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.
Displaying 3 of 3 results fitness landscape clear
Peer reviewed Strategy with Externalities
J Applegate Glenn Hoetker | Published Thursday, December 21, 2017The SWE models firms search behaviour as the performance landscape shifts. The shift represents society’s pricing of negative externalities, and the performance landscape is an NK structure. The model is written in NetLogo.
Model to simulation the landscape of possible shedding games
Marco Janssen | Published Sunday, May 16, 2010 | Last modified Saturday, April 27, 2013This model simulates 2048 versions of shedding games and evaluates the consequences on the average length and the difficulty of the game agents experience. The purpose of the model is to understand th
Citation Agents: A model of collective learning through scientific publication
Christopher Watts | Published Friday, July 31, 2015Simulates the construction of scientific journal publications, including authors, references, contents and peer review. Also simulates collective learning on a fitness landscape. Described in: Watts, Christopher & Nigel Gilbert (forthcoming) “Does cumulative advantage affect collective learning in science? An agent-based simulation”, Scientometrics.