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Uncertainty analysis in multi-sector systems: Considerations for risk analysis, projection, and planning for complex systems
  • +8
  • Vivek Srikrishnan,
  • David C Lafferty,
  • Tony E Wong,
  • Jonathan R. Lamontagne,
  • Julianne D Quinn,
  • Sanjib Sharma,
  • Nusrat J Molla,
  • Jonathan D Herman,
  • Ryan L. Sriver,
  • Jennifer Morris,
  • Ben Seiyon Lee
Vivek Srikrishnan
Cornell University, Cornell University

Corresponding Author:[email protected]

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David C Lafferty
University of Illinois at Urbana Champaign, University of Illinois at Urbana Champaign
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Tony E Wong
Rochester Institute of Technology, Rochester Institute of Technology
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Jonathan R. Lamontagne
Tufts University, Tufts University
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Julianne D Quinn
University of Virginia, University of Virginia
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Sanjib Sharma
The Pennsylvania State University, The Pennsylvania State University
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Nusrat J Molla
University of California, Davis, University of California, Davis
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Jonathan D Herman
University of California, Davis, University of California, Davis
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Ryan L. Sriver
University of Illinois at Urbana Champaign, University of Illinois at Urbana Champaign
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Jennifer Morris
Massachusetts Institute of Technology, Massachusetts Institute of Technology
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Ben Seiyon Lee
The George Mason University, The George Mason University
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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.