
Course Catalog 20142015
SGN53606 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 








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 realworld 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 realworld 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, Nonlinear dynamics, Chaos theory, Cellular automata, Power laws, Selforganized 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 (15) will be used on the course
Additional information about prerequisites
1) Advisable basic knowledge of calculus.
2) Advisable knowledge of Differential equations.
3) SGN52406 Models of Gene Networks
Prerequisite relations (Requires logging in to POP)
Correspondence of content
Course  Corresponds course  Description 


More precise information per implementation
Implementation  Description  Methods of instruction  Implementation 
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. 