Entry requirements

General entry requirements

Learning outcomes

On completion of the course the student shall be able to:

• explain and apply different computational models, including agent-based models, social network analysis and computational text analysis, to study social phenomena and systems;
• use these models with appropriate software tools;
• evaluate and compare the suitability of different models to address social science problems;
• make judgments with regard to relevant scientific, social and ethical aspects in the application of computational social science.

Content

The course introduces computational approaches to model human behaviour and social phenomena. Core concepts in computational social science are covered, such as observational studies (what types of data exist, possible biases and how to use data for modelling), basic concepts and techniques for running experiments (asking vs. observing, natural experiments, simulations, validity and generalisation) and discuss key issues such as ethical considerations.

The course has both a theoretical and a practical perspective, where students learn basic principles and also how to apply them in practice in three main areas:

• social network analysis,
• text analysis,
• agent-based modelling and simulation.

Instruction

Lectures, lessons, labs and seminars.

Assessment

Written assignments (4 credits), active participation in obligatory seminars (1.5 credits) and project (2 credits).

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.