6 EC
Semester 1, period 2
5132MOFS6Y
We use models of various forms, both in everyday life as in science, for many purposes: to express ourselves, understand the world around us and to solve problems of various kinds. Reasons to use models may be to exchange of ideas between different disciplines, identify processes where understanding is lacking, generate predictions or test the theoretical consistency of an idea.
In this course, our aim is to learn about the various ways by which models may be used to in relation to sustainability questions. The modeling skills you will acquire will not be limited to sustainability but be broadly applicable: you will learn about the meaning of fundamental modeling concepts, the different phases in a modeling-project, the various possible relations between models and observations (data), conducting experiments with models and reporting about model results.
In the first part of the course, basic concepts and skills are taught to translate a (complex and interdisciplinary) system into a computer model. While focusing on system dynamics and agent based models, we will explore the various opportunities this offers for understanding system behavior, assessing risk and uncertainty, and experimenting with possible interventions.
Besides these two important model types, we will also study, we will apply various types of socio-ecological models and explore the properties of these. The three types of models we cover are:
1. system dynamics models (contemporary climate models belong to this class)
2. agent-based models (these were e.g. used to inform decision making during the Covid pandemic)
3. statistical and machine learning models
is available in the UvA library (made available via hyperlinks) and otherwise open source material is used
is available at the Canvas site
is available at the Canvas site
various web-based tools & open source software that can easily be installed
During the lecture/lab and discussion sessions on Wednesdays, important concepts and ideas will be explained and the study material will be placed in context. Next students start working on your comupter practical, where theydevelop or modify computer code and submit it as an html file by the start of the next computer practical on Friday.
There will also be in-class activities that help to understand the material.
During each (computer) lab session on Friday, we will illustrate the theoretical ideas of the week in the context by a hands-on case study or a task.
Starting from the second week of the course, students will work on a modeling case-study in a group of 3-4 students. First project ends with the presentation in the end of week 4. Second prohect will have presentation on Wednesday of the last week of the course.
Each lecture/lab is accompanied by homework.
|
Activity |
Hours |
|
|
lectures |
14 |
|
|
(computer) labs |
24 |
|
|
seminar |
4 |
|
|
digital exam |
3 |
|
|
modeling project |
60 |
|
|
Self study |
63 |
|
|
Total |
168 |
(6 EC x 28 hr) |
Additional requirements for this course:
For this course, an attendance requirement applies to the lectures and computer practicals. During the lectures you will learn to recognize different phases in model development. The guidance and exercises during computer practicas ensure that the learning objectives can be achieved. These are tested in the final exam.
| Item and weight | Details |
|
Final grade | |
|
Computer practicals | |
|
Submitted Homework | |
|
Small research project report | |
|
small research project presentation | |
|
Tentamen digitaal | Must be ≥ 5.5 |
The digital exam will consist of (1) open questions about the reading material as well as discussions during the lectures and practicals and (2) practical coding exersises. The study-material is available during the exam and also personal notes (hard-copies) can be used at the exam.
| Learning goal: | digital exam: | preparation & participation: | modeling project: |
|---|---|---|---|
| #1. | x | x | |
| #2. | x | x | |
| #3. | x | x | |
| #4. | x | x | x |
| #5. | x | x | x |
The partial grades remain valid for 1 year.
When the partial grades are announced, students can inspect their assessed work, as well as the answer models (or assessment models) in Canvas and/or the testing environment.
Each lectur is accomanied by a short assignment, to be submitted online.
Home work and computer practicals are graded with the scores from 0 (not submitter) to 2 (fully done).
Modelling project is a group assignment and both the preseantion and the short report are graded separately.
An example exam will be available by November 25. During the exam students can use their own printed homework and computer practicals.
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
| Week | Day & Date | Time | Topic | Homework |
|---|---|---|---|---|
| 44 | Wednesday, Oct 29 | 13:00–17:00 | Start-up lecture; computer practical (CP) 1 on data simulation. | Learn/refresh the basics of R & RStudio. Homework (HW) 1. |
| 44 | Friday, Oct 31 | 13:00–17:00 | Lecture 2 — introduction to system dynamics models; CP 2. | HW 2 on basic system dynamics models. |
| 45 | Wednesday, Nov 5 | 13:00–15:00 | Lecture 3 — epidemiological models; CP 3 — code basics: epidemiological models. | HW 3 — epidemiological models. |
| 45 | Friday, Nov 7 | 13:00–17:00 | CP 4 — intervention analysis with epidemiological models; introduction of small research projects. | HW 4 — epidemiological models; start research projects. |
| 46 | Wednesday, Nov 12 | 13:00–15:00 | Lecture 4 — hydrology and climate-dynamics models; CP 5. | HW 5 — exploration of hydrology models. |
| 46 | Friday, Nov 14 | 13:00–17:00 | CP 6 — climate models. | HW 6 — climate models. |
| 47 | Wednesday, Nov 19 | 13:00–15:00 | Lecture 5 — statistical models; CP 7. | HW 7 — linear models. |
| 47 | Friday, Nov 21 | 13:00–17:00 | CP 8 — generalised linear models. CP8 | HW 8 — generalised linear models. |
| 48 | Wednesday, Nov 26 | 13:00–15:00 | Lecture 6 — introduction to decision-making; linear programming; CP 9. | HW 9 — basic linear programming. |
| 48 | Friday, Nov 28 | 13:00–17:00 | CP 10 — spatial prioritisation. | HW 10 — spatial prioritisation. |
| 49 | Wednesday, Dec 3 | 13:00–15:00 | Lecture 7 — agent-based models; CP 11. | HW 11 — agent-based models in R and NetLogo. |
| 49 | Friday, Dec 5 | 13:00–17:00 | CP 12 — NetLogo models. | HW 12 — NetLogo models. |
| 50 | Wednesday, Dec 10 | 13:00–15:00 | Lecture 10 — course round-up; presentation of second research project; group project — presentation of results. | - |
| 50 | Friday, Dec 12 | 13:00–17:00 | Q&A | - |
| 51 | Monday, Dec 15 | 9:00 - 12:00 | exam |
It is important that everyone feels safe at the UvA and Future Planet Studies. We are committed to provide social safety and we offer various forms of support for people experiencing inappropriate or unsafe situations. Consult the UvA website or Future Planet Studies Canvas page for more information and contact info.
AI assistants maybe used as tutor, but not during assignments or exams.
In order to provide students some insight to how we use student feedback to enhance the quality of education, we include it here in combination with the ways by which it has been used to improve the course.
The students from last year liked the diversity of activities in the course but found the amount of study material as well as the group-modelling assignments challenging. Therefore this year we redesigned the course indtrocing weekly lectures, and two short instead of one long research projects.