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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NetLogo software for the Peer Review Game model. It represents a population of scientists endowed with a proportion of a fixed pool of resources. At each step scientists decide how to allocate their resources between submitting manuscripts and reviewing others’ submissions. Quality of submissions and reviews depend on the amount of allocated resources and biased perception of submissions’ quality. Scientists can behave according to different allocation strategies by simply reacting to the outcome of their previous submission process or comparing their outcome with published papers’ quality. Overall bias of selected submissions and quality of published papers are computed at each step.
This agent-based model simulates a stoplight parrotfish population in a heavily-fished Caribbean coral reef. The model allows for the simulation of various fishing regulations and observation of population and catch outcomes. It was built using the structure and equations from several previously published models, including the work of Bozec et al. (2016) and Alonzo and Mangel (2004 and 2005). The initial model conditions are parameterized to population and fishing data collected in Buen Hombre, Dominican Republic by Tyler Pavlowich.
This model simulates the mechanisms of evolution, or how allele frequencies change in a population over time.
The (cultural) evolution of cooperative breeding in harsh environments.
Local scale mobility, namely foraging, leads to global population dispersal. Agents acquire information about their environment in two ways, one individual and one social. See also http://www.openabm.org/model/3846/
This agent-based model explores the existence of positive feedback loops related to illegal, unregulated, unreported (IUU) fishing; the use of forced labor; and the depletion of fish populations due to commercial fishing.
The purpose of this model is to provide a platform to test and compare four conceptual models have been proposed to explain the spread of the Impresso-Cardial Neolithic in the west Mediterranean.
The model employs an agent-based model for exploring the victim-centered approach to identifying human trafficking and the approach’s effectiveness in an abstract representation of migrant flows.
This generic model simulates climate change adaptation in the form of resistance, accommodation, and retreat in coastal regions vulnerable to sea level rise and flooding. It tracks how population changes as households retreat to higher ground.
Our societal belief systems are pruned by evolution, informing our unsustainable economies. This is one of a series of models exploring the dynamics of sustainable economics – PSoup, ModEco, EiLab, OamLab, MppLab, TpLab, CmLab.
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