Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons from Ecology

By Volker Grimm, E. Revilla, Uta Berger, Florian Jeltsch, Wolf M. Mooij, Steven F. Railsback, H. Thulke, J. Weiner, T. Wiegand, D. DeAngelis. Science, 2005

2,254 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Public Health, Environmental and Occupational Health, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/b138231e2ce9f510b7c5335a68b4eba3f9a31c69

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Citations in the corpus

NEW HORIZONS FOR MANAGING THE ENVIRONMENT: A REVIEW OF COUPLED SOCIAL‐ECOLOGICAL SYSTEMS MODELING (2012, background): “Recently, Grimm et al. [2006, 2010, see also Janssen et al. [2008]] have proposed the ODD protocol (Overview, Design concepts, and Details) for describing individual-based models in ecology. ODD provides a guide for presentation of the purpose of a model, its input and state variables, temporal and ”
Exploring, exploiting and evolving diversity of aquatic ecosystem models: a community perspective (2015, methodology): “ODD for IBMs, Grimm et al. (2006)], terminology to categorize AEMs and the methods to analyse AEMs.”
Predicting monarch butterfly (Danaus plexippus) movement and egg-laying with a spatially-explicit agent-based model: The role of monarch perceptual range and spatial memory (2018, background): “Agent-based modeling is well-suited to address the effects of different spatially-explicit landscape configurations on animal 4 population dynamics (DeAngelis and Mooij, 2005, Grimm et al., 2005, Railsback et al., 2006; Railsback and Grimm, 2011).”
End-To-End Models for the Analysis of Marine Ecosystems: Challenges, Issues, and Next Steps (2010, positive): “While such model behavior is often beguiling, we must be prepared to detect and diagnose it—via analysis of model output and simulation experiments (Grimm et al. 2005)—to ensure that the output is indeed realistic and to fully understand the reasons for our results.”
The ODD Protocol for Describing Agent-Based and Other Simulation Models: A Second Update to Improve Clarity, Replication, and Structural Realism (2020, background): “Once the patterns are identified, POM then consists of (a) selecting model entities, state variables, and scales so that the patterns could be reproduced; (b) testing “theory” for agent behaviours by showing whether alternative submodels for behaviour cause the patterns to emerge from the model; and”
Machine learning and deep learning—A review for ecologists (2022, neutral): “Even so, when considering the known complexity of the natural world (Grimm et al., 2005), statistical models tend to be rather simple and rigid, due to the mathematical difficulties involved in calculating likelihoods for more complex or flexible models, and it remains an important caveat of…”
Challenging Wallacean and Linnean shortfalls: knowledge gradients and conservation planning in a biodiversity hotspot (2006, background): “The model developed here can be viewed in the context of pattern-orientated modelling (see Grimm et al., 2005), since HUS are not ‘real’ in a systematic sense, although we expect that their geographical properties (i.e. their RSFD) will match real ones, allowing Wallacean and Linnean shortfalls to…”
Competition among plants: Concepts, individual-based modelling approaches, and a proposal for a future research strategy (2008, methodology): “Then, to identify the most appropriate set of submodels describing key processes, statistical methods can be used for comparing real and predicted patterns (Grimm et al., 2005; Grimm and Railsback, 2005).”
Describing human decisions in agent-based models - ODD + D, an extension of the ODD protocol (2013, background): “data (non)availability, reference to previous studies, theoretical reasons, or pattern-oriented modelling (Grimm et al., 2005).”
Agent-based Simulation Platforms: Review and Development Recommendations (2006, background): “, for parameterization or theory development; [3]).”