Uncertainty Analysis in Multi‐Sector Systems: Considerations for Risk Analysis, Projection, and Planning for Complex Systems
Name
Earth s Future - 2022 - Srikrishnan - Uncertainty Analysis in Multi‐Sector Systems Considerations for Risk Analysis .pdf
Description
Published version
Size
948.19 KB
Format
Adobe PDF
Checksum (MD5)
09baf69f8319ed6bdee8e0e8cc856f07
Author(s)
Srikrishnan, Vivek
Lafferty, David C
Wong, Tony E
Lamontagne, Jonathan R
Quinn, Julianne D
Sharma, Sanjib
Molla, Nusrat J
Herman, Jonathan D
Sriver, Ryan L
Morris, Jennifer F
Date Issued
August 13, 2022
Journal
Earth's Future
Publisher
American Geophysical Union
Citation
Srikrishnan, V., Lafferty, D. C., Wong, T. E., Lamontagne, J. R., Quinn, J. D., Sharma, S., et al. (2022). Uncertainty analysis in multi-sector systems: Considerations for risk analysis, projection, and planning for complex systems. Earth's Future, 10, e2021EF002644.
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Joint Program on the Science & Policy of Global Change
Terms of Use
Creative Commons Attribution-NonCommercial
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1029/2021ef002644
