Hold Ctrl-tasten nede. Trykk på + for å forstørre eller - for å forminske.


Modeling for Decision Insight

This is the study programme for 2020/2021.

Learning outcome

  • An understanding of modeling's role in strategic decision-making and of what constitutes a good decision model
  • A thorough understanding of essential elements of good modeling principles to strive for clarity in complex and uncertain decision-making situations.
  • Be able to recognize and account for the human biases and errors that most often affect decision making.
  • Develop models, tools, and mental frameworks that will allow you to deal effectively with uncertainty
  • Revise your beliefs after gathering additional information using Bayesian methods
  • Examine and quantify the value created by gathering additional information
  • Quantify your appetite for risk and how to factor this into your decision making
  • Use Bayesian Networks to help structure a decision model
  • Design models with a parametric approach to maximize insights
  • Anticipate decision-makers' questions and design in features to answer them
  • Build a decision model for a typical engineering or corporate decision situation
  • Discover how to create flexible models that allow you to analyze multiple strategic alternatives
  • Understand sensitivity analysis and the information it provides
  • Conduct probabilistic analysis to general additional insights and understand risk
  • Identify how to effectively communicate the insights derived from your model

  • Skills needed to build a good basic decision model and to use it in generating powerful insights into the decision situation
  • Be able to apply and construct decision models and to use the most important elements in decision analysis relevant to engineering type decision-making in the face of uncertainty.

General qualifications:
Students should understand fundamental logical principles and analyses and be able to communicate their choices and recommendations clearly.


Everyone makes decisions, but few people think about how they do it. Yet, psychological research shows that we are prone to many different errors of thought that degrade our decision-making ability. In this course we will discuss the principles and fundamental concepts for the normative theory of decision making under uncertainty. Decision models are created to generate insights that can guide and inform decision-making. This course will equip you to answer questions including, which decision alternative creates the most value? Why is it better than the others? How much uncertainty does it entail? What are the most important sources of uncertainty? You will create models, extract powerful insights and be prepared to present analysis results to those who make complex decisions in uncertain environments. We will develop a language, set of theories, and modeling tools to transform complex decisions into ones where the course of action is clear.
What are the benefits of building and using formal models, as opposed to relying on mental models or just "gut feel?" The primary purpose of modeling is to generate decision insight; by which we mean an improved understanding of the decision situation at hand. While mathematical models consist of numbers and symbols, the real benefit of using them is to make better decisions. Better decisions results from improved understanding, not just the numbers themselves.

Required prerequisite knowledge


Recommended previous knowledge

A bachelor degree in engineering or equivalent


Weight Duration Marks Aid
Folder evaluation1/1 A - FStandard calculator.
Lectures and compulsory exercises. The overall course grade will be based on folder evaluation which includes a final exam (30%), a modeling project (40%), and exercises (30%). Each element is percentage-based whilst the overall course grade is letter-based. The lectures are in English.
The final grade is made up of:
  • 30% exam - standard calculator is allowed
  • 40% project
  • 30% exercises

Course teacher(s)

Course coordinator
Reidar Brumer Bratvold
Head of Department
Alejandro Escalona Varela

Method of work

The work will consist of 6 hours of lecture and scheduled tutorials per week. Students are expected to spend an additional 6-8 hours a week on self-study, assignments, and project.

Open to

Single Course Admission to PhD-courses
External candidates


Literatur will be published as soon as it has been prepared by the course coordinator/teacher

This is the study programme for 2020/2021.

Sist oppdatert: 09.08.2020