Lesson 1 · 1 October 2026
Course introduction: thinking in systems
Getting to know each other and the course
David Maier
University Assistant and PhD student, Environmental Systems Sciences
Until 2024 a senior developer. I led the team behind a web platform that connects people with disabilities with personal assistants.
How technological and biological solutions can be connected, using language models and knowledge graphs.
BSc and MSc in Environmental Systems Sciences at this university.
If you were to pursue a career in research, what would you like to study or investigate?
A topic or a question is enough.
First name, row, seat, interest.
30 seconds each: name, studies, what you want to investigate.
Find people with whom you could develop an interesting research question.
You have 15 minutes for the team, the question and sending it.
Per team. These are temporary teams for today.
They don't have to match. Your question should connect them.
You can change teams while ideas develop.
Your output: one research question that connects each member's interest.
One member per team sends it.
Note one decision you made.
Reconstruct two or three events.
Looking back, what most influenced how you joined your team?
A sentence is enough. There is no correct answer.
Three neighbours formed a team.
Being nearby made conversation easy.
Did they share interests or already know each other?
An example. We replace it with events from our teams.
Could we turn one explanation into a rule for how a student chooses?
Students with different interests and information.
Acquaintances, conversations, invitations, memberships.
Our task, the room, the lecturer, and the time available.
Mobus & Kalton (2015), Ch. 1 · Preiser et al. (2022), Ch. 2, pp. 33 to 38
Given by the task
Teams of three to five.
Arose from your choices
Who joined which team.
Which interests came together.
Self-organisation: how memberships form.
Emergence: which patterns arise.
Railsback & Grimm (2019), Ch. 8.1 · Preiser et al. (2022), Ch. 2
Someone joins a team.
Fewer places remain.
Another student considers a different team.
Adaptation: adjusting a decision as the situation changes.
Your underlying interest can stay the same.
Did someone else’s decision change yours?
Railsback & Grimm (2019), Ch. 3, Table 3.1 · Preiser et al. (2022), Ch. 2
If we repeated the team formation 5 minutes later, which statement is most defensible?
Represent students as agents, with states, information, and rules.
“Approach someone with a related interest.”
What can A know?
A could choose C or E. How should A decide?
Does the other person have to agree?
Our model question: how do choices produce team memberships?
Railsback & Grimm (2019), Ch. 1.2 and 1.4 · Schlüter et al. (2022), Ch. 28
Carry out the whole modelling cycle yourself, from conception through implementation to interpretation.
Analyse, simulate, and critically reflect on complex systems with agent-based models.
Deepen your Python basics and learn to work with coding agents.
Learn a bit how real software teams collaborate, with git and GitHub.
Find out that coding can be fun with the right tools.
I want to test my interactive slide deck on you.
In this course, you will analyse systems, build models in Python, and examine what you can learn from them.
Observe a process.
Propose an explanation.
Describe and implement a model.
Compare results with evidence.
Revise the question, assumptions, or model as you learn.
Railsback & Grimm (2019), Ch. 1.3 · Schlüter et al. (2022), Ch. 28
Seventeen appointments, three parts
Sessions 1 to 4
Systems, feedback, emergence, the modelling cycle, agent-based models. Setup of Python, VS Code, git and GitHub.
Sessions 5 to 8
From ODD to code with a traffic model. Analysing runs. LLMs from Python.
Sessions 9 to 17
Your own agent-based model in a small group, with presentations of your preliminary work and of the results.
From session 5 on, we spend most of the time coding. Your dev setup should be done by then; the cheatsheet helps with Python.
| Session | Date | What |
|---|---|---|
| 2 | Wed 14 Oct, 09:00 to 10:30 | Systems, feedback and emergence |
| 6 | Thu 05 Nov | Quiz 1 |
| 7 | Thu 12 Nov | Project topics published, allocation opens |
| 9 | Thu 26 Nov | Project kick-off |
| 11, 12 | Thu 10 and 17 Dec | Presentations of preliminary work (P1) |
| 14 | Thu 14 Jan | Quiz 2 |
| 16, 17 | Thu 28 Jan, 08:30 and 10:00 | Final presentations (P2) |
All sessions are in SR 35.K1, usually Thursday 08:30 to 09:45. Winter break between sessions 12 and 13.
| Part | Points |
|---|---|
| Quiz 1: basics | 15 |
| Quiz 2: analysis and LLMs | 14 |
| Group project | 40 |
| Individual final report | 15 |
| Participation | 16 |
| Grade | Points |
|---|---|
| 1 (Excellent) | 89 to 100 |
| 2 (Good) | 77 to 88 |
| 3 (Satisfactory) | 65 to 76 |
| 4 (Sufficient) | 51 to 64 |
| 5 (Fail) | 0 to 50 |
15 + 14 points
16 points
Took part? Come to me at the end of the session and I award you the points.
40 points, groups of 4 to 5
20 points
A GitHub repository with working, documented code. Research question and approach count.
10 points
10 to 15 minutes plus 5 minutes of discussion. Sessions 16 and 17.
10 points
5 to 10 pages: question, methods, results, discussion.
Topics come from a given list and are allocated online before the kick-off in session 9. You present your preliminary work in session 11 or 12 (P1).
15 points
AI tools encouraged
You are responsible for correctness and for understanding your code. In the final presentation, I ask oral questions about it.
Next meeting: Wednesday 14 October 2026 · 09:00 to 10:30 · SR 35.K1
Name one thing you take away from this lesson.
Something you see differently now.
Something that surprised you.
A question you still have.
One you are fine with the class seeing.
Post your username on Moodle.
On the course website, GitHub identifies you in the participation exercises.
In the group projects, GitHub is the main tool for collaboration.
Also apply for GitHub Education at education.github.com/students: as a student you get free Copilot usage. Approval is not instant, so apply early.
Railsback, S. F. & Grimm, V. (2019).
Agent-Based and Individual-Based Modeling: A Practical Introduction.
2nd ed.
Ch. 1: models and ABM; Ch. 3: design concepts; Ch. 8.1: emergence.
Mobus, G. E. & Kalton, M. C. (2015).
Principles of Systems Science.
Ch. 1: systems, relationships, and boundaries.
Biggs et al. (eds., 2022).
The Routledge Handbook of Research Methods for Social-Ecological Systems.
Preiser et al., Ch. 2, pp. 33 to 40: relationships, adaptation, and context.
Schlüter, Lindkvist, Wijermans & Polhill, Ch. 28, pp. 383 to 389: agent-based modelling.
The classroom activity and toy model are teaching examples. Their outcomes are not findings from these sources.