ECE421

Complexity
Fall 2012
Designation: 
Elective for ECE
Catalog Data: 

ECE 421 – Complexity (3 units)
Description:  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. 

Grading:  Regular grades are awarded for this course: A B C D E.

May be convened with:  ECE 521.

Usually offered:  Fall.

Prerequisite(s): 
ECE 408 is strongly recommended
Textbook(s): 

A long list of recommended reading.

Course Learning Outcomes: 

By the end of this course, the student will understand

  1. The key issues associated with Complexity
  2. The main approaches to study Complexity
  3. The ways of describing complex systems
  4. The process of formation of complex systems
  5. How local interactions give rise to global patterns of behavior
  6. Emergent phenomena
  7. Analytical and computational tools for studying Complexity
  8. The main application areas of Complexity
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: 

Two 75-minute lecture sessions per week.

Relationship to Student Outcomes: 

a) an ability to apply knowledge of mathematics, science, and engineering (Medium)
c) an ability to design a system, component, or process to meet desired needs within
    realistic constraints such as economic, environmental, social, political, ethical, health and
    safety, manufacturability, and sustainability (Medium)
e) an ability to identify, formulate, and solve engineering problems (High)
g) an ability to communicate effectively (Medium)
h) the broad education necessary to understand the impact of engineering solutions in a global,
    economic, environmental, and societal context (Medium)
k) an ability to use the techniques, skills, and modern engineering tools necessary for engineering practice.

Prepared by: 
Dr. Miklos Szilagyi
Prepared Date: 
2/1/10

University of Arizona College of Engineering