When building teams, organizations select individuals capable of acquiring and maintaining skills required for team success. Often, teams undergo a period of skill non-use that can result in skill decay during the lifespan of the team. Despite this reality, researchers have failed to create a comprehensive model that can predict complex skill decay at multiple levels within an organization. McDermott and colleagues (2016) proposed an equation to address this gap. Using simulation data, the present paper applies this equation to model expected skill decay in three hypothetical contexts: simple, mid-range, and complex engineering design cases. The model predicts team-level skill decay in a holistic manner by integrating factors from task, environment, and team characteristics. Results indicate that, while novel for its integration mechanisms, the model maintains strong congruency with previous literature for individual factors. We discuss ways that organizations may reduce complex skill decay as well as future research directions.
Cognitive bias is a general issue in human decision making. It is easy to assume there is less bias in engineering, systems engineering, and program management, as these are generally considered rational processes. However, human decision making is almost always affected by bias. In the systems engineering practice as well as training systems engineers, we find directly addressing principles of human cognition, decision‐making, and bias to be an important part of individual and team learning. In the context of systems engineering competencies, these principles are core to mastery of systems and critical thinking, facilitation, and team dynamics. This paper discusses the characteristics of thinking, cognition, and decision‐making; conceptual approaches in psychology that frame these characteristics in the context of human decisions; common biases that affect decisions in the domain of systems engineering; and application of systems thinking tools to help overcome these issues.
Engineering resilient cyber physical systems (CPS) is viewed as a systems engineering (SE) challenge. The research and education programs in computer and software security and resilience were advanced through the 1990's with many formal approaches to system reliability, security, dependability, and safety. Since that time, the internet and the information security needs in information technology (IT) systems has had a large education focus. Meanwhile, the scale and complexity of today's critical computing systems used in engineering domains has increased immensely. There has not been a related increased focus on research, knowledge, and education specifically addressing dependable and secure computing in domains such as infrastructure, industrial control, defense, and emerging commercial autonomous systems and the Internet of Things (IoT). While security education in the U.S. has greatly expanded in the domain of IT systems and related computer science and engineering fields, there exists a lack of engineering education programs and opportunities for engineering graduates to gain knowledge and skills related to design of secure, safe, and dependable CPS, what we call resilient CPS. There is also a lack of investment in laboratory facilities that can simulate large scale CPS, which are costlier to develop than IT related facilities. Formal research or studies in CPS and resilient computing related curricula are also difficult to find, although the domain is well researched. This paper discusses the primary foundations and characteristics of resilient CPS as a step toward a general taxonomy, the emerging needs and gaps in engineering education related to these systems, and the knowledge areas and bodies of knowledge that are already established or need to be developed in the education system. The goal is to bring broad attention in the systems engineering community to these needs.
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