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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REHAB: A Role Playing Game to Explore the Influence of Knowledge and Communication on Natural Resources Management
Christophe Le Page Anne Dray Pascal Perez Claude Garcia | Published Monday, July 13, 2015 | Last modified Monday, July 13, 2015REHAB has been designed as an ice-breaker in courses dealing with ecosystem management and participatory modelling. It helps introducing the two main tools used by the Companion Modelling approach, namely role-playing games and agent-based models.
ForagerNet3_Demography_V2
Andrew White | Published Thursday, February 13, 2014ForagerNet3_Demography_V2 is a non-spatial ABM for exploring hunter-gatherer demography. This version (developed from FN3D_V1) contains code for calculating the ratio of old to young adults (the “OY ratio”) in the living and dead populations.
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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