Sources of uncertainty in interdependent infrastructure and their implications. (September 2021)

• Record Type: Journal Article
• Authors: Reilly, Allison C.; Baroud, Hiba; Flage, Roger; Gerst, Michael D.
• Is Part Of: Reliability engineering & system safety. Volume 213(2021)
• Publication Date: 2021-09
• Subjects: aleatory uncertainty -- epistemic uncertainty, interdependent infrastructure -- network modeling -- simulation; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2021.107756
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: A hierarchy of uncertainty for interdependent infrastructure is developed. System uncertainty is inherent uncertainty within all coupled infrastructure. Modeling uncertainty occurs when systems are given mathematical representations. A review of how uncertainty is treated in the literature is provided. Abstract: While significant modeling advances have unpacked the complexities of interdependent infrastructure, post-disaster reconnaissance consistently demonstrates a wide variability of outcomes and how much is still to be learned. With that in mind, one might expect the treatment of uncertainty to be quite advanced in interdependent infrastructure models, but we find that to not be the case. In this work, we identify, define, and describe two key classes of uncertainty: system uncertainty and modeling uncertainty. System uncertainty is inherent in all complex infrastructure systems and possesses several subclasses (e.g., physical uncertainty and operational uncertainty). Modeling uncertainty occurs when researchers downscale a complex system to a mathematical or other symbolic representation. It too has several subclasses (e.g., parameter uncertainty and completeness uncertainty). We then identify how the literature to date treats uncertainty with respect to each type of uncertainty. While some work has investigated the implications of physical and temporal uncertainty, by and large, most types of uncertainty have had minimal exploration, suggesting significant knowledge gaps. Finally, we suggest a path forward for treatment and discussion of uncertainty, including what can be learned from other fields involving complex interdependent systems.