Avoiding Pitfalls in Undergraduate Simulation Courses

Simulation development has historically been a specialized skill performed by engineers with graduate-level training and industry experience. However, advances in computing technology, coupled with the rise of model-based systems engineering, have dramatically increased the usage of simulations, such that most engineers now require a working knowledge of modeling and simulation (M&S). As such, an increasing number of undergraduate engineering programs are now requiring students to complete a simulation course. These courses are intended to reinforce foundational engineering knowledge while also teaching the students useful M&S tools that they will need in industry. Yet, a number of pitfalls are associated with teaching M&S to undergraduate students. The first major pitfall is focusing on the tool or software without properly teaching the underlying methodologies. This pitfall can result in students becoming fixated on the software, limiting their broader knowledge of M&S. The second pitfall involves the use of contrived, academic tutorials as course projects, which limits students from fully understanding the simulation design process. The third and fourth pitfalls are only superficially covering verification and validation and not building upon material that was taught in other courses. Finally, the fifth pitfall is the over-reliance on group projects and tests over individual projects. These pitfalls were uncovered during academic years (AYs) 2017 and 2018 in different undergraduate simulation courses at the United States Military Academy. The combat modeling course adapted its structure and content in AY2019 to address these pitfalls, with several lessons learned that are applicable to the broader simulation education community. Generally, students gained a broader understanding of M&S and submitted higher quality work. Additionally, the course-end feedback found an overall increase in M&S knowledge, with many students choosing to use M&S to support their honors theses and capstone projects, a trend not seen in past years

Causal inference methods and simulation approaches in observational health research within a geographical framework

Statistical methods are often used habitually, perhaps without sufficient reflection on their robustness in a range of novel circumstances. Increasingly, there is a desire to unravel the complexities of humans interacting with their environments, to improve our understanding and explanation of what influences population health in the wider context of our living environment. A framework is provided for using simulation and causal inference methods to evaluate analytical approaches in health geography, to introduce the reader to some of the considerations around complexity of context and data generation that may need to be reflected upon carefully when applying such methods in their own work. These methods have the potential to aid researchers in their explanation of what factors are important for population health and well–being in the context of our geographical environment while avoiding potential pitfalls in their work and allowing for greater critical evaluation of the methods employed by themselves and others. This thesis considers the utility of simulation to investigate applied problems related to mathematical coupling and specific considerations that need to be made in relation to research on the relationship between limiting long–term illness and deprivation and the challenges encountered while investigating the relationship between population mixing and childhood leukaemia —with all such considerations examined through the lens of cause and effect. The datasets chosen are representative of many others in health geography and span the full range of outcome prevalence rates likely encountered. Methods in causal inference and simulation are demonstrated to be powerful tools in understanding potential bias in research analyses. With careful planning, forethought and reflection on the data generating processes of the context of interest, causal inference and simulation methodologies are accessible to all researchers to improve their understanding of the methods they employ to address the research questions they pose

Multivariate methods and small sample size: combining with small effect size

This manuscript is the author's response to: "Dochtermann, N.A. & Jenkins, S.H. Multivariate methods and small sample\ud sizes, Ethology, 117, 95-101." and accompanies this paper: "Budaev, S. Using principal components and factor analysis in animal behaviour research: Caveats and guidelines. Ethology, 116, 472-480"\u

Evolution of a supply chain management game for the trading agent competition

TAC SCM is a supply chain management game for the Trading Agent Competition (TAC). The purpose of TAC is to spur high quality research into realistic trading agent problems. We discuss TAC and TAC SCM: game and competition design, scientific impact, and lessons learnt