Lindkvist uses agent-based simulation models to understand aspects of resilience and sustainability in social-ecological systems. She is interested in novel ways of combining qualitative data into these models, particularly in small-scale fisheries, to explore phenomena such as social differentiation, co-management, cooperation, mobility and migration, sequential resource exploitation, and overfishing.
Lindkvist is leading the research project “Navigating the complexity of small-scale fishery interventions: An intersection of agent-based modeling and participatory empirical research”. She is also participating in the research project “Inequality and the sustainable development goals: A multi-scale analysis of tradeoffs, synergies, and interactions” lead by Carl Folke and the Beijer Young Scholars, and the project “Approaches to causation in the social and natural sciences and their implications for theory building in sustainability science” lead by Maja Schlüter.
Lindkvist has a background in computer science from KTH Royal Institute of Technology and Uppsala University, as well as mathematics from Stockholm University, Sweden. She started working at the Systems Ecology Department at Stockholm University as a modeller together with Jon Norberg in 2004. She later pursued her PhD in sustainability Sciences at the Stockholm Resilience Centre with Jon Norberg and Maja Schlüter, titled “Learning-by-modeling–Novel Computational Approaches for Exploring the Dynamics of Learning and Self-governance in Social-ecological Systems”. In her following postdoc Lindkvist was funded by USA NSF coupled human nature systems grant in collaboration with researchers at Duke University Marine Lab, Scripps Institution of Oceanography at University of San Diego, and Darling Marine centre at Maine University.
Blanca González García-Mon, PhD candidate
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Time to go from simply describing social-ecological systems to explaining how their complex interactions generate observed outcomes
2020 - Journal / article
The sustainable governance and management of small-scale fisheries (SSF) is challenging, largely due to their dynamic and complex nature. Agent-based modeling (ABM) is a computational modeling approach that can account for the dynamism and complexity in SSF by modeling entities as individual agents with different characteristics and behavior, and simulate how their interactions can give rise to emergent phenomena, such as over...
2019 - Journal / article
Explanations that account for complex causation, emergence, and social-ecological interdependence are necessary for building theories of social-ecological phenomena. Social-ecological systems (SES) research has accumulated rich empirical understanding of SES; however, integration of this knowledge toward contextualized generalizations, or middle-range theories, remains challenging. We discuss the potential of an iterative and ...
2019 - Journal / article
Social-ecological systems (SES) are complex adaptive systems. Social-ecological system phenomena, such as regime shifts, transformations, or traps, emerge from interactions among and between human and nonhuman entities within and across scales. Analyses of SES phenomena thus require approaches that can account for (1) the intertwinedness of social and ecological processes and (2) the ways they jointly give rise to emergent so...
2018 - Journal / article
Rising inequalities and accelerating global environmental change pose two of the most pressing challenges of the twenty-first century. To explore how these phenomena are linked, we apply a social-ecological systems perspective and review the literature to identify six different types of interactions (or “pathways”) between inequality and the biosphere. We find that most of the research so far has only considered one-directiona...