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.
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The aim of the model is to define when researcher’s assumptions of dependence or independence of cases in multiple case study research affect the results — hence, the understanding of these cases.
This model explores a social mechanism that links the reversal of the gender gap in education with changing patterns in relative divorce risks in 12 European countries.
This ABM simulates opinions on a topic (originally contested infrastructures) through the interactions between paired agents and based on the sociopsychological assumptions of social judgment theory (SJT; Sherif & Hovland, 1961).
This model is a highly stylized land use model in the Clear Creek Watershed in Eastern Iowa, designed to illustrate the construction of stability landscapes within resilience theory.
A computational model of a classic small group study by Alex Bavelas. This computational model was designed to explore the difficulty in translating a seemingly simple real-world experiment into a computational model.
This agent-based model simulates the diffusion of a social change process stratified by social class in space and time which is solely driven social and spatial variation in communication links.
This is an adaptation and extension of Robert Axtell’s model (2013) of endogenous firms, in Python 3.4
The 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.
In this Repast model the ‘Consumat’ cognitive framework is applied to an ABM of the Dutch car market. Different policy scenarios can be selected or created to examine their effect on the diffusion of EVs.
The purpose of this model is to enhance a basic ABM through a simple set of rules identified using the activity-driven models in order to produce more realistic patterns of pedestrian movement.
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