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

From the Classroom to the Tip of the Spear – Designing a System to Track USMA’s Intellectual Capital

As the world becomes increasingly interconnected and unstable, the US Army’s mission becomes more complex. This reality, when coupled with a smaller force, is increasing the Army’s reliance on foreign partners and its need for non-traditional skills. Given these challenges, deployed units often offset capability gaps using “reachback,” the act of contacting external organizations for critical expertise. Based on recent support to the 1st Infantry Division in Iraq, the United States Military Academy (USMA) possesses considerable reachback potential; however, to fulfill such requests, USMA must first understand its capability and capacity. With this in mind, our research shows that although USMA’s faculty is quite willing to help deployed units, no formalized process exists to catalogue and leverage its collective intellectual capital. As such, we identify the requirement for an intuitive system to fill this void, and we develop and analyze several alternative

Developing a Solution to the TRADOC Analysis Center’s Big Data Problem: A Big Data Opportunity

As data production, collection, and analytic techniques grow, emerging issues surrounding data management and storage challenge businesses and organizations around the globe. The US Army Training and Doctrine Command’s Analysis Center (TRAC) is no exception. For example, among TRAC's many tasks is the evaluation of new materiel solutions for the Army, which typically necessitates the use of computer simulation models such as COMBAT XXI. These models are computationally expensive, and they generate copious amounts of data, straining TRAC's current resources and forcing difficult, suboptimal decisions regarding data retention and analysis. This paper addresses this issue directly by developing "big data" solutions for TRAC and evaluating them using its organizational values. Framed in the context of a use case that prescribes system requirements, we leverage Monte Carlo simulation to account for inherent uncertainty and, ultimately, focus TRAC on several high potential alternatives

Poetry, Money, and the Public : Subsidy and Accountability in Northern Irish Literature

This paper considers the relationship between Northern Irish literature, especially poetry, and its public funding, focusing on two periods of time; before 1971 and after the 1998 Good Friday Agreement. The official regular support for literature via the Arts Council of Northern Ireland began in fiscal year 1970. As the arts subsidy has developed, there has been greater demand for accountability for the use of public money in art, corresponding with the emergence and spread of the logic that measures the value of art in economic terms. This paper discusses how writers have articulated the relationship between literature and society through an analysis of their accounts explaining the need for subsidy, and for subsidised literary activities involving the public, such as poetry reading tours and community arts activities

Redesigning the Senior Leader Engagement Program of the United States Africa Command

AFRICOM conducts hundreds of senior leader engagements (SLEs) each year throughout the African continent in order to create strategic partnerships and military relationships that preserve American interests abroad. While AFRICOM has been planning and executing these engagements since the inception of the organization in 2008, it lacks a well-defined method to systemize its SLE process. As a result, SLE development is largely ad hoc, potentially decreasing the strategic effectiveness of the engagements and increasing their cost. This paper delineates a decision-making framework to redesign and enhance AFRICOM’s SLE program. In particular, it posits a multiple objective decision analysis model that quantifies key stakeholder values and develops several alternatives for future evaluation. Of note, potential solutions imagine a more expansive system where subsets of Senior Leaders (SLs) are assigned to clusters of African countries based on the SLs’ similarity to countries within each cluster, providing a basis for relationship ownership and mutual trust

Blockmodeling and the Estimation of Evolutionary Architectural Growth in Major Defense Acquisition Programs

Naval Postgraduate School Acquisition Research Progra

The Budding SV3: Estimating the Cost of Architectural Growth Early in the Life Cycle

Naval Postgraduate School Acquisition Research Progra

Using Network Science to Estimate the Cost of Architectural Growth

Between 1997 and 2009, 47 major defense acquisition programs experienced cost overruns of at least 15% or 30% over their current or original baseline estimates, respectively (GAO, 2011, p. 1). Known formally as a Nunn-McCurdy breach (GAO, 2011, p. 1), the reasons for this excessive growth are myriad, although nearly 70% of the cases identified engineering and design issues as a contributing factor (GAO, 2011, p. 5). Accordingly, Congress legislatively acknowledged the need for change in 2009 with the passage of the Weapon Systems Acquisition Reform Act (WSARA, 2009), which mandated additional rigor and accountability in early life cycle (or Pre-Milestone A) cost estimation. Consistent with this effort, the Department of Defense has recently required more system specification earlier in the life cycle, notably the submission of detailed architectural models, and this has created opportunities for new approaches. In this dissertation, I describe my effort to transform one such model (or view), namely the SV-3, into computational knowledge that can be leveraged in Pre-Milestone A cost estimation and risk analysis. The principal contribution of my work is Algorithm 3-a novel, network science-based method for estimating the cost of unforeseen architectural growth in defense programs. Specifically, using number theory, network science, simulation, and statistical analysis, I simultaneously find the best fitting probability mass functions and strengths of preferential attachment for an incoming subsystem's interfaces, and I apply blockmodeling to find the SV-3's globally optimal macrostructure. Leveraging these inputs, I use Monte Carlo simulation and the Constructive Systems Engineering Cost Model to estimate the systems engineering effort required to connect a new subsystem to the existing architecture. This effort is chronicled by the five articles given in Appendices A through C, and it is summarized in Chapter 2.In addition to Algorithm 3, there are several important, tangential outcomes of this work, including: an explicit connection between Model Based System Engineering and parametric cost modeling, a general procedure for organizations to improve the measurement reliability of their early life cycle cost estimates, and several exact and heuristic methods for the blockmodeling of one-, two-, and mixed-mode networks. More generally, this research highlights the benefits of applying network science to systems engineering, and it reinforces the value of viewing architectural models as computational objects.Dissertation not available (per author's request

Exploring the Causal Relationship between Factors Affecting US Army Recruitment

Every year, United States Army Recruiting Command (USAREC) dedicates considerable resources to recruiting and accessing soldiers. As the largest branch of the United States Armed Forces, the Army must meet a high recruiting quota while competing in the free-labor market for quality recruits. Over the past two decades, the Army’s success in recruiting ebbed and flowed within the broader context of society and global events. While numerous studies have examined the statistical relationship between factors associated with recruitment, these studies are observational and definitively ascribing causality in retrospect is difficult. With this in mind, we apply fuzzy cognitive mapping (FCM), a graphical method of representing uncertainty in a dynamic system, to model and explore the complex causal relationships between factors. We conclude our paper with implications for USAREC’s efforts, as well as our model’s limitations and opportunities for future work

Modeling and Analysis in Support of Organizational Decisions During the COVID-19 Pandemic

The 2019 coronavirus disease (COVID-19) disrupted economic and social systems on an unprecedented scale. Organizational leaders faced unstructured problems that required novel analysis and evidenced-based decision-making approaches. This paper explains several analytical tools and problem-solving methodologies used at the United States Military Academy at West Point to support decision-making related to operational activities and future planning. While many of the tools apply basic analytical methods, the novelty of this paper lies in the unique application of the tools, visual presentation of data analytics, and the explanation of the contextual circumstances that prompted the development of these tools