Designing, Formulating, and Communicating Agent-Based Models

Grimm, Volker; Railsback, Steven F.

doi:10.1007/978-90-481-8927-4_17

Cited by 30 publications

(14 citation statements)

References 28 publications

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“…Machine learning and the development of surrogate meta-models can help to efficiently explore parameter space and effectively improve calibration exercises (Lee et al, 2015, Pereda et al, 2017. In addition, pattern-oriented modelling is an approach to avoid making an ABM become over-parameterized and lose predictive power (Grimm et al 2005, Grimm andRailsback, 2012). Moreover models should be as transparent as possible (e.g.…”

Section: Challenges and Prospects Of Agricultural Abmmentioning

confidence: 99%

Representation of decision-making in European agricultural agent-based models

Huber

Bakker

Balmann

et al. 2018

Agricultural Systems

115

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 Agent-based modelling is a suitable tool for improving the understanding of farmers' behaviour.  Review 20 agricultural ABM addressing heterogeneous decision-making processes in the context of European agriculture.  Considerable scope to improve diversity in representation of decision-making by combining existing modelling approaches.  More coordinated and purposeful combinations of ABM and hybrid modelling approaches are needed.  Results provide an entry point for collaboration of agent-based modellers, agricultural systems modellers and social scientist.

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Section: Challenges and Prospects Of Agricultural Abmmentioning

confidence: 99%

Representation of decision-making in European agricultural agent-based models

Huber

Bakker

Balmann

et al. 2018

Agricultural Systems

115

View full text Add to dashboard Cite

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“…Liu and Ashton 1999;Grimm et al 2006). The overview, design concepts, and details (ODD) framework developed by Grimm et al (2006;Grimm et al 2010) and Grimm and Railsback (2012) is a framework for describing ABMs in a standardized format. The ODD framework has been successfully used to describe ABMs in ecology and other disciplines and has also been adopted by many models in the model library maintained by the Open ABM Consortium (Janssen et al 2008).…”

Section: Comparison Approachmentioning

confidence: 99%

Agent-Based Modeling in Coupled Human and Natural Systems (CHANS): Lessons from a Comparative Analysis

Zvoleff

Liu

et al. 2014

Annals of the Association of American Geographers

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“…A typical example of an explanatory model is the Fitzhugh-Nagumo model for spiking neurons (FitzHugh, 1955;Nagumo et al, 1962), which gives no predictions of the membrane potential of neural cells at all but simply illustrates how a spike in this potential develops. Many ABMs and the very similar individual-based models (IBMs) in ecology are aimed at explanation, trying to explain emergent system properties from micro-level processes (Macal and North, 2005;Grimm and Railsback, 2012).…”

Section: Modelling Aimsmentioning

confidence: 99%

Agent-based modelling of socio-ecological systems: Models, projects and ontologies

Gotts¹,

Voorn

Polhill

et al. 2019

Ecological Complexity

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Socio-Ecological Systems (SESs) are the systems in which our everyday lives are embedded, so understanding them is important. The complex properties of such systems make modelling an indispensable tool for their description and analysis. Human actors play a pivotal role in SESs, but their interactions with each other and their environment are often underrepresented in SES modelling. We argue that more attention should be given to social aspects in models of SESs, but this entails additional kinds of complexity. Modelling choices need to be as transparent as possible, and to be based on analysis of the purposes and limitations of modelling. We recommend thinking in terms of modelling projects rather than single models. Such a project may involve multiple models adopting di↵erent modelling methods. We argue that agent-based models (ABMs) are an essential tool in an SES modelling project, but their expressivity, which is their major advantage, also produces problems with model transparency and validation. We propose the use of formal ontologies to make the structure and meaning of models as explicit as possible, facilitating model design, implementation, assessment, comparison and extension.

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Designing, Formulating, and Communicating Agent-Based Models

Cited by 30 publications

References 28 publications

Representation of decision-making in European agricultural agent-based models

Representation of decision-making in European agricultural agent-based models

Agent-Based Modeling in Coupled Human and Natural Systems (CHANS): Lessons from a Comparative Analysis

Agent-based modelling of socio-ecological systems: Models, projects and ontologies

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