Cognition of feedback loops in a fire-prone social-ecological system

Hamilton, Max; Salerno, Jonathan; Fischer, A. Paige

doi:10.1016/j.gloenvcha.2022.102519

Cited by 4 publications

(6 citation statements)

References 71 publications

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“…In general, the micro-motif count within the individual maps decreased in-line with Levy’s findings of micro-motif prevalence (e.g., a common micro-motif like multiple causes has a probability of 91.4% while more complex motifs like feedback loops has a 17.9% chance) (Levy et al, 2018). In contrast to Hamilton et al (2022)’s finding of more feedback loops in aggregated cognitive maps than individual maps, we found very few feedback loops in the aggregated maps. This difference is likely due to dissimilarities in aggregation methodology and the modeled system.…”

Section: Discussioncontrasting

confidence: 99%

“…Compared to random networks of the same size and density, complex networks like the food system are found to have a significantly higher occurrence of complex micro-motifs (Milo et al, 2002). Micro-motifs have been used in FCM studies to compare levels of systems thinking across individuals and within aggregated models (Aminpour et al, 2021a; Hamilton et al, 2022). Using the six substructures proposed by Levy et al (see Figure 13), we determined the number of each micro-motif within the individual maps, group maps, and metamodel.…”

Section: Methodsmentioning

confidence: 99%

“…Micro-motifs were detected and counted in the individual, group, and metamodels (see Appendix I for precise counts). In addition, the final number of motifs was compared to 1000 random sample connected networks of the same size and density to calculate the probability of the model having fewer motifs than was present (see Figure 15) (Hamilton et al, 2022). For example, if a metamodel had four instances of bidirectionality, the percentage of random networks with three or less of that particular micro-motif would represent the probability that the metamodel would have fewer network structures.…”

Section: Network Structure Measures and Micro-motifsmentioning

confidence: 99%

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Modeling complex problems by harnessing the collective intelligence of local experts: New approaches in fuzzy cognitive mapping

Knox,

Gray,

Zareei

et al. 2023

Collective Intelligence

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Developing system understanding and testing interventions are critical steps to addressing wicked problems. Fuzzy cognitive mapping (FCM) can be a useful participatory modeling tool that enables aggregation of individual perspectives to build system models that represent groups’ collective intelligence (CI). However, current FCM aggregation methodologies for creating CI models have rarely been tested and compared. We conducted 51 FCM interviews with local experts in the Flint, MI food system to map their mental models about how different food system sectors influenced desirable outcomes. Using four differing aggregation techniques, based on experts’ identity diversity and cognitive diversity, we generated four CI models. The models were compared based on their similarity to real-world complex systems using performance metrics like network structure, micro-motifs, cognitive distance, and scenario outcomes. We found that using cognitive diversity to group individuals was better suited for modeling systems with diverse holders of knowledge.

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Section: Discussioncontrasting

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Network Structure Measures and Micro-motifsmentioning

confidence: 99%

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Modeling complex problems by harnessing the collective intelligence of local experts: New approaches in fuzzy cognitive mapping

Knox,

Gray,

Zareei

et al. 2023

Collective Intelligence

View full text Add to dashboard Cite

show abstract

“…The capacity to anticipate potentially perverse consequences of new policies due to feedbacks between human and natural systems [128] prior to their implementation is an important value of SES simulation modeling. Yet, most people have trouble understanding feedback loops in the context of wildfire risk mitigation, particularly those that may dampen effective responses to risk or that lead to negative consequences [129].…”

Section: Disentangling Coupled Processes To Craft Local Solutionsmentioning

confidence: 99%

Exploring and Testing Wildfire Risk Decision-Making in the Face of Deep Uncertainty

Johnson

Ager

Evers

et al. 2023

Fire

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We integrated a mechanistic wildfire simulation system with an agent-based landscape change model to investigate the feedbacks among climate change, population growth, development, landowner decision-making, vegetative succession, and wildfire. Our goal was to develop an adaptable simulation platform for anticipating risk-mitigation tradeoffs in a fire-prone wildland–urban interface (WUI) facing conditions outside the bounds of experience. We describe how five social and ecological system (SES) submodels interact over time and space to generate highly variable alternative futures even within the same scenario as stochastic elements in simulated wildfire, succession, and landowner decisions create large sets of unique, path-dependent futures for analysis. We applied the modeling system to an 815 km2 study area in western Oregon at a sub-taxlot parcel grain and annual timestep, generating hundreds of alternative futures for 2007–2056 (50 years) to explore how WUI communities facing compound risks from increasing wildfire and expanding periurban development can situate and assess alternative risk management approaches in their localized SES context. The ability to link trends and uncertainties across many futures to processes and events that unfold in individual futures is central to the modeling system. By contrasting selected alternative futures, we illustrate how assessing simulated feedbacks between wildfire and other SES processes can identify tradeoffs and leverage points in fire-prone WUI landscapes. Assessments include a detailed “post-mortem” of a rare, extreme wildfire event, and uncovered, unexpected stabilizing feedbacks from treatment costs that reduced the effectiveness of agent responses to signs of increasing risk.

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“…The integration of human interaction with the environment is conceptualized in the literature on SES, which exist at different scales and forms sub-systems of larger SES (Reyers et al, 2018). SES assume that environmental, economic, and social (including cultural and political) sub-systems are interconnected (Folke, 2006), e.g., a disturbance in a sub-system may affect others, via self-amplifying or stabilizing feedback loops that operate within or across sub-systems (Senge, 1991;Sterman, 2002;Hamilton et al, 2022). These dynamics may not be linear and lead to rapid, hardly predictable changes.…”

Section: Introductionmentioning

confidence: 99%

Describing complex interactions of social-ecological systems for tipping point assessments: an analytical framework

Froese,

Andrino,

Giudice

et al. 2023

Front. Clim.

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Humans play an interconnecting role in social-ecological systems (SES), they are part of these systems and act as agents of their destruction and regulation. This study aims to provide an analytical framework, which combines the concept of SES with the concept of tipping dynamics. As a result, we propose an analytical framework describing relevant dynamics and feedbacks within SES based on two matrixes: the “tipping matrix” and the “cross-impact matrix.” We take the Southwestern Amazon as an example for tropical regions at large and apply the proposed analytical framework to identify key underlying sub-systems within the study region: the soil ecosystem, the household livelihood system, the regional social system, and the regional climate system, which are interconnected through a network of feedbacks. We consider these sub-systems as tipping elements (TE), which when put under stress, can cross a tipping point (TP), resulting in a qualitative and potentially irreversible change of the respective TE. By systematically assessing linkages and feedbacks within and between TEs, our proposed analytical framework can provide an entry point for empirically assessing tipping point dynamics such as “tipping cascades,” which means that the crossing of a TP in one TE may force the tipping of another TE. Policy implications: The proposed joint description of the structure and dynamics within and across SES in respect to characteristics of tipping point dynamics promotes a better understanding of human-nature interactions and critical linkages within regional SES that may be used for effectively informing and directing empirical tipping point assessments, monitoring or intervention purposes. Thereby, the framework can inform policy-making for enhancing the resilience of regional SES.

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Cognition of feedback loops in a fire-prone social-ecological system

Cited by 4 publications

References 71 publications

Modeling complex problems by harnessing the collective intelligence of local experts: New approaches in fuzzy cognitive mapping

Modeling complex problems by harnessing the collective intelligence of local experts: New approaches in fuzzy cognitive mapping

Exploring and Testing Wildfire Risk Decision-Making in the Face of Deep Uncertainty

Describing complex interactions of social-ecological systems for tipping point assessments: an analytical framework

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