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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PSMED - Patagonia Simple Model of Ethnic Differentiation
Joan A Barceló J A Cuesta Florencia Del Castillo Ricardo Del Olmo José M Galán Laura Mameli Francisco J Miguel David Poza José I Santos Xavier Vilà | Published Tuesday, December 10, 2013Patagonia PSMED is an agent-based model designed to study a simple case of Evolution of Ethnic Differentiation. It replicates how can hunter-gatherer societies evolve and built cultural identities as a consequence of the way they interacted.
Universal Darwinism in Dutch Greenhouses
Julia Kasmire | Published Wednesday, May 09, 2012 | Last modified Saturday, April 27, 2013An ABM, derived from a case study and a series of surveys with greenhouse growers in the Westland, Netherlands. Experiments using this model showshow that the greenhouse horticulture industry displays diversity, adaptive complexity and an uneven distribution, which all suggest that the industry is an evolving system.
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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