Dynamical systems modeling for structural understanding of social-ecological systems: A primer

Radosavljevic, Sonja; Banitz, Thomas; Grimm, Volker; Johansson, Lars-Göran; Lindkvist, Emilie; Schlüter, Maja; Ylikoski, Petri

doi:10.1016/j.ecocom.2023.101052

Cited by 8 publications

(2 citation statements)

References 58 publications

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“…The purpose of dynamical systems analysis is to increase understanding of how the structure of a dynamical system brings about its dynamics (cf. Radosavljevic et al 2023). A dynamical systems model relates variables (relata) through rules of interaction in ways that describe the evolution of the system, for instance the co-evolution of a predator and prey population.…”

Section: Dynamical Systems Analysismentioning

confidence: 99%

“…Indeed, we regard causal reasoningand the claims and insights it allows-as more than just a methodological question. Causal reasoning involves many assumptions and choices that influence its direction, such as assessing and specifying the relevant causal aspects of a phenomenon, the causal configuration in which the phenomenon is embedded, or potential biases and alternative explanations (Schlüter et al 2023, unpublished manuscript, https://doi. org/10.31235/osf.io/kn49v).…”

Section: Introductionmentioning

confidence: 99%

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Eliciting the plurality of causal reasoning in social-ecological systems research

Hertz,

Banitz,

Martínez-Peña

et al. 2024

E&S

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Understanding causation in social-ecological systems (SES) is indispensable for promoting sustainable outcomes. However, the study of such causal relations is challenging because they are often complex and intertwined, and their analysis involves diverse disciplines. Although there is agreement that no single research approach (RA) can comprehensively explain SES phenomena, there is a lack of ability to deal with this diversity. Underlying this diversity and the challenge of dealing with it are different causal reasonings that are rarely explicit. Awareness of hidden assumptions is essential for understanding how the causal reasoning of an RA is constituted, and for promoting the integration, translation, or juxtaposition of different RAs. We identify the following elements as particularly relevant for understanding causal reasoning: methods, frameworks and theories, accounts of causation, analytical focus, and causal notions. We begin with the idea that one of these elements typically figures as an entry point to an RA. This entry point is particularly important because it generates a path dependence that orients causal reasoning. In a subsequent step, when an approach is applied, causal reasoning concretizes as a result of a particular constellation of the remaining elements. We come to these insights by studying the application of four different RAs to the same social-ecological case (the collapse of Baltic cod stocks in the 1980s). On the basis of our findings we developed a guide for the analysis of causal reasoning by raising awareness of the assumptions, key elements, and the relations between these key elements for a given RA. The guide can be used to elicit the causal reasoning of RAs, facilitate interdisciplinary collaboration, and support disclosure of ethical/political dimensions that underlie management/governance interventions that are formulated on the basis of causal findings of research studies.

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Section: Dynamical Systems Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Eliciting the plurality of causal reasoning in social-ecological systems research

Hertz,

Banitz,

Martínez-Peña

et al. 2024

E&S

View full text Add to dashboard Cite

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Exploratory modeling of social‐ecological systems

Eppinga,

Reader,

Santos

2024

Ecosphere

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Navigating social‐ecological systems toward sustainable trajectories is an important challenge of the Anthropocene. Models of social‐ecological systems can increase our understanding of how social and ecological subsystems interact, their response to environmental changes, and how their dynamics may be altered by management interventions. However, the level of representational detail required for models to describe a particular social‐ecological system with high fidelity (i.e., accurately quantifying system dynamics) may hamper both the interpretability of model results and our ability to identify key processes and feedbacks within the system. In contrast, stylized models describe simplified interactions between a small subset of social‐ecological system elements. Stylized models are a useful tool to identify potential consequences of specific key processes and feedbacks on system functioning. However, the relatively low level of representational detail in these models limits their ability to deliver concrete management options for a particular social‐ecological system. Here, we describe how an exploratory modeling approach can utilize the strengths of stylized models before the construction of social‐ecological system models with high fidelity and representational detail. This exploratory modeling approach is an iterative strategy, with the initial steps comprising the development of stylized models informed by empirical observations. We illustrate this with two examples of stylized modeling of isolated and connected social‐ecological systems. Through repeated confrontation of alternative models with empirical data, exploratory modeling provides useful stepping stones toward the development of models that describe social‐ecological systems in increasingly specific settings with increasing levels of representational detail. When these latter types of models reach a high level of fidelity, they could be used for scenario‐based analyses and participatory decision‐making processes. At this stage, the conceptual insights previously obtained during the exploratory modeling phase may aid in the interpretation and communication of the outcomes of scenario‐based analyses. Hence, exploratory modeling aims to create a synergy between the insights obtained from stylized models and system‐specific, high‐fidelity models in order to generate a deep understanding of the drivers of social‐ecological system dynamics, and how to leverage these drivers to initiate desired changes.

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Causal Explanations

Johansson,

Banitz,

Grimm

et al. 2024

SpringerBriefs in Philosophy

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There are several forms of explanation, one of which being causal explanations. Causal explanations are often descriptions of mechanisms, i.e., descriptions of how a state change in one object, labelled ‘the cause’, is transmitted through a number of intermediate objects to the final effect, i.e., a state change in another object. So the fundamental structure of mechanistic explanations is that of chained cause-effect relations.The main points of this chapter are: Causal explanation is one kind of explanation beside several other kinds. A causal explanation often consists of describing the mechanism by which the cause produces its effect. Reasons for human actions are often viewed as causes of those actions, but that is controversial. Three types of causal explanations in terms of mechanisms are confounder mechanisms, feedbacks, and bifurcations.

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Dynamical systems modeling for structural understanding of social-ecological systems: A primer

Cited by 8 publications

References 58 publications

Eliciting the plurality of causal reasoning in social-ecological systems research

Eliciting the plurality of causal reasoning in social-ecological systems research

Exploratory modeling of social‐ecological systems

Causal Explanations

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