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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This model simulates different spread hypotheses proposed for the introduction of agriculture on the Iberian peninsula. We include three dispersal types: neighborhood, leapfrog, and ideal despotic distribution (IDD).
One of four extensions to the standard Adder model that replicates the various interventions typically associated with transition experiments.
This is one of four extensions to the standard Adder model that replicate the various interventions typical of transition experiments.
One of four extensions to the standard Adder model that replicates a common type of transition experiment.
The fourth and final extension to the standard Adder model to replicate the various interventions typically associated with Transition Experiments.
A simplified Arthur & Polak logic circuit model of combinatory technology build-out via incremental development. Only some inventions trigger radical effects, suggesting they depend on whole interdependent systems rather than specific innovations.
Continuing on from the Adder model, this adaptation explores how rationality, learning and uncertainty influence the exploration of complex landscapes representing technological evolution.
This is the same model as used in the article ‘Modelling Society’s Evolutionary Forces’ except the Fertility graph has been corrected. The Fertility graph was not used in the published article.
CoDMER v. 2.0 was parameterized with ethnographic data from organizations dealing with prescribed fire and seeding native plants, to advance theory on how collective decisions emerge in ecological restoration.
The MOBILITY model analyzes how agents’ mobility affects the performance of social-ecological systems in different landscape configurations.
Displaying 10 of 1209 results