Economics and Decision Analysis for Engineers (PET685)

This course teaches the skills required for a key component of an Engineer's job - creating value by making decisions that yield optimal returns on the allocation of human and financial resources.


Course description for study year 2024-2025. Please note that changes may occur.

Facts

Course code

PET685

Version

1

Credits (ECTS)

10

Semester tution start

Autumn

Number of semesters

1

Exam semester

Autumn

Language of instruction

English

Content

NB! This is an elective course and may be cancelled if fewer than 10 students are enrolled by August 20th. The course will not be offered after the fall semester of 2024.

Engineers perform technical work to support the "business" objectives of the organization they work for (corporation, government). It is therefore important that they understand that "business" because it will influence the judgments they make. Economic evaluations provide the main source of the organization's information by which investment and operational decisions are made regarding the most effective use of resources. It is these decisions corporate value is being created (or destroyed).

There are many subtleties and assumptions that underlie the apparently straight-forward economic calculations that are often seen. Consequently, a fundamental understanding of the concepts behind economic evaluation and of techniques for performing them within a corporate decision making context, are essential skills. Furthermore, as all investment decisions are made without knowing what the future holds, understanding the uncertainties we face in any given decision situation is essential for good decision-making.

This course provides the tools necessary for engineers to economically evaluate their uncertainties and decisions. It also allows engineers to communicate with the "business" world, which is generally more interested in monetary values, and their risks, than engineering tolerances and specifications. It also provides understanding and knowledge of economic and business concepts, time-value of money, discounted cash flow, cash-flows, net present value and other economic decision criteria, the decision-making process, multi-objective decision making, decision-tree analysis, and value-of-information & flexibility. Some of the psychological and judgmental aspects of how people respond to uncertain and complex decision situations will be discussed.

Moreover, we delve into the transformative potential of Artificial Intelligence (AI) in this space. AI, one of the most momentous technological advancements, harnesses Machine Learning and data analysis to predict future outcomes, enhancing the efficiency of investment decisions. By rapidly processing vast data, including social sentiment and financial reports, AI provides insights on company performance and optimizes financial operations. Join us to understand how AI reshapes economic evaluations and decision-making in today's digital age.

Learning outcome

The course provides understanding and knowledge of economic and business concepts, time-value of money, discounted cash flow, cash-flows, net present value and other economic decision criteria, the decision-making process, multi-objective decision making, decision-tree analysis, and value-of-information & flexibility. Some of the psychological and judgmental aspects of how people respond to uncertain and complex decision situations will be discussed.

Required prerequisite knowledge

None

Recommended prerequisites

Bachelor degree in engineering.

Exam

Form of assessment Weight Duration Marks Aid
Written exam 1/1 4 Hours Letter grades - 1)

1) Access to Python, Excel and DLP.

The assessment consists of a digital on-campus exam. Students will not have access to ChatGPT or other LLM models.The students must hand in and pass four assignments during the semester in order to be allowed to take the final exam.

Coursework requirements

Compulsory assignments

Portfolio containing five assignments that needs to be approved in to access the final exam.

The students may cooperate on the assignments.

Course teacher(s)

Course coordinator:

Reidar Brumer Bratvold

Study Program Director:

Lisa Jean Watson

Study Adviser:

Karina Sanni

Head of Department:

Alejandro Escalona Varela

Method of work

Lectures, Interactive discussions, compulsory assignments, self-study or group work. Lecture language is English. Excel and Python will be used for assignments 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.

Open for

Admission to Single Courses at the Faculty of Science and Technology
Energy, Reservoir and Earth Sciences - Master of Science Degree Programme
Exchange programme at Faculty of Science and Technology

Course assessment

There must be an early dialogue between the course supervisor, the student union 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 subject evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.

Literature

Search for literature in Leganto