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Knowledge Based Decision Model for Architecting and Evolving Complex System‐of‐Systems

Architecting complex System‐of‐System (SoS) has evinced keen interest recently, specifically towards factoring in the operational and managerial independence of the constituent systems, and the evolutionary and adaptive nature of SoS development. Architectural decisions have a significant bearing on the operational measures of success, referred to as Measures of Effectiveness (MOEs), of the SoS. There is inherent uncertainty while making an architectural decision for complex SoS due to the incomplete knowledge on the implications of the decision. The impact on the MOEs of the SoS may be realized only later in the development lifecycle, influencing the performance and the emergent behavior of the SoS. Further, when learning cycles on the architectural decisions are experienced, once the implication of decision is realized, there needs to be means for incorporating the feedback on the decisions taken, and to reflect back on the uncertainty associated with the decisions. This paper proposes a knowledge based decision model for architecting and evolving complex SoS, that takes into account the uncertainty associated with architectural decisions and the learning cycles and feedback loops experienced. It also enables augmenting the architectural knowledge base, both at a constituent system level as well as at the System‐of‐System level. The proposed model adopts a decision oriented view that enables factoring in uncertainty, learning cycles and feedback loops in architectural decisions. It facilitates exploring the implications of various SoS evolution scenarios on architectural decisions, while analyzing MOEs of the SoS in relation to the MOEs of the constituent systems.