Topic outline

  • General

    Lecturer: Prof. Ahti Salo

    Course assistant: MSc Jussi Leppinen


    Why this course?
    Resolving major problems calls for decisions that have often far-reaching and uncertain future impacts. These impacts may have to be assessed with regard to multiple objectives whose relative importance may be perceived differently by the stakeholders who take part in or are affected by the decision. 

    This course covers systematic approaches to developing formal models that support decision-makers in tackling complex decision problems. After completing the course, the student is able to 
    • recognize real-life decision problems in which decision models can be beneficial, 
    • build decision models to help solve such problems,
    • solve such models with the help of software tools, and
    • interpret the results of decision these models to generate defensible decision recommendations.

    The student will also become aware of
    • the possible discrepancy between formal models and human behavior and
    • ways to mitigate the adverse effects of this discrepancy.


    Practical matters

    Lectures (24h) on Thursdays 10-12

    • Lectures in Period 1 from Sep 7th to Oct 12th in Hall R2 - 253, Rakentajanaukio 4
    • Lectures in Period 2 from Oct 26th to Nov 30th in Hall U1, U154 Undergraduate Centre

    Exercise sessions (24h) on Tuesdays 12-14

    • All the exercise sessions from Sep 5th to Nov 28th in Hall R2 - 253, Rakentajanaukio 4

    Course exam on Dec 14th at 9-12 in Halls A-E, Undergraduate center


    Assessment methods: Home assignments and the exam; grading principles:

    • Maximum points 36 – you have to get at least 17 to pass the course

    1. Three home assignments (max points 2 + 6 + 10 = 18)
    2. Exam (max points 16) – at least 7 exam points are required to pass the course
    3. Successful submission of course feedback: 1 point
    4. Participation in quest lecture: 1 point (to be confirmed)

    Grading scale: 0-5

    Study material: Lecture slides and exercises

    Language of instruction: English

    Prerequisites: MS-A05XX - First course in probability and statistics (or equivalent), MS-C2105 - Introduction to Optimization (or equivalent)