Exploring Energy and Economic Futures Using Agent-Based Modeling and Scenario Discovery

Wang, P.; Gerst, Michael D.; Borsuk, Mark E.

doi:10.1007/978-1-4614-8606-0_13

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…Other interactive forms are games or agent-based models. [64,119] Some of these studies involve stakeholders selectively at certain stages of the process. For instance, scholars may opt for involving stakeholders to estimate model parameter values [109] or to evaluate outcomes by using surveys at the end of the process.…”

Section: Summary Of the Scenario-based Literaturementioning

confidence: 99%

Assembling the climate story: use of storyline approaches in climate‐related science

et al. 2023

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Storylines are introduced in climate science to provide unity of discourse, integrate the physical and socioeconomic components of phenomena, and make climate evolution more tangible. The use of this concept by multiple scholar communities and the novelty of some of its applications renders the concept ambiguous nonetheless, because the term hides behind a wide range of purposes, understandings, and methodologies. This semi-systematic literature review identifies three approaches that use storylines as a keystone concept: scenarios-familiar for their use in IPCC reports-discourseanalytical approaches, and physical climate storylines. After screening peer-reviewed articles that mention climate and storylines, 270 articles are selected, with 158, 55, and 57 in each category. The results indicate that each scholarly community works with a finite and different set of methods and diverging understandings. Moreover, these approaches have received criticism in their assembly of storylines: either for lacking explicitness or for the homogeneity of expertise involved. This article proposes that cross-pollination among the approaches can improve the usefulness and usability of climate-related storylines. Among good practices are the involvement of a broader range of scientific disciplines and expertise, use of mixed-methods, assessment of storylines against a wider set of quality criteria, and targeted stakeholder participation in key stages of the process.

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Section: Summary Of the Scenario-based Literaturementioning

confidence: 99%

Assembling the climate story: use of storyline approaches in climate‐related science

et al. 2023

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“…This requires a method to select a few key desirable or undesirable outcomes, ideally representative of a broader class of dynamics, from a large set of model runs. Scenario discovery (Bryant & Lempert, 2010) is one class of such methods, which has already seen wide adoption in MSD‐related work (e.g., Dolan et al., 2021; Guivarch et al., 2016; Halim et al., 2016; Jafino & Kwakkel, 2021; Lamontagne et al., 2018; Moallemi, et al., 2020; Quinn et al., 2018; Wang et al., 2013).…”

Section: Scenario Discovery and Characterizing Dynamicsmentioning

confidence: 99%

Uncertainty Analysis in Multi‐Sector Systems: Considerations for Risk Analysis, Projection, and Planning for Complex Systems

et al. 2022

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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.

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Modeling and Simulation in Service of Energy Policy: The Challenges

Qudrat‐Ullah

2015

SpringerBriefs in Complexity

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Exploring Energy and Economic Futures Using Agent-Based Modeling and Scenario Discovery

Cited by 4 publications

References 16 publications

Assembling the climate story: use of storyline approaches in climate‐related science

Assembling the climate story: use of storyline approaches in climate‐related science

Uncertainty Analysis in Multi‐Sector Systems: Considerations for Risk Analysis, Projection, and Planning for Complex Systems

Modeling and Simulation in Service of Energy Policy: The Challenges

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