About the CoMSES Model Library more info
Our mission is to help computational modelers at all levels engage in the establishment and adoption of community standards and good practices for developing and sharing computational models. Model authors can freely publish their model source code in the Computational Model Library alongside narrative documentation, open science metadata, and other emerging open science norms that facilitate software citation, reproducibility, interoperability, and reuse. Model authors can also request peer review of their computational models to receive a DOI.
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 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 additional 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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Peer reviewed Umwelten Ants
Kit Martin | Published Thursday, January 15, 2015 | Last modified Thursday, August 27, 2015Simulates impacts of ants killing colony mates when in conflict with another nest. The murder rate is adjustable, and the environmental change is variable. The colonies employ social learning so knowledge diffusion proceeds if interactions occur.
Value Chain Marketing (VCM)
Stephanie Hintze | Published Monday, April 14, 2014 | Last modified Thursday, October 16, 2014Inspired by the SKIN model, the basic concept here is to model the acceptance and implementation of supplier innovations. This model includes three types of agents comprising suppliers, manufacturers and applicators.
Seasonal Social Networks and Learning Opportunities Under Unbiased Cultural Transmission
Adam Rorabaugh | Published Monday, May 18, 2015This agent-based model examines the impact of seasonal aggregation, dispersion, and learning opportunities on the richness and evenness of artifact styles under random social learning (unbiased transmission).
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