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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The basic idea behind developing MIXTRUST was to represent a network of agricultural stakeholders composed of farmers and a cooperative in a mixed landscape to test its performances in response to risks. A mixed landscape here is a landscape where crop and livestock systems interact by the intermediary of material flows of agricultural products. It can be within mixed farms, or between farms, often specialized, (e.g. straw-manure).
This model slowly evolves to become Westeros, with houses fighting for the thrones, and whitewalkers trying to kill all living things. You can download each version to see the evolution of the code, from the Wolf Sheep Predation model to the Game of Thrones model. If you are only interested in the end product, simply download the latest version.
For instructions on each step, see: https://claudinegravelmigu.wixsite.com/got-abm
This model simulates the motion picture industry and tests how social influences affect market shares. It is empirically validated at the micro level by a cross-cultural survey.
CHALMS simulates housing and land market interactions between housing consumers, developers, and farmers in a growing ex-urban area.
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 model represents the flight paths of a flock of homing pigeons according to their flocking-, orientation- and leadership behaviour.
A dynamic model of social network formation on single-layer and multiplex networks with structural incentives that vary over time.
The Mobility Model is a model of a small-scale fishery with the purpose to study the movement of fishers between different sub-regions within a larger region, as they move between different regions to fish.
The purpose of the model is to study the impact of global food trade on food and nutrition security in countries around the world. It will incorporate three main aspects of trade between countries, including a country’s wealth, geographic location, and its trade relationships with other countries (past and ongoing), and can be used to study food and nutrition security across countries in various scenarios, such as climate change, sustainable intensification, waste reduction and dietary change.
This is a basic Susceptible, Infected, Recovered (SIR) model. This model explores the spread of disease in a space. In particular, it explores how changing assumptions about the number of susceptible people, starting number of infected people, as well as the disease’s infection probability, and average duration of infection. The model shows that the interactions of agents can drastically affect the results of the model.
We used it in our course on COVID-19: https://www.csats.psu.edu/science-of-covid19
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