Uncertainty analysis in multi-sector systems: Considerations for risk
analysis, projection, and planning for complex systems
Abstract
Simulation models of multi-sector systems are increasingly used to
understand societal resilience to climate and economic shocks and
change. However, multi-sector systems are also subject to numerous
uncertainties that prevent the direct application of simulation models
for prediction and planning, particularly when extrapolating past
behavior to a nonstationary future. Recent studies have developed a
combination of methods to characterize, attribute, and quantify these
uncertainties for both single- and multi-sector systems. Here we review
challenges and complications to the idealized goal of fully quantifying
all uncertainties in a multi-sector model and their interactions with
policy design as they emerge at different stages of analysis: (1)
inference and model calibration; (2) projecting future outcomes; and (3)
scenario discovery and identification of risk regimes. We also identify
potential methods and research opportunities to help navigate the
tradeoffs inherent in uncertainty analyses for complex systems. During
this discussion, we provide a classification of uncertainty types and
discuss model coupling frameworks to support interdisciplinary
collaboration on multi-sector dynamics (MSD) research. Finally, we
conclude with recommendations for best practices to ensure that MSD
research can be properly contextualized with respect to the underlying
uncertainties.