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: (a) inference and model calibration; (b) projecting future outcomes; and (c) 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.

Proposing DAPP-MR as a disaster risk management pathways framework for complex, dynamic multi-risk

“…Nevertheless, while DAPP-MR (with the right tool set) could provide support to find solutions for “difficult-to-answer” questions, complex multi-risk systems also face the challenge that they are wicked ( Rittel and Webber, 1973 ) meaning “difficult to define problems.” In our highly interconnected and interdependent society, the problem definition (i.e., what types of compounding/cascading hazards could interact in combination with growing multi-sectoral demands, or which elements do we include as exogenous forcing or within endogenous dynamics in our system definition) already introduces significant uncertainty and ambiguity ( Ringsmuth et al., 2022 ; Srikrishnan et al., 2022 ). A challenge remains whether informative pathways of actions for navigating the problem can be developed in such difficult and highly uncertain systems.…”

MimiBRICK.jl: A Julia package for the BRICK model for sea-level change in the Mimi integrated modeling framework

“…MimiBRICK.jl is flexible, efficient, and modular, to facilitate incorporating BRICK into coupled models and integrated assessments of climate impacts in a modular fashion to provide global average as well as local sea-level projections (National Academies of Sciences & Medicine, 2017). This focus on tight model coupling (Srikrishnan et al, 2022) and integrated modeling is a distinction between FACTS and MimiBRICK.jl and the broader Mimi modeling framework. This implementation includes examples for using observational data to calibrate the model, as well as various configurations in which MimiBRICK is coupled to other climate model components.…”

“…Wong et al (2022). MimiBRICK.jl: A Julia package for the BRICK model for sea-level change in the Mimi integrated modeling framework.…”

Quantifying the Safe Operating Space for Land‐System SDG Achievement via Machine Learning and Scenario Discovery

“…Multiple land‐use models have been developed both as stand‐alone models and as a part of multi‐sectoral integrated assessment models to understand the complex relationships and dynamics underpinning land‐system sustainability (Bryan, Nolan, et al., 2016; Hurtt et al., 2011; Liu et al., 2021; Meiyappan et al., 2014; Schaldach et al., 2011; Souty et al., 2012; Van Asselen & Verburg, 2013; Wise et al., 2014; Zilli et al., 2020). However, land‐use futures are characterized by substantial complexity and uncertainty driven by the large potential range in input data, model parameter specifications, and the scenario assumptions involved in land‐use modeling, and the sporadic and incomplete consideration of these interacting uncertainties (Moallemi, Kwakkel, et al., 2020; Srikrishnan et al., 2022). There is a need for the comprehensive exploration of this uncertainty space to better account for the diversity of plausible futures and identify pathways to achieving the SDGs (Moallemi, Eker, et al., 2022).…”