OPIM 410 Decision Making Under Uncertainty is about decision science, which is an interdisciplinary field that derives from economics, psychology and quantitative modeling. The course is designed as an introduction to the basic concepts, principles and methods of decision sciences; our main focus is going to be trying to answer the question: ?how can we help people make better decisions (when there is some uncertainty present)??, but we will also talk about ?how people actually make decisions?. Over the years, several tools, methods and models have been proposed to help people make better decisions under uncertainty. Two of these tools, and the most popular ones, are simulation and decision trees, and those are the tools that we are mainly going to focus on in this course. In the first part of the course, using decision trees and simulation, as well as various other approaches, we will learn to identify a decision making problem, break it down to more manageable parts, and then pick the best alternative. While doing so, we shall also have the chance to discuss the concept of randomness, especially why it is so important, and how it can be measured. We will further talk about the role of information, and how it impacts a decision making situation. In the second part of the course, we will work with a topic that has gained ample interest in decision sciences, as well as other fields: risk. We will talk about utility theory, which is a tool that enables the decision maker or the people helping the decision maker to incorporate elements of risk into the decision problem. The topics we will cover under this heading include the basics of utility theory, identifying and incorporating different feelings/attitudes towards risk into a decision problem, making choices in this context, and measuring how much a person values a given construct.
The course requires some familiarity with quantitative modeling techniques and basic probability concepts. However, no high level mathematics is required.