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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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.
This theoretical model includes forested polygons and three types of agents: forest landowners, foresters, and peer leaders. Agent rules and characteristics were parameterized from existing literature and an empirical survey of forest landowners.
The model simulates the process of widespread diffusion of something due to popularity (i.e., bandwagon) within an organization.
Three policy scenarios for urban expansion under the influences of the behaviours and decision modes of four agents and their interactions have been applied to predict the future development patterns of the Guangzhou metropolitan region.
What is stable: the large but coordinated change during a diffusion or the small but constant and uncoordinated changes during a dynamic equilibrium? This agent-based model of a diffusion creates output that reveal insights for system stability.
This model simulates diffusion curves and it allows to test how social influence, network structure and consumer heterogeneity affect their spreads and their speeds.
This is a complex “Data Integration Model”, following a “KIDS” rather than a “KISS” methodology - guided by the available evidence. It looks at the complex mix of social processes that may determine why people vote or not.
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.
This model allows for analyzing the most efficient levers for enhancing the use of recycled construction materials, and the role of empirically based decision parameters.
This models provides the infrastructure to model the activity of making. Individuals use resources they find in their environment plus those they buy, to design, construct and deconstruct items. It represents plans and complex objects explicitly.
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