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Generalized modeling of empirical social-ecological systems
Stockholm University, Faculty of Science, Stockholm Resilience Centre. The Australian National University, Australia.ORCID iD: 0000-0001-9719-9826
Stockholm University, Faculty of Science, Stockholm Resilience Centre.ORCID iD: 0000-0001-8405-8717
Number of Authors: 22017 (English)In: Natural Resource Modeling, ISSN 0890-8575, E-ISSN 1939-7445, Vol. 30, no 3, article id e12129Article in journal (Refereed) Published
Abstract [en]

Modeling social-ecological systems is difficult due to the complexity of ecosystems and of individual and collective human behavior. Key components of the social-ecological system are often over-simplified or omitted. Generalized modeling is a dynamical systems approach that can overcome some of these challenges. It can rigorously analyze qualitative system dynamics such as regime shifts despite incomplete knowledge of the model's constituent processes. Here, we review generalized modeling and use a recent study on the Baltic Sea cod fishery's boom and collapse to demonstrate its application to modeling the dynamics of empirical social-ecological systems. These empirical applications demand new methods of analysis suited to larger, more complicated generalized models. Generalized modeling is a promising tool for rapidly developing mathematically rigorous, process-based understanding of a social-ecological system's dynamics despite limited knowledge of the system. Recommendations for Resource Managers Understanding empirical social-ecological dynamics requires integrating quantitative and qualitative data Generalized modeling can analyze qualitative dynamics, such as regime shifts, by integrating both qualitative and quantitative data Generalized modeling is well-suited to use in participatory or collaborative settings

Place, publisher, year, edition, pages
2017. Vol. 30, no 3, article id e12129
Keywords [en]
dynamical systems, eigenvalues, generalized modeling, social-ecological systems
National Category
Earth and Related Environmental Sciences
Identifiers
URN: urn:nbn:se:su:diva-147134DOI: 10.1111/nrm.12129ISI: 000407149600001OAI: oai:DiVA.org:su-147134DiVA, id: diva2:1145283
Available from: 2017-09-28 Created: 2017-09-28 Last updated: 2025-02-07Bibliographically approved

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Lade, Steven J.Niiranen, Susa

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