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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This model incorporates three mechanisms shaping the dynamics of opinion formation, which mimics the dynamics of the virus spreading in the population. There are three methods of getting infected (or convinced) - direct contact, indirect contact, and contact with ``contaminated’’ elements.
This model represents the flight paths of a flock of homing pigeons according to their flocking-, orientation- and leadership behaviour.
In this model, we simulate the navigation behavior of homing pigeons. Specifically we use genetic algorithms to optimize the navigation and flocking parameters of pigeon agents.
This is a short NetLogo example demonstrating how to initialize 500 agents with 4 correlated parameters each with random values by doing the necessary calculations in the program “R” and retrieving the results.
This Python module contain a function that is able to test the ergodicity of a given agent based model. It is sufficient to produce one long time series and many smaller time series. The function uses
This model was designed to study resilience in organizations. Inspired by ethnographic work, it follows the simple goal to understand whether team structure affects the way in which tasks are performed. In so doing, it compares the ‘hybrid’ data-inspired structure with three more traditional structures (i.e. hierarchy, flexible/relaxed hierarchy, and anarchy/disorganization).
This is a simulation model of an intelligent agent that has the objective to learn sustainable management of a renewable resource, such as a fish stock.
CRESY-I stands for CREativity from a SYstems perspetive, Model I. This is the base model in a series designed to describe a systems approach to creativity in terms of variation, selection and retention (VSR) subprocesses.
An agent-based model of the Free/Libre Open Source Software (FLOSS) development process designed around agents selecting FLOSS projects to contribute to and/or download.
We provide a full description of the model following the ODD protocol (Grimm et al. 2010) in the attached document. The model is developed in NetLogo 5.0 (Wilenski 1999).
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