Course Catalog 2009-2010 International Basic Pori International Postgraduate Open University |Degrees|     |Study blocks|     |Courses|

Person responsible

Jarl-Thure Eriksson

Implementations

	Lecture times and places	Target group recommended to
Implementation 1	Per 2, 3 : Wednesday 13 - 15, TC161

Requirements

Written examination and computer exercises (min. 50%)

Principles and baselines related to teaching and learning

-

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.

Evaluation criteria for the course

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

Study material

Type	Name	Author	ISBN	URL	Edition, availability, ...	Examination material	Language
Lecture slides	Computational Models for Complex Systems	Juha Kesseli, Pauli Rämö					English

Correspondence of content

Additional information

Complexity arises in situations where the system parts (or agents) interact with each other in a complicated "emergent" fashion. Complex Systems is a highly multidisciplinary research topic that aims to understand complex behaviour and solve practical problems that arise in numerous different situations. This course introduces mathematical tools and concepts that are widely used in the complex systems community.

More precise information per implementation

	Description	Methods of instruction	Implementation
Implementation 1			Contact teaching: 0 % Distance learning: 0 % Self-directed learning: 0 %

Last modified	19.01.2010
Modifier	Jason Lloyd-Price