Characterizing Representational Uncertainty in System Design and Operations

Abstract

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.