Our mission is to help computational modelers develop, document, and share their computational models in accordance with community standards and good open science and software engineering practices. Model authors can publish their model source code in the Computational Model Library with narrative documentation as well as metadata that supports open science and emerging norms that facilitate software citation, computational reproducibility / frictionless reuse, and interoperability. Model authors can also request private peer review of their computational models. Models that pass peer review receive a DOI once published.
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 feel free to 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 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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A system that receives from an agent-based social simulation the agent’s emotional data, their emotional-related data such as motivations and beliefs, as well as their location, and visualizes of all this information in a two dimensional map of the geographic region the agents inhabit as well as on graphs along the time dimension.
This models simulates innovation diffusion curves and it tests the effects of the degree and the direction of social influences. This model replicates, extends and departs from classical percolation models.
How do households alter their spending patterns when they experience changes in income? This model answers this question using a random assignment scheme where spending patterns are copied from a household in the new income bracket.
A System Dynamics Model to anticipate insurgent movements and policy design to handle them .
This model is intended to explore the effectiveness of different courses of interventions on an abstract population of infections. Illustrative findings highlight the importance of the mechanisms for variability and mutation on the effectiveness of different interventions.
SONG is a simulator designed for simulating the process of transportation network growth.
The agent based model matches origins and destinations using employment search methods at the individual level.
The core algorithm is an agent-based model, which simulates travel patterns on a network based on microscopic decision-making by each traveler.
An agent-based model of species interaction on fragmented landscape is developed to address the question, how do population levels of predators and prey react with respect to changes in the patch connectivity as well as changes in the sharpness of threshold dispersal?
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