Making Optimal Decisions: The Mathematics Behind Better Choices
About the course
May be taken as a singular course.
This course can be taken as a micro-credential
How can mathematics help us make better decisions? Whether choosing between alternatives, allocating limited resources, or anticipating the actions of others, mathematical models provide tools for structured and rational decision-making.
This course introduces the core ideas behind optimal decision-making in a clear and accessible way. Students learn how to model decision problems by identifying objectives, constraints, and available choices. Building on this foundation, the course explores key mathematical tools such as optimization methods, decision trees, probability and risk analysis, network models, and game theory for strategic interaction.
Through examples from everyday life, economics, public policy, logistics, and social systems, participants see how mathematical reasoning helps clarify trade-offs, evaluate uncertainty, and analyze competing interests. The course also reflects on the strengths and limitations of mathematical models in real-world decision processes.
The course is designed for a broad audience and requires no prior mathematical background.
Admission requirements
General university admissions certification or prior learning and work experience qualifications.
Application code 9199.
Objectives of the course
After completing the course students will have achieved the following learning outcomes:
Knowledge
- Has a basic understanding of key concepts in quantitative decision methods, including modeling, optimization, uncertainty, and game theory.
- Is familiar with how decision-making tools are applied in various real-world contexts such as business, logistics, public policy, and everyday life.
- Understands the role and limitations of mathematical models in supporting rational decision-making.
Skills
- Can identify and describe decision problems using structured approaches.
- Can interpret and use simple models and tools such as decision trees, optimization frameworks, and strategic interaction scenarios (game theory) to analyze problems.
- Can reflect critically on different solution strategies and evaluate the outcomes of decisions under constraints or uncertainty.
General Competence
- Can communicate decision-making challenges and strategies clearly
- Is able to engage in interdisciplinary dialogue about the use of analytical tools in real-world decision-making, even without advanced technical background.
Teaching methods
This is an asynchronous, digital micro course. There are no scheduled lectures or meetings. All course materials and resources are available digitally via Canvas. The course can be taken at any point during the semester. The course is offered both in the spring and fall.
There will be approximately 12 hours of digital lectures and modules and approximately 50 hours of self study and problem solving.
Language of instruction and examination
EnglishSchedule
The schedules are normally finalized and published well in advance of the start of the semester, often a few weeks beforehand. This gives students the opportunity to organize their studies and prepare for upcoming activities.
It is recommended to check the schedule regularly, as changes may occur.
Examination
| Multiple choice exam | Hand in: 15.10.2026 14:00 Hand out: 15.09.2026 09:00 Duration: 1 Hours |
Grade: Passed / Not Passed |
| Multiple choice exam | Hand in: 15.11.2026 14:00 Hand out: 16.10.2026 09:00 Duration: 1 Hours |
Grade: Passed / Not Passed |
| Multiple choice exam | Hand in: 15.12.2026 14:00 Hand out: 16.11.2026 09:00 Duration: 1 Hours |
Grade: Passed / Not Passed |
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