Complex System Simulation

Course manual 2018/2019

Course content

The course has two major components, the first two weeks will include lectures covering concepts and methods related to Complex System Simulation. The second two weeks will be group projects (2-3 students), where each group will develop a simulation of a complex system and conduct research using that simulation.

The group project should reflect the concepts and methods covered in the first two weeks, however students are also encouraged to self study and suggest alternative ideas.

Topics to be covered (preliminary, may change):

• Introduction to Complex Systems / Modelling and Simulation
  
• Introduction to Cellular Automata (1D/2D CA, rule codes,phenomenological studies, behaviour classes)
• Self-Organized Criticality (Sandpile Model, Forest fire model)
  
• Complex Networks, Random Networks, Scale Free Networks
• Phase Transitions

Study materials

Literature

• Nino Boccara: 'Modeling Complex Systems' (see also http://www.amazon.com/gp/product/0387404627/103-6032108-4445446?v=glance&n=283155)
• Mark Newman: 'Networks: An Introduction' (see also http://www.amazon.com/Networks-Introduction-Mark-Newman/dp/0199206651)
• Background material: Andrew Ilachinski, 'Cellular Automata: A Discrete Universe', World Scientific Publishing Co. Pte. Ltd., 2001.
• Laszlo Barabasi - 'Network Science' (see also http://barabasilab.neu.edu/networksciencebook/downlPDF.html)
• Advanced Physics Texts: Kim Christensen, Nicholas R. Moloney, 'Complexity and Criticality', Paperback, Imperial College Press, 2005

Objectives

The aim of this course is to introduce students to the field of Complex Systems (modelling, syntheses and analysis) and get them directly involved in research. As such, less emphasis will be on a "top-down" approach than in standard lecture courses. Students will have an opportunity to decide which topics will shape the direction of the course. At the end of this course, the student is able to:

• Explain the concept of emergence;
• Name and reason about different types of emergent phenomena, such as chaos, phase transitions, network connectivity, and complexity;
• Name and reason about different types of computational models used to study these phenomena;
• Implement these models to reproduce a given emergent phenomenon;
• Compare model outcomes with predictions from (mean-field) theory and selected statistical physics constructs;
• Interpret and use the model outcomes in terms of practical applications;
• Implement and study interventions and what-if scenarios to improve/optimize with respect to a practical application.

Teaching methods

• Lecture
• Working independently on e.g. a project or thesis
• Laptop seminar

During the lectures (first half of the course) the students will learn about a wide variety of theoretical concepts relevant to studying complex systems. This will be combined with independently reading provided materials. In the second half a selection of theory and methods will be used to perform a computational study by a project group, in laptop seminars as well as working independently.

Learning activities

Attendance

The programme does not have requirements concerning attendance (OER-B).

Additional requirements for this course:

Although the lectures and laptop seminars are not listed as mandatory it is highly recommended to attend all of them, as complexity related concepts are difficult at first to grasp and to connect to each other (it is still a relatively young field).

Assessment

It is not necessary to pass (>= 5.5) on each individual grade, only the final weighted average must pass. Subgrades will not be rounded. The resit for the theory quiz will be in August; please notify the coordinator if you intend to make use of the resit.

Inspection of assessed work

Contact the course coordinator to make an appointment for inspection.

Assignments

Project plan

• This is a pass/fail group assignment at the start of week 3 to kickstart the project phase. Feedback occurs in the first laptop seminar that follows, per group.

Project presentation

• Final presentation by the group.

Code and documentation

• Group assignment. The code must reproduce the figures used in the presentation. It must be stand-alone understandable with good structure, inline comments, and documentation.

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Timetable

The schedule for this course is published on DataNose.

Additional information

Programming (any language) and basic mathematics skills will be indispensable.

Contact information

Coordinator

• dr. Rick Quax