Course Catalog 2014-2015
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Course Catalog 2014-2015

SGN-53606 Computational Models in Complex Systems, 5 cr

Additional information

Suitable for postgraduate studies

Person responsible

Andre Sanches Ribeiro

Lessons

Study type P1 P2 P3 P4 Summer Implementations Lecture times and places
Lectures
Excercises


 


 


 
 4 h/week
 2 h/week


 
SGN-53606 2014-01 Monday 14 - 16 , TC219
Wednesday 12 - 14 , TC219

Requirements

Written examination and computer exercises (min. 50%)

Learning Outcomes

Students will be introduced to a wide range of examples, models and concepts in complex systems. Students will become familiar with the mathematical tools and methods that are used to model complex systems. Also, the student will practice implementing models with Matlab. After the course, the student will be able to: 1) Organize complex systems in classes, identify their dynamical properties, and write appropriate models of these systems that reproduce their behavior. 2) Classify and explain the behavior of complex systems from an Information Theoretical point of view. 3) Implement models of complex systems, apply them to real-world problems, and calculate optimal solutions. 4) Evaluate the strengths and weaknesses of a model in a given context. Analyze the results of simulations of the models. 5) Compare and appraise different computational models, and interpret conclusions using different models when confronted to real-world problems. 6) Create and develop models of competing agents, epidemics, and global resource management.

Content

Content Core content Complementary knowledge Specialist knowledge
1. Mathematical methods in Complex systems: Algorithmic complexity, Fractals, Non-linear dynamics, Chaos theory, Cellular automata, Power laws, Self-organized criticality, Complex networks, Evolution, Genetic algorithms, Pattern formation, Synchronization phenomena, Game theory, Autonomous agents, Artificial life.     
2. Programming models of complex systems: Matlab, Netlogo.     
3. Systemic view on solving complex problems.     

Instructions for students on how to achieve the learning outcomes

Examination. Students may earn extra points for the exam with computer exercises.

Assessment scale:

Numerical evaluation scale (1-5) will be used on the course

Additional information about prerequisites
1) Advisable basic knowledge of calculus. 2) Advisable knowledge of Differential equations. 3) SGN-52406 Models of Gene Networks

Prerequisite relations (Requires logging in to POP)



Correspondence of content

Course Corresponds course  Description 
SGN-53606 Computational Models in Complex Systems, 5 cr SGN-6457 Computational Models in Complex Systems, 5 cr  

More precise information per implementation

Implementation Description Methods of instruction Implementation
SGN-53606 2014-01 This course teaches models and concepts in complex systems. Students will become familiar with the mathematical tools and methods that are used to model complex systems. Also, the student will practice implementing models with Matlab.        

Last modified11.02.2015