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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An agent-based artificial stock with a dynamic investor network
Matthew Oldham | Published Tuesday, June 25, 2019The model is an agent-based artificial stock market where investors connect in a dynamic network. The network is dynamic in the sense that the investors, at specified intervals, decide whether to keep their current adviser (those investors they receive trading advise from). The investors also gain information from a private source and share public information about the risky asset. Investors have different tendencies to follow the different information sources, consider differing amounts of history, and have different thresholds for investing.
Health and social public information office (SPUN) simulation
Manuela Vinai Emilio Sulis | Published Friday, November 06, 2015 | Last modified Saturday, November 07, 2015The program simulate the functioning of an italian health and social public information office (SPUN) on the basis of the real data collected in the first five years of functioning.
Composite Collective Decision Making - ant colony foraging model
Tomer Czaczkes Benjamin I Czaczkes | Published Thursday, December 17, 2015The model explores how two types of information - social (in the form of pheromone trails) and private (in the form of route memories) affect ant colony level foraging in a variable enviroment.