Students should:be able to describe human decision makingbe able to structure decision problems and develop creative decision optionsbe able to use data-driven software tools to support decisions

front lectures,exercises with examples,discussions

Topicshuman decision making (biases, heuristics)decision modelinguncertainty modelingpreferences modelingsoftware tools for decision support

Robert T. Clemen "Making Hard Decisions: An Introduction to Decision Analysis."Daniel Kahneman, "Thinking Fast and Slow"

Project Work (50% of the final score)Written exam (50% of the final score)

Project work: students submit individual project reports on solving a decision-making problem with the tools we learn at lectures (influence diagram, decision tree, risk profile, sensitivity analysis etc.)Written exam: quizzes with open-ended answers and quizzes with multiple-choice answers

Minimum 50% of the project (i.e. 25% of the total score)Minimum 50% of the exam (i.e. 25% of the total score)pass scores: 4: 50-60%, 3: 61-70%, 2: 71-80%, 1: 81%+