment:

Homework:  8 – 10 assignments

Project:  1 project

Exams:  1 Final Exam

Grading Policy:

Typically:  40% Final Exam,

60% Project. 

Course Summary:

Complexity is a new approach studying how interconnected parts give rise to the collective behavior of large systems and how the systems interact with their environment. It cuts across all traditional disciplines: science, engineering, medicine, management, etc. This course introduces the students to  

  • ·         the key issues associated with Complexity,
  • ·         the main approaches to study Complexity, 
  • ·         the ways of describing complex systems,
  • ·         the process of formation of complex systems,
  • ·         how local interactions give rise to global patterns of behavior,
  • ·         emergent phenomena,
  • ·         analytical and computational tools for studying Complexity, 
  • ·         the main application areas of Complexity.         

Prerequisite(s): 
Graduate Standing
Textbook(s): 

M. Resnick, Turtles, Termites, and Traffic Jams. MIT Press, 1994.

G. W. Flake, The Computational Beauty of Nature. MIT Press, Cambridge, MA, 1998.

L. Fisher, The Perfect Swarm: The Science of Complexity in Everyday Life. Basic Books, 2009.

Course Topics: 

What is complexity?

Complexity in physics, chemistry, mathematics, biology, engineering, society, weather, traffic, family, organizations, corporations, markets, brain, person, consciousness, life, etc, etc.

The common principles and general characteristics of complex systems.

            Agents.

            Feedback.

            Learning.

            Adaptation.

Self-organization.

Emergence.

Phase transitions.

Approaches to complexity:

General System Theory.

Cybernetics.

Chaos.

Fractals. Mandelbrot and Julia sets.

Cellular Automata.

Swarms.

Information.

Artificial Intelligence.

Search procedures. Dynamic programming.

Decision heuristics.

Artificial Life.

Artificial Societies.
N-person games.

Network Theory.

Power laws.

Neural Networks.

Evolution.

Self-organized criticality,

Simulated annealing.

Models.

Agent-Based Simulation.

Criticism of Complexity Science.

Case studies:

            Complexity Economics.

            Social norms, etc, etc.

Class/Laboratory Schedule: 

Lecture:  150 minutes/week

Prepared by: 
Miklos N. Szilagyi
Prepared Date: 
April 2013

University of Arizona College of Engineering