This case study outlines a master's-level course in Systems Engineering. The course focuses on learning about model-based decision support frameworks and tools under conditions of deep uncertainty. Designed to equip students with essential tools for strategic decision-making in novel and uncertain situations, the course targets individuals interested in addressing decision problems within contexts of deep uncertainty. It utilizes a System Dynamics (SD) workforce simulation model, developed from a real-life case study, alongside the Exploratory Modelling and Analysis (EMA) Workbench. The course structure emphasizes the development of students' abilities to formulate decision problems and analyse outcomes to infer decision-making insights in the face of complexity and uncertainty.
Delivering the course during Semester 2, 2022 and 2023 for a diverse student cohort in terms of age, discipline (such as engineering and business backgrounds), and technical backgrounds, allowed us to identify several aspects that succeeded and the reasons behind their success. These aspects include blended teaching, real-life case studies, power of modern technology, and interactive activities. Likewise, we identified several challenges and the underlying reasons. These challenges include a little overwhelming course content and lack of consideration of diversity of student cohort at the first iteration in 2022. While it was both interesting and challenging to deliver this course for the diverse student cohort, it was also a rewarding experience for both participants and course staff.
The study reveals that students' grasp of complexity and uncertainty within decision contexts has advanced significantly through practical exposure and real-life case study involvement. Additionally, practical exposure provides students with a valuable opportunity to immerse themselves in the intricacies of the problem at hand. This experience not only imparts the skills needed to navigate complex situations but also cultivates the ability to arrive at well-informed decisions, even when confronted with uncertainties.