Complex adaptive systems modeling with Repast Simphony
By Michael J. North, Nicholson T. Collier, Jonathan Ozik, Eric R. Tatara, Charles M. Macal, Mark J. Bragen, P. Sydelko. Complex Adaptive Systems Modeling, 2013
594 citations recorded by the source corpus.
PurposeThis paper is to describe development of the features and functions of Repast Simphony, the widely used, free, and open source agent-based modeling environment that builds on the Repast 3 library. Repast Simphony was designed from the ground up with a focus on well-factored abstractions. The resulting code has a modular architecture that allows individual components such as networks, logging, and time scheduling to be replaced as needed. The Repast family of agent-based modeling software has collectively been under continuous development for more than 10 years.MethodIncludes reviewing other free and open-source modeling libraries and environments as well as describing the architecture of Repast Simphony. The architectural description includes a discussion of the Simphony application framework, the core module, ReLogo, data collection, the geographical information system, visualization, freeze drying, and third party application integration.ResultsInclude a review of several Repast Simphony applications and brief tutorial on how to use Repast Simphony to model a simple complex adaptive system.ConclusionsWe discuss opportunities for future work, including plans to provide support for increasingly large-scale modeling efforts.
Subfields: Environmental Engineering, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/594ccc6549126284917321f531d1de986dbfe60d
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In Environmental Engineering
In Management Science and Operations Research
Citations in the corpus
- Synthetic Data for Deep Learning (2019, unknown): “While AirSim and Gazebo have been the most successful simulation frame- works, we also note other frameworks for multi-agent simulation that have been used for autonomous vehicles, usually without a realistic 3D rendered environment (see also a comparison of the frameworks in [420] and of their perc”
- 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, methodology): “The model is implemented in the Repast Simphony 2.3.1 platform using the Java programming language (North et al., 2013; see Supplemental Material A).”
- Everything you need to know about agent-based modelling and simulation (2016, background): “…on ABMS development have become established as open-source ABMS platforms, such as NetLogo (Wilensky and Rand, 2015; NetLogo, 2016), Repast (North et al , 2013; Repast, 2016), and MASON (Luke et al , 2005; MASON, 2016), and as commercial products such as AnyLogic (Borshchev, 2013;…”