Modeling for Decision Insight (MOD500)

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.

Course description for study year 2023-2024


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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.

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.

Required prerequisite knowledge


Recommended prerequisites

A bachelor degree in engineering or equivalent


Written project report and written exam

Form of assessment Weight Duration Marks Aid
Report 1/2 1 Semesters Letter grades
Written school exam 1/2 4 Hours Letter grades All 1)

1) All aids allowed. Collaboration is not allowed.

The overall course grade will be based on continuous assessment which includes a final exam, and a modeling project. All parts must be passed in order to obtain a final grade in the course.The project is a group project, and an extended analysis which must be presented in a written report over no more than 20 pages. A resit exam is offered for students who do not pass the written exam. Students who do not pass or want to improve their grade in the project report, must take this when the course is offered again.

Course teacher(s)

Course coordinator:

Reidar Brumer Bratvold

Course teacher:

Enrico Riccardi

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. Excel, Python and DPL will be used for assignments, projects and tests. The course does provide a short introduction to Python but it is NOT a course in Python so the students will need to consult other resources for learning Python if they are not already familiar with it. A brief introduction to DPL will also be provided early in the course.

Open for

Single Course Admission to PhD-courses Admission to Single Courses at the Faculty of Science and Technology Exchange programme at Faculty of Science and Technology

Course assessment

There must be an early dialogue between the course coordinator, the student representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital course evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.


The syllabus can be found in Leganto