Advanced Decision Analysis II

Faculty of Science and Technology
Department of Petroleum Engineering

Introduction

This course gives participants both the understanding and skills needed to build useful models in decision situations and to use them to draw powerful insights into the decision. The course topics range from conceptual, such as how to define the goodness of a model, to practical, such as best practices in laying out the structure of a model in Microsoft® Excel. In this highly interactive course, most of the lectures include hands-on experience for the participants to cement their grasp of new skills. In the exercises, participants build a complete decision model under the guidance of the instructor. The lectures will take place in a lecture room where the students have access to computers with Excel and other modeling applications. Because this course builds on the core course MPE630 - Decision Analysis I, we strongly encourage participants complete this course first. In addition, participants are expected to be familiar with but not necessarily experts in using Excel.

Learning outcome

At the end of the course the student is expected to:

- Understand modeling's role in strategic decision-making

- Understand what constitutes a good decision model

- Have the skills needed to build a good basic decision model and to use it in generating powerful insights into the decision situation

- Understand best practices in decision modeling

- Know how to use Excel spreadsheets and other software tools effectively for decision analysis

- Have the skills to structure and build a good decision model for an unstructured real world decision situation

Contents

- Review of decision analysis basics

- Planning the model

- Best practices in decision modeling using Excel

- Model development

- Debugging models

- Software tools for probabilistic analysis

- Sensitivity and probabilistic analysis

- Drawing and communicating insights

Prerequisites
No

The grade for the course will be based on test (30%), exercises (40%), class participation (10%), and project (20%). Class participation will be evaluated subjectively. As the instructor, I value attendance, punctuality, familiarity with the required readings, and classroom questions or comments that are relevant and insightful. Differences in technical background or skill are not a criterion. In general, I evaluate classroom participation on the basis of the extent to which you contribute to a positive and effective learning environment (for yourself and others). Demonstrating mastery of advanced topics at innappropriate times does not contribute to a positive learning environment. Correcting me when I make a mistake, however, or asking what may appear to be a naive question, quite often contribute positively. ("Dumb" questions, which rarely are that, are usually shared by many students, and asking one can keep the class on track.)

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