771A21 · 7.5 credits · Advanced level (A1N) · Computational Social Science
Agent-Based Modelling
Active · next start VT 2027
98%
passed the latest regular sitting
ASSI · 30 May 2025 · 48 students
99%
passed the regular sittings, all sittings over the last 5 years
ASSI · 102 students
8%
Harder than 8% of courses at advanced level
Compared with 285 courses at the same level, ordinary exams over the last three academic years. 48 students.
Part of 1 programme
Other module
ASSI Assignments
- A
- B
- C
- D
- E
- F
Main rounds
99% passed · 102 students
- 98%48
- 100%19
- 100%20
- 100%15
Later rounds
86% passed · 21 students
- 86%21
- Pass a later round: 86% (21 students), compared with 99% (102 students) on the ordinary exam
- Get B or A: 51% (123 students)
Show small sittings (2)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
- 100%3
About the course
Aim
After completion of the course, the student should on an advanced level be able to:
- Describe key applications of agent-based simulation modeling (ABM) in the social sciences;
- Explain the logic behind and the explanatory role of agent-based modeling;
- Design and program different types of agent-based models;
- Run agent-based computational experiments;
- Evaluate the results of agent-based simulations through various forms of statistical sensitivity analyses.
Content
Agent-based modeling is a methodology for analyzing how groups of interacting individuals or other types of agents bring about various macro outcomes. This course provides a detailed introduction to the agent-based modelling (ABM) technique. The course covers all the steps in the process of developing an ABM, from theoretical design to model implementation and model evaluation. During intensive computer labs, ABMs are implemented using object-oriented programing, including the treatment of variables, commands, and procedures. The course includes practical work with various types of computer-based experiments, as well as methods for evaluating the robustness of simulation results using various statistical sensitivity analyses.
- Examiner
- Eduardo Tapia
- Main field of study
- Computational Social Science