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Organizations use command and control (C2) systems to collect, organize, and disseminate information in order to make decisions, impart instructions, and manage resources to accomplish a mission. C2 agility and robustness are critical to ensure that a C2 system can perform well in a variety of environments. One system design principle, decentralization, has been closely linked to desirable system characteristics such as agility and adaptability, but its relationship to system performance robustness is not well‐established. In this study, we explore C2 system architectures—ranging from fully centralized to fully decentralized archetypes—to assess their performance and robustness characteristics across a spectrum of operating environments. While the centralized archetype achieves high performance in favorable environmental conditions, its performance quickly degrades when conditions worsen, hindering its overall robustness. Conversely, the decentralized archetype achieves a lower but more stable performance profile resulting in more robustness when performance requirements are lower. Finally, we explore alternative, hybrid architectures with varying degrees of centralized and decentralized decision‐making. We find that by centralizing only certain system‐consequential functions such as resource allocation, and decentralizing more focused decision functions which can be performed suitably with only local information and resources, system performance and robustness are improved beyond that of the simple archetypes.

Systems engineering and design (SE&D) researchers increasingly tackle questions at the intersection of technical and social aspects of complex systems design. Practical challenges of access, limited observation scope, and long timescales limit empirical study of SE&D phenomena. As a result, studies are typically conducted in model worldsettings abstracted from the real world, such as behavioral experiments with student subjects. Model worlds must be representative of the phenomena being studied to ensure insights generalize to the real‐world settings. Currently, there is a lack of shared understanding and standards within the SE&D research community to evaluate representativeness of model worlds. This communication captures the results of ongoing efforts to build consensus on this topic: it defines the concept of model worlds, disambiguates representativeness from related concepts, and draws comparisons to other research domains. It outlines a potential path forward and calls for community participation in establishing shared standards for model world representativeness in SE&D research.

This paper discusses the role that qualitative methods can and should play in engineering systems research and lays out the process of doing good qualitative research. As engineering research increasingly focuses on sociotechnical systems, in which human behavior and organizational context play important roles in system behavior, there is an increasing need for the insights qualitative research can provide. This paper synthesizes the literature on qualitative methods and lessons from the authors’ experience employing qualitative methods to study a variety of engineering systems. We hope that by framing the key issues clearly, other engineers who hope to join the qualitative path will build on what we have learned so far to enable greater insight into engineering systems.

This paper develops the concept of representational uncertainty to frame a critical challenge in systems engineering. Representational uncertainty arises in complex systems problems when the correct system representation cannot practically be known until some initial work has been undertaken. Drawing on empirical evidence from two very different system design problems, we illustrate the nature and prevalence of representational uncertainty in systems engineering practice. Our findings show that errors in the system representation may lead to wasted design work that explores the wrong tradespaces, expects the wrong value from design choices, and organizes work on the wrong set of decomposed subproblems. We find that mitigating representational uncertainty requires design processes that incorporate discovery of the system properties through a “reality check” early in the design process. We consider the implications for systems engineering processes and tools, and highlight directions for future research.