About the CoMSES Model Library more info
Our mission is to help computational modelers develop, document, and share their computational models in accordance with community standards and good open science and software engineering practices. Model authors can publish their model source code in the Computational Model Library with narrative documentation as well as metadata that supports open science and emerging norms that facilitate software citation, computational reproducibility / frictionless reuse, and interoperability. Model authors can also request private peer review of their computational models. Models that pass peer review receive a DOI once published.
All users of models published in the library must cite model authors when they use and benefit from their code.
Please check out our model publishing tutorial and feel free to contact us if you have any questions or concerns about publishing your model(s) in the Computational Model Library.
We also maintain a curated database of over 7500 publications of agent-based and individual based models with detailed metadata on availability of code and bibliometric information on the landscape of ABM/IBM publications that we welcome you to explore.
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Model to assess factors that influence local communities compliance with protected areas policies
Gustavo Andrade | Published Monday, November 21, 2011 | Last modified Saturday, April 27, 2013We built a model using R,polr package, to assess 55 published case studies from developing countries to determine what factors influence the level of compliance of local communities with protected area regulations.
Firm explore-exploit of knowledge
Rosanna Garcia | Published Monday, March 28, 2011 | Last modified Saturday, April 27, 2013The basic premise of the model is to simulate several ‘agents’ going through build-buy cycles: Build: Factories follow simple rules of strategy in the allocation of resources between making exploration and exploitation type products. Buy: Each of two types of Consumers, early-adopters and late adopters, follow simple purchase decision rules in deciding to purchase a product from one of two randomly chosen factories. Thus, the two working ‘agents’ of the model are ‘factories’ and […]
Diet breadth model from Optimal Foraging Theory (Human Behavioral Ecology)
C Michael Barton | Published Wednesday, November 26, 2008 | Last modified Thursday, March 12, 2015Diet breadth is a classic optimal foraging theory (OFT) model from human behavioral ecology (HBE). Different resources, ranked according to their food value and processing costs, are distributed in th
Patch choice model from Optimal Foraging Theory (Human Behavioral Ecology)
C Michael Barton | Published Saturday, November 22, 2008 | Last modified Saturday, April 27, 2013NetLogo model of patch choice model from optimal foraging theory (human behavioral ecology).
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