Combining system dynamics and agent-based modeling to analyze social-ecological interactions—an example from modeling restoration of a shallow lake

Martin, Romina; Schlüter, Maja

doi:10.3389/fenvs.2015.00066

Cited by 80 publications

(51 citation statements)

References 48 publications

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“…The temporal scales of the reviewed models range from small, e.g., three years in daily time steps in , to large, e.g., 10,000 years in yearly time steps in Janssen (2010). Numerous models applied different time steps for simulating different processes and, thereby, tackle the challenge of subsystems operating at different temporal scales as discussed by Martin and Schlüter (2015). For example, Smajgl and Bohensky (2013) use daily time steps for capturing environmental processes and weekly time steps for household-level processes such as migration.…”

Section: Overviewmentioning

confidence: 99%

Agent-based modeling of environment-migration linkages: a review

Thober¹,

Schwarz²,

Hermans³

2018

E&S

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Environmental change can lead to human migration and vice versa. Agent-based models (ABMs) are valuable tools to study these linkages because they can represent individual migration decisions of human actors. Indeed, there is an increasing, yet small, number of ABMs that consider the natural environment in rural migration processes. Therefore, we reviewed 15 ABMs of environment-migration linkages in rural contexts to synthesize the current state of the art. The reviewed ABMs are mostly applied in tropical contexts, serve a wide range of purposes, and cover diverse scales and types of environmental factors, migration processes, and social-ecological feedbacks. We identified potential for future model development with respect to the (1) complexity of environmental influence factors, (2) representation of relevant migration flows, and (3) type of social-ecological couplings. We found that existing models tend to not include fully integrated feedbacks and provide recommendations for the further development of ABMs to contribute to an understanding of the environment-migration-nexus in the future.Ecology and Society 23(2): 41 https://www.ecologyandsociety.org/vol23/iss2/art41/

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Section: Overviewmentioning

confidence: 99%

Agent-based modeling of environment-migration linkages: a review

Thober¹,

Schwarz²,

Hermans³

2018

E&S

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“…Two computational modeling approaches are popularly used for ecological modeling: agent-based models (ABM) and system dynamics models. Agent-based models are used to describe disaggregated parts of a system, system dynamics models represent the aggregated system (in the form of stocks and flows) (Martin and Schlüter 2015). The approach of agent-based modeling aims to study complex adaptive systems, and focuses on micro-level interactions to explain patterns, e.g.…”

Section: Ecosystem Modelingmentioning

confidence: 99%

“…Characteristics of agent-based and system dynamics models, and a hybrid model(Martin and Schlüter 2015) applications, multiple networks, it is necessary to investigate the smart grid architecture. There are several smart grid models have been proposed and discussed, e.g.…”

mentioning

confidence: 99%

Business ecosystem modeling- the hybrid of system modeling and ecological modeling: an application of the smart grid

2019

Energy Inform

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Business ecosystem is popularly used to investigate a complex social system with the business perspective, and particularly contributes to the understanding of actors and their relations in the innovation research. However, the aspect of business ecosystem modeling is limited discussed in the literature, although the importance has emerged significantly in recent years due to the emphasis on cross-disciplinary research and digitalization with artificial intelligence. Therefore, this paper proposes a framework for business ecosystem modeling with the discussion of system engineering and ecological modeling. The domain of smart grid is selected to demonstrate how system engineering, especially standards and ontologies contribute to the business ecosystem modeling. The proposed framework of the business ecosystem modeling includes three parts and nine stages that combines theories from system engineering, ecology, and business ecosystem. Part I-Business ecosystem architecture development includes four stages which aims to identify a target business ecosystem and its elements (actors, roles, and interactions). Part II-Factor analysis includes two stages to identify potential changes (and the dimensions of the changes) in the ecosystem. Part III-Ecosystem simulation and reconfiguration aims to use simulations to investigate the transition of an ecosystem and the re-configurated ecosystem. The framework not only provides a systematic approach for modeling a business ecosystem but also provides a methodological foundation for research on the aspect of complex systems in the business ecosystem field.

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“…Increasing the understanding on individual level processes and behavior, and how they link to population and community dynamics [70,131,132] may, however, enable a new generation of modeling tools to study the emergence of cross-scale dynamics. Such models may take into account that underlying mechanisms may occur at different scales than observed patterns, such as regime shifts and collapses [133].…”

Section: Adaptive Management Across Scales To Ensure Blue Growthmentioning

confidence: 99%

Global connectivity and cross-scale interactions create uncertainty for Blue Growth of Arctic fisheries

et al. 2018

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A B S T R A C TThe Arctic faces high expectations of Blue Growth due to future projections of easier access and increased biological productivity. These expectations are, however, often based on global and regional climate change projections and largely ignore the complexity of social-ecological interactions taking place across different temporal and spatial scales. This paper illustrates how such cross-scale interactions at, and across, different dimensions (e.g., ecological, socioeconomic and governance) can affect the development of Arctic fisheries; and potentially create uncertainties for future Blue Growth projections. Two Arctic marine systems, The Barents Sea and the Central Arctic Ocean (CAO), are used as focus areas. The former hosts productive fisheries and is mostly covered by the EEZs of Norway and Russia, while the latter is still mainly covered by sea-ice and is a high seas area with no multilevel governance system in place. The examples show that, both systems are affected by a number of processes, beyond the environmental change, spanning a wide range of dimensions, as well as spatial and temporal scales. To address the complexity of the Arctic marine systems calls for an increase in holistic scientific understanding together with adaptive management practices. This is particularly important in the CAO, where no robust regional management structures are in place. Recognizing how cross-scale dynamics can cause uncertainties to the current fisheries projections and implementing well-functioning adaptive management structures across different Arctic sub-systems can play a key role in whether the Blue Growth potential in Arctic fisheries is realized or lost.

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Combining system dynamics and agent-based modeling to analyze social-ecological interactions—an example from modeling restoration of a shallow lake

Abstract: Combining system dynamics and agent-based modeling to analyze social-ecological interactions-an example from modeling restoration of a shallow lake.

Cited by 80 publications

References 48 publications

Agent-based modeling of environment-migration linkages: a review

Agent-based modeling of environment-migration linkages: a review

Business ecosystem modeling- the hybrid of system modeling and ecological modeling: an application of the smart grid

Global connectivity and cross-scale interactions create uncertainty for Blue Growth of Arctic fisheries

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